Ivermectin reduces COVID-19 risk: real-time meta-analysis of 106 studies (c19ivm ivmmeta)

@CovidAnalysis, Aug 31, 2026, Version 257V257 (V1 2020)
Why do many authorities claim no efficacy? They did not proactively review data, guaranteeing late recognition, by which time moral, legal, and reputational liability disincentivizes admission of error. Studies overwhelmingly show efficacy, including the largest RCT, and results across studies match the mechanisms, which is not possible without a real treatment effect.
TLDR: evidence for efficacy, PRINCIPLE, TOGETHER, ACTIV-6, Strongyloides, GidMK, Scott Alexander, Popp
Abstract
Significantly lower risk is seen for mortality, ventilation, ICU admission, hospitalization, recovery, cases, and viral clearance. All remain significant for higher quality studies. 66 studies from 60 independent teams in 28 different countries show significant improvements.
Meta-analysis using the most serious outcome shows 62% [52‑70%] and 84% [75‑89%] lower risk for early treatment and prophylaxis, with similar results for higher quality studies, primary outcomes, peer-reviewed studies, and for RCTs.
Results are very robust—in worst case exclusion sensitivity analysis 65 of 106 studies must be excluded before statistical significance is lost. Emergent results matching biological mechanisms confirm efficacy: meta-regression of efficacy vs. treatment delay (p = 0.0038), efficacy gradient across stages (p < 0.0000001), and fed vs. fasting administration (p = 0.03).
No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Pharmacokinetics show significant inter-individual variability1. Efficacy varies depending on the manufacturer2. Administration with a high-fat meal increases the bioavailability of ivermectin, and may aid distribution to target tissues.
Over 20 countries adopted ivermectin for COVID-19. The evidence base is much larger and has much lower conflict of interest than typically used to approve drugs.
All data and sources to reproduce this analysis are in the appendix. Multiple other meta-analyses show efficacy3-7.
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Evolution of COVID-19 clinical evidence Meta-analysis results over time Ivermectin p<0.0000000001 Acetaminophen p=0.00000021 2020 2021 2022 2023 2024 2025 2026 Lowerrisk Higherrisk c19early.org September 2026 50% 0% -50%
Ivermectin for COVID-19 — Highlights
Ivermectin reduces risk with very high confidence for mortality, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and in pooled analysis.
Emergent results matching biological mechanisms confirm efficacy: meta-regression of efficacy vs. treatment delay (p = 0.0038), efficacy gradient across stages (p < 0.0000001), and fed vs. fasting administration (p = 0.03).
4th treatment shown effective in August 2020, now with p < 0.0000000001 from 106 studies, recognized in 24 countries.
Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
c19early.org
October 2026

Ivermectin COVID-19 early treatment and prophylaxis studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96hosp.0/602/5614mgOT1 CT2 Espitia-Hernandez70%0.30 · 0.16–0.55recov. time28 (n)7 (n)12mgCT2 Carvallo85%0.15 · 0.02–1.28death1/323/1436mgCT2 Mahmud (DB RCT)86%0.14 · 0.01–2.75death0/1833/18312mgCT2 Szente Fonseca−14%1.14 · 0.75–1.66hosp.340 (n)377 (n)24mg Cadegiani78%0.22 · 0.01–4.48death0/1102/13742mgCT2 Ahmed (DB RCT)85%0.15 · 0.01–2.70symptoms0/173/1948mg Chaccour (DB RCT)96%0.04 · 0.00–1.01symptoms12 (n)12 (n)28mgSAINT Ghauri92%0.08 · 0.01–0.88no recov.0/377/5348mg Babalola (DB RCT)64%0.36 · 0.10–1.27viral+40 (n)20 (n)24mgOT1 Bukhari (RCT)82%0.18 · 0.07–0.46viral+4/4125/4512mg Mohan (DB RCT)62%0.38 · 0.08–1.75no recov.2/406/4528mgRIVET-COV Biber (DB RCT)70%0.30 · 0.03–2.76hosp.1/473/4236mg Elalfy87%0.13 · 0.06–0.27viral+7/6244/5136mgCT2 López-Me.. (DB RCT)67%0.33 · 0.01–8.11death0/2001/19884mg Chahla (CLUS. RCT)87%0.13 · 0.03–0.54no disch.2/11020/14424mg Mourya89%0.11 · 0.05–0.25viral+5/5047/5048mg Loue (QR)70%0.30 · 0.04–2.20death1/105/1514mg Merino (QR)74%0.26 · 0.11–0.57hosp.77,381 (all patients)24mgcensored, see notes CS5 Faisal (RCT)68%0.32 · 0.14–0.72no recov.6/5019/5048mg Aref (RCT)63%0.37 · 0.22–0.61recov. time57 (n)57 (n)n/a Krolewiecki (RCT)−152%2.52 · 0.11–58.11ventilation1/270/14168mg Vallejos (DB RCT)−33%1.33 · 0.30–5.72death4/2503/25124mg Reis (DB RCT)12%0.88 · 0.49–1.55death21/67924/67984mgTOGETHERimpossible data, see notes Buonfrate (DB RCT)−211%3.11 · 0.13–73.31hosp.1/280/31336mgCOVER Mayer55%0.45 · 0.32–0.63death3,266 (n)17,966 (n)151mg Borody92%0.08 · 0.01–0.79death0/6006/60096mgMD3 CT2 SC4 Abbas (DB RCT)−4%1.04 · 0.07–16.41death1/991/10384mg de Jesús Ascenci..59%0.41 · 0.36–0.47death/hosp.7,898 (n)20,150 (n)12mgCT2 Manomai.. (DB RCT)43%0.57 · 0.20–1.46no recov.3/366/3648mg de la Ro.. (DB RCT)−187%2.87 · 0.12–67.49misc.1/300/2636mg Rezai (DB RCT)−5%1.05 · 0.07–16.68death1/2681/28184mg Mirahma.. (DB RCT)67%0.33 · 0.03–3.14ventilation1/1313/13024mg Schilling (RCT)67%0.33 · 0.01–7.97hosp.0/451/45168mgPLATCOV Bramante (DB RCT)−197%2.97 · 0.12–72.71death1/4080/39690mgCOVID-OUTOT1 Mikamo (DB RCT)−205%3.05 · 0.12–74.69progression1/5020/52763mgIVERMILCO Siripongbo.. (RCT)0%1.00 · 0.07–15.26progression1/301/30168mgFINCOVCT2 Wijewic.. (DB RCT)−196%2.96 · 0.12–71.99death1/1270/12296mg Mouawia (RCT)56%0.44 · 0.14–1.37hosp.4/639/6312mg
Early treatment62%0.38 · 0.30–0.4871/16,013245/43,02562% lower risk
Tau² = 0.15, I² = 46.9%, p < 0.0001
Shouman (RCT)91%0.09 · 0.03–0.23symp. case15/20359/10136mg Carvallo96%0.04 · 0.00–0.63cases0/13111/9814mgsee notes CT2 Behera54%0.46 · 0.29–0.71cases41/117145/25542mg Carvallo100%0.00 · 0.00–0.02cases0/788237/40748mgsee notes CT2 Hellwig (ECO.)78%0.22 · 0.06–0.76casesecological14mg Bernigaud99%0.01 · 0.00–0.10death0/69150/3,06284mg Alam91%0.09 · 0.04–0.25cases4/5844/6012mg IVERCOR PREP73%0.27 · 0.15–0.48cases13/38961/48648mgMD3 Chahla (RCT)95%0.05 · 0.00–0.80m/s case0/11710/11748mgCT2 Behera83%0.17 · 0.12–0.23cases45/2,199133/1,14742mg Tanioka (ECO.)88%0.12 · 0.03–0.46deathecological14mg Seet (CLUS. RCT)50%0.50 · 0.33–0.76symp. case32/61764/61912mgOT1 Morgenstern (PSM)80%0.20 · 0.01–4.15hosp.0/2712/27156mg Mondal88%0.12 · 0.01–0.55symp. case128 (n)1,342 (n)n/a Samajdar80%0.20 · 0.11–0.38cases12/16429/81n/a Kerr (PSM)70%0.30 · 0.19–0.46death25/3,03479/3,03456mg Desort-H.. (DB RCT)96%0.04 · 0.02–0.11high v. load4/20099/199203mgSAIVE Wagstaff (DB RCT)55%0.45 · 0.22–1.00DAFS36 (n)32 (n)
Prophylaxis84%0.16 · 0.11–0.25191/8,5211,123/11,31184% lower risk
Tau² = 0.50, I² = 82.7%, p < 0.0001
All studies73%0.27 · 0.22–0.34262/24,5341,368/54,33673% lower risk
Tau² = 0.31, I² = 71.5%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
3 MD: minimal detail available currently
4 SC: study uses synthetic control arm
5 CS: censored, see details
Rotate screen for more detailsIncrease width for more details
← Ivermectin
reduces riskIvermectin
increases risk →
B
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Fig. 1. A. Random-effects meta-analysis for early treatment and prophylaxis studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. Simplified dosages are shown for comparison, these are the total dose in the first four days for treatment, or the monthly dose for prophylaxis, for a 70kg person. For details of effect extraction and full dosage information see the appendix. B. Timeline of results in ivermectin studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 3.6 months, compared to using all studies. Efficacy based on specific outcomes was delayed by 1.3 months, compared to using pooled outcomes.
Introduction
Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from8.
Immediate treatment recommended
SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury9-25 and cognitive deficits12,17, cardiovascular complications26-32, DNA damage33-36, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits37—the spike protein binds to fibrin leading to fibrinolysis-resistant blood clots, thromboinflammation, and neuropathology. Minimizing replication as early as possible is recommended.
Many treatments are expected to modulate infection
SARS-CoV-2 infection and replication involves the complex interplay of 500+ host and viral proteins and other factorsA,38-45, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk46, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
Extensive supporting research
Ivermectin, better known for antiparasitic activity, is a broad spectrum antiviral with activity against many viruses including H7N747, Dengue48-50, HIV-149, Simian virus 4051, Zika50,52,53, West Nile53, Yellow Fever54,55, Japanese encephalitis54, Chikungunya55, Semliki Forest virus55, Human papillomavirus56, Epstein-Barr56, BK Polyomavirus57, and Sindbis virus55.
Ivermectin inhibits importin-α/β-dependent nuclear import of viral proteins47,49,51,58, shows spike-ACE2 disruption at 1nM with microfluidic diffusional sizing59, binds to glycan sites on the SARS-CoV-2 spike protein preventing interaction with blood and epithelial cells and inhibiting hemagglutination60,61, shows dose-dependent inhibition of wildtype and omicron variants62, exhibits dose-dependent inhibition of lung injury63,64, may inhibit SARS-CoV-2 via IMPase inhibition50, may inhibit SARS-CoV-2 induced formation of fibrin clots resistant to degradation65, inhibits SARS-CoV-2 3CLpro66, may inhibit SARS-CoV-2 RdRp activity67,68, may minimize viral myocarditis by inhibiting NF-κB/p65-mediated inflammation in macrophages69, may be beneficial for COVID-19 ARDS by blocking GSDMD and NET formation70, may interfere with SARS-CoV-2's immune evasion via ORF8 binding71, may inhibit SARS-CoV-2 by disrupting CD147 interaction72-75, may inhibit SARS-CoV-2 attachment to lipid rafts via spike NTD binding76, shows protection against inflammation, cytokine storm, and mortality in an LPS mouse model sharing key pathological features of severe COVID-1977,78, may be beneficial in severe COVID-19 by binding IGF1 to inhibit the promotion of inflammation, fibrosis, and cell proliferation that leads to lung damage79, significantly mitigates bleomycin-induced pulmonary fibrosis by reducing collagen accumulation and inflammatory cell infiltration80, improves oxidative stress markers while suppressing myofibroblast proliferation80, may minimize SARS-CoV-2 induced cardiac damage81,82, may counter immune evasion by inhibiting NSP15-TBK1/KPNA1 interaction and restoring IRF3 activation83, may disrupt SARS-CoV-2 N and ORF6 protein nuclear transport and their suppression of host interferon responses84, reduces TAZ/YAP nuclear import, relieving SARS-CoV-2-driven suppression of IRF3 and NF-κB antiviral pathways85, increases Bifidobacteria which play a key role in the immune system86, has immunomodulatory87 and anti-inflammatory88,89 properties, and has an extensive and very positive safety profile90.
Analysis
We analyze all significant controlled studies of ivermectin for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random-effects meta-analysis results for all studies, studies within each treatment stage, mortality, ventilation, ICU admission, hospitalization, recovery, cases, viral clearance, peer-reviewed studies, Randomized Controlled Trials (RCTs), and after exclusion of lower quality studies.
Treatment timing
Fig. 3 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early treatment refers to treatment immediately or soon after symptoms appear, while late treatment refers to more delayed treatment.
Prophylaxis pre-exposure post-exposure Early treatment Late treatment Treatment delay viral load disease severity exposed symptom onset hospitalized ICU Expected benefit of antiviral therapy −2 0 2 4 6 8 10 Time relative to symptom onset (days) trajectories vary widely immediately or soon after symptoms after disease progression
Fig. 3. Treatment stages.
Evaluation Notes
Preclinical Research
Ivermectin, better known for antiparasitic activity, is a broad spectrum antiviral with activity against many viruses including H7N747, Dengue48-50, HIV-149, Simian virus 4051, Zika50,52,53, West Nile53, Yellow Fever54,55, Japanese encephalitis54, Chikungunya55, Semliki Forest virus55, Human papillomavirus56, Epstein-Barr56, BK Polyomavirus57, and Sindbis virus55.
Ivermectin inhibits importin-α/β-dependent nuclear import of viral proteins47,49,51,58, shows spike-ACE2 disruption at 1nM with microfluidic diffusional sizing59, binds to glycan sites on the SARS-CoV-2 spike protein preventing interaction with blood and epithelial cells and inhibiting hemagglutination60,61, shows dose-dependent inhibition of wildtype and omicron variants62, exhibits dose-dependent inhibition of lung injury63,64, may inhibit SARS-CoV-2 via IMPase inhibition50, may inhibit SARS-CoV-2 induced formation of fibrin clots resistant to degradation65, inhibits SARS-CoV-2 3CLpro66, may inhibit SARS-CoV-2 RdRp activity67,68, may minimize viral myocarditis by inhibiting NF-κB/p65-mediated inflammation in macrophages69, may be beneficial for COVID-19 ARDS by blocking GSDMD and NET formation70, may interfere with SARS-CoV-2's immune evasion via ORF8 binding71, may inhibit SARS-CoV-2 by disrupting CD147 interaction72-75, may inhibit SARS-CoV-2 attachment to lipid rafts via spike NTD binding76, shows protection against inflammation, cytokine storm, and mortality in an LPS mouse model sharing key pathological features of severe COVID-1977,78, may be beneficial in severe COVID-19 by binding IGF1 to inhibit the promotion of inflammation, fibrosis, and cell proliferation that leads to lung damage79, significantly mitigates bleomycin-induced pulmonary fibrosis by reducing collagen accumulation and inflammatory cell infiltration80, improves oxidative stress markers while suppressing myofibroblast proliferation80, may minimize SARS-CoV-2 induced cardiac damage81,82, may counter immune evasion by inhibiting NSP15-TBK1/KPNA1 interaction and restoring IRF3 activation83, may disrupt SARS-CoV-2 N and ORF6 protein nuclear transport and their suppression of host interferon responses84, and reduces TAZ/YAP nuclear import, relieving SARS-CoV-2-driven suppression of IRF3 and NF-κB antiviral pathways85.
35 in silico studies support the efficacy of ivermectin65,67,68,71,76,79,84,104-131.
28 in vitro studies support the efficacy of ivermectin50,56,59,60,62,66,76,77,81,82,85,87,132-147.
15 in vivo animal studies support the efficacy of ivermectin63,64,69,77,80,89,139,143,148-154.
Studies show synergistic antiviral effects for ivermectin in combination with favipiravir142, niclosamide137, paxlovid132, and remdesivir133,135,145,155, which may allow increased efficacy and/or similar efficacy at lower doses, potentially reducing dose-dependent side effects.
Studies show complementary effects for ivermectin in combination with hydroxychloroquine137 and doxycycline116.
8 studies investigate novel formulations of ivermectin that may be more effective for COVID-19136,149,150,154,156-159.
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Results
Table 1 summarizes the results of random-effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Fig. 4 plots individual results by treatment stage. Fig. 5, 6, 7, 8, 9, 10, 11, and 12 show forest plots for random-effects meta-analysis of all studies with pooled effects, and for specific outcomes: mortality, ICU admission, mechanical ventilation, hospitalization, recovery, cases, and viral clearance. Fig. 13 shows results for peer reviewed trials only, and the supplementary data contains peer reviewed and individual outcome results after exclusions.
Table 1. Random-effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, with different exclusions, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Relative Risk Studies Patients
All studies0.39 [0.33‑0.47] p < 0.0001****106220K
After exclusions0.34 [0.28‑0.42] p < 0.0001****70200K
Peer-reviewedPeer-reviewed0.41 [0.34‑0.50] p < 0.0001****91130K
RCTsRCTs0.47 [0.36‑0.61] p < 0.0001****5410K
RCTs after exclusionsRCTs w/exc.0.38 [0.28‑0.52] p < 0.0001****3910K
Mortality0.53 [0.42‑0.66] p < 0.0001****54120K
VentilationVent.0.65 [0.50‑0.83] p = 0.00063***2030K
ICU admissionICU0.60 [0.42‑0.88] p = 0.0078**1520K
HospitalizationHosp.0.66 [0.56‑0.79] p < 0.0001****31120K
Recovery0.62 [0.55‑0.71] p < 0.0001****4110K
Cases0.21 [0.14‑0.31] p < 0.0001****1710K
Viral0.56 [0.46‑0.69] p < 0.0001****334,502
RCT mortality0.73 [0.55‑0.96] p = 0.023*218,181
RCT casesRCT cases0.18 [0.07‑0.47] p = 0.00052***52,241
RCT viral0.73 [0.60‑0.87] p = 0.00078***253,403
Table 2. Random-effects meta-analysis results by treatment stage. Results show the relative risk with treatment and the 95% confidence interval.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies0.38 [0.30‑0.48]****0.38****
[0.30‑0.48]		0.60 [0.49‑0.73]****0.60****
[0.49‑0.73]		0.16 [0.11‑0.25]****0.16****
[0.11‑0.25]
After exclusions0.32 [0.25‑0.40]****0.32****
[0.25‑0.40]		0.50 [0.38‑0.65]****0.50****
[0.38‑0.65]		0.17 [0.10‑0.29]****0.17****
[0.10‑0.29]
Peer-reviewedPeer-reviewed0.40 [0.31‑0.52]****0.40****
[0.31‑0.52]		0.58 [0.45‑0.73]****0.58****
[0.45‑0.73]		0.18 [0.11‑0.29]****0.18****
[0.11‑0.29]
RCTsRCTs0.45 [0.34‑0.61]****0.45****
[0.34‑0.61]		0.71 [0.58‑0.87]***0.71***
[0.58‑0.87]		0.15 [0.05‑0.46]***0.15***
[0.05‑0.46]
RCTs after exclusionsRCTs w/exc.0.36 [0.27‑0.48]****0.36****
[0.27‑0.48]		0.61 [0.45‑0.82]**0.61**
[0.45‑0.82]		0.15 [0.05‑0.46]***0.15***
[0.05‑0.46]
Mortality0.63 [0.43‑0.92]*0.63*
[0.43‑0.92]		0.57 [0.44‑0.73]****0.57****
[0.44‑0.73]		0.10 [0.02‑0.50]**0.10**
[0.02‑0.50]
VentilationVent.0.86 [0.65‑1.14]0.86
[0.65‑1.14]		0.52 [0.38‑0.70]****0.52****
[0.38‑0.70]
ICU admissionICU0.35 [0.23‑0.53]****0.35****
[0.23‑0.53]		0.67 [0.46‑0.97]*0.67*
[0.46‑0.97]
HospitalizationHosp.0.47 [0.31‑0.71]***0.47***
[0.31‑0.71]		0.82 [0.71‑0.95]**0.82**
[0.71‑0.95]		0.33 [0.23‑0.46]****0.33****
[0.23‑0.46]
Recovery0.49 [0.36‑0.67]****0.49****
[0.36‑0.67]		0.73 [0.64‑0.82]****0.73****
[0.64‑0.82]		0.45 [0.22‑1.00]*0.45*
[0.22‑1.00]
Cases0.21 [0.14‑0.31]****0.21****
[0.14‑0.31]
Viral0.51 [0.39‑0.66]****0.51****
[0.39‑0.66]		0.69 [0.48‑0.98]*0.69*
[0.48‑0.98]
RCT mortality0.92 [0.56‑1.50]0.92
[0.56‑1.50]		0.66 [0.47‑0.91]*0.66*
[0.47‑0.91]
RCT casesRCT cases0.18 [0.07‑0.47]***0.18***
[0.07‑0.47]
RCT viral0.67 [0.54‑0.83]***0.67***
[0.54‑0.83]		1.03 [0.81‑1.31]1.03
[0.81‑1.31]
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Fig. 4. Results by treatment stage.
c19early.org
October 2026

Ivermectin COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96hosp.0/602/5614mgOT1 CT2 Espitia-Hernandez70%0.30 · 0.16–0.55recov. time28 (n)7 (n)12mgCT2 Carvallo85%0.15 · 0.02–1.28death1/323/1436mgCT2 Mahmud (DB RCT)86%0.14 · 0.01–2.75death0/1833/18312mgCT2 Szente Fonseca−14%1.14 · 0.75–1.66hosp.340 (n)377 (n)24mg Cadegiani78%0.22 · 0.01–4.48death0/1102/13742mgCT2 Ahmed (DB RCT)85%0.15 · 0.01–2.70symptoms0/173/1948mg Chaccour (DB RCT)96%0.04 · 0.00–1.01symptoms12 (n)12 (n)28mgSAINT Ghauri92%0.08 · 0.01–0.88no recov.0/377/5348mg Babalola (DB RCT)64%0.36 · 0.10–1.27viral+40 (n)20 (n)24mgOT1 Bukhari (RCT)82%0.18 · 0.07–0.46viral+4/4125/4512mg Mohan (DB RCT)62%0.38 · 0.08–1.75no recov.2/406/4528mgRIVET-COV Biber (DB RCT)70%0.30 · 0.03–2.76hosp.1/473/4236mg Elalfy87%0.13 · 0.06–0.27viral+7/6244/5136mgCT2 López-Me.. (DB RCT)67%0.33 · 0.01–8.11death0/2001/19884mg Chahla (CLUS. RCT)87%0.13 · 0.03–0.54no disch.2/11020/14424mg Mourya89%0.11 · 0.05–0.25viral+5/5047/5048mg Loue (QR)70%0.30 · 0.04–2.20death1/105/1514mg Merino (QR)74%0.26 · 0.11–0.57hosp.77,381 (all patients)24mgcensored, see notes CS5 Faisal (RCT)68%0.32 · 0.14–0.72no recov.6/5019/5048mg Aref (RCT)63%0.37 · 0.22–0.61recov. time57 (n)57 (n)n/a Krolewiecki (RCT)−152%2.52 · 0.11–58.11ventilation1/270/14168mg Vallejos (DB RCT)−33%1.33 · 0.30–5.72death4/2503/25124mg Reis (DB RCT)12%0.88 · 0.49–1.55death21/67924/67984mgTOGETHERimpossible data, see notes Buonfrate (DB RCT)−211%3.11 · 0.13–73.31hosp.1/280/31336mgCOVER Mayer55%0.45 · 0.32–0.63death3,266 (n)17,966 (n)151mg Borody92%0.08 · 0.01–0.79death0/6006/60096mgMD3 CT2 SC4 Abbas (DB RCT)−4%1.04 · 0.07–16.41death1/991/10384mg de Jesús Ascenci..59%0.41 · 0.36–0.47death/hosp.7,898 (n)20,150 (n)12mgCT2 Manomai.. (DB RCT)43%0.57 · 0.20–1.46no recov.3/366/3648mg de la Ro.. (DB RCT)−187%2.87 · 0.12–67.49misc.1/300/2636mg Rezai (DB RCT)−5%1.05 · 0.07–16.68death1/2681/28184mg Mirahma.. (DB RCT)67%0.33 · 0.03–3.14ventilation1/1313/13024mg Schilling (RCT)67%0.33 · 0.01–7.97hosp.0/451/45168mgPLATCOV Bramante (DB RCT)−197%2.97 · 0.12–72.71death1/4080/39690mgCOVID-OUTOT1 Mikamo (DB RCT)−205%3.05 · 0.12–74.69progression1/5020/52763mgIVERMILCO Siripongbo.. (RCT)0%1.00 · 0.07–15.26progression1/301/30168mgFINCOVCT2 Wijewic.. (DB RCT)−196%2.96 · 0.12–71.99death1/1270/12296mg Mouawia (RCT)56%0.44 · 0.14–1.37hosp.4/639/6312mg
Early treatment62%0.38 · 0.30–0.4871/16,013245/43,02562% lower risk
Tau² = 0.15, I² = 46.9%, p < 0.0001
Gorial71%0.29 · 0.01–5.76death0/162/7114mg Kishoria (RCT)−8%1.08 · 0.57–2.02no disch.11/197/1312mg Podder (RCT)16%0.84 · 0.55–1.12recov. time32 (n)30 (n)14mg Khan87%0.13 · 0.02–1.00death1/1159/13312mg Chachar (RCT)10%0.90 · 0.44–1.83no recov.9/2510/2536mg Soto-Becerra17%0.83 · 0.71–0.97death92/2031,438/2,63014mg Rajter (PSM)46%0.54 · 0.27–0.99death13/9824/9814mg Hashim (SB RCT)92%0.08 · 0.00–1.44death0/596/7028mgCT2 Camprubí40%0.60 · 0.18–2.01ventilation3/135/1314mg Spoorthi21%0.79 · 0.64–0.98recov. time50 (n)50 (n)n/aCT2 Budhiraja99%0.01 · 0.00–0.15death0/34103/942n/a Ravikirti (DB RCT)89%0.11 · 0.01–2.05death0/554/5724mg Okumuş (DB RCT)33%0.67 · 0.27–1.64death6/309/3056mg Shahbazn.. (DB RCT)−197%2.97 · 0.13–70.51death1/350/3414mg Lima-Morales78%0.22 · 0.12–0.41death15/48152/28712mgCT2 Beltran .. (DB RCT)14%0.86 · 0.29–2.56death5/366/3712mg Pott-Junior (RCT)85%0.15 · 0.01–1.93ventilation1/271/414mgcensored, see notes Huvemek (DB RCT)32%0.68 · 0.38–1.23no improv.13/5019/5084mg Ahsan50%0.50 · 0.28–0.90death17/11017/5521mgCT2 Abd-Elsalam (RCT)25%0.75 · 0.17–3.06death3/824/8236mg Hazan86%0.14 · 0.01–2.15death0/24synthetic24mgsee notes CT2 SC4 Elavarasi20%0.80 · 0.61–1.06death48/283311/1,475n/a Rezk80%0.20 · 0.01–4.13death0/1602/16072mg Lim (RCT)69%0.31 · 0.09–1.11death3/24110/249112mgI-TECH Ozer75%0.25 · 0.06–1.13death2/608/6028mg Ferreira−5%1.05 · 0.32–3.43death3/2111/81n/a Jamir (ICU)−53%1.53 · 0.88–2.67death32/7669/190n/aICU patients Mustafa64%0.36 · 0.12–1.14death3/7342/371varies Zubair−9%1.09 · 0.33–3.64death5/905/9812mg Thairu (PSM)88%0.12 · 0.01–2.14death0/214/2656mg Efimenko (PSM)69%0.31 · 0.20–0.48death1,072 (n)40,536 (n)n/aself-censored, see notes OT1 Soto−41%1.41 · 1.16–1.76death280/484374/934n/a Ravikirti3%0.97 · 0.74–1.24death53/171254/794varies George (RCT)30%0.70 · 0.25–1.93death5/358/3924mg Naggie (DB RCT)−202%3.02 · 0.12–74.08death1/7080/724168mgACTIV-6 Rezai (DB RCT)31%0.69 · 0.35–1.39death13/31118/29884mg Qadeer58%0.42 · 0.31–0.56viral+35/10584/10548mg Aref (RCT)74%0.26 · 0.12–0.55recov. time49 (n)47 (n)n/aLONG COVID Ochoa-Ja.. (DB RCT)57%0.43 · 0.07–2.50death2/374/3828mg Sarojvisut (RCT)−2%1.02 · 0.06–16.15death1/1571/160112mg Munir48%0.52 · 0.22–1.21death92 (n)908 (n)n/a Llenas-García17%0.83 · 0.38–1.84death10/9612/9614mg Wada (DB RCT)19%0.81 · 0.44–1.38progression19/10623/10614mg Osati32%0.68 · 0.45–0.92death448 (n)849 (n)n/a Hayward (RCT)−1%1.01 · 0.61–1.68death/hosp.34/2,15727/1,80673mgPRINCIPLE Varnaseri (DB RCT)82%0.18 · 0.04–0.78ventilation2/5511/5584mg Hashmi (RCT)26%0.74 · 0.38–1.47death81 (n)69 (n)56mgREMAP-CAPICU patients
Late treatment40%0.60 · 0.49–0.73741/8,7832,994/54,98540% lower risk
Tau² = 0.24, I² = 79.5%, p < 0.0001
Shouman (RCT)91%0.09 · 0.03–0.23symp. case15/20359/10136mg Carvallo96%0.04 · 0.00–0.63cases0/13111/9814mgsee notes CT2 Behera54%0.46 · 0.29–0.71cases41/117145/25542mg Carvallo100%0.00 · 0.00–0.02cases0/788237/40748mgsee notes CT2 Hellwig (ECO.)78%0.22 · 0.06–0.76casesecological14mg Bernigaud99%0.01 · 0.00–0.10death0/69150/3,06284mg Alam91%0.09 · 0.04–0.25cases4/5844/6012mg IVERCOR PREP73%0.27 · 0.15–0.48cases13/38961/48648mgMD3 Chahla (RCT)95%0.05 · 0.00–0.80m/s case0/11710/11748mgCT2 Behera83%0.17 · 0.12–0.23cases45/2,199133/1,14742mg Tanioka (ECO.)88%0.12 · 0.03–0.46deathecological14mg Seet (CLUS. RCT)50%0.50 · 0.33–0.76symp. case32/61764/61912mgOT1 Morgenstern (PSM)80%0.20 · 0.01–4.15hosp.0/2712/27156mg Mondal88%0.12 · 0.01–0.55symp. case128 (n)1,342 (n)n/a Samajdar80%0.20 · 0.11–0.38cases12/16429/81n/a Kerr (PSM)70%0.30 · 0.19–0.46death25/3,03479/3,03456mg Desort-H.. (DB RCT)96%0.04 · 0.02–0.11high v. load4/20099/199203mgSAIVE Wagstaff (DB RCT)55%0.45 · 0.22–1.00DAFS36 (n)32 (n)
Prophylaxis84%0.16 · 0.11–0.25191/8,5211,123/11,31184% lower risk
Tau² = 0.50, I² = 82.7%, p < 0.0001
All studies61%0.39 · 0.33–0.471,003/33,3174,362/109K61% lower risk
Tau² = 0.47, I² = 85.6%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
3 MD: minimal detail available currently
4 SC: study uses synthetic control arm
5 CS: censored, see details
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 5. Random-effects meta-analysis for all studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. Simplified dosages are shown for comparison, these are the total dose in the first four days for treatment, or the monthly dose for prophylaxis, for a 70kg person. For details of effect extraction and full dosage information see the appendix.
c19early.org
October 2026

Ivermectin COVID-19 mortality results

StudyImprovementRR · 95% CITreatmentControlDose (4d)Relative Risk
Carvallo85%0.15 · 0.02–1.281/323/1436mgCT2 Mahmud (DB RCT)86%0.14 · 0.01–2.750/1833/18312mgCT2 Cadegiani78%0.22 · 0.01–4.480/1102/13742mgCT2 López-Me.. (DB RCT)67%0.33 · 0.01–8.110/2001/19884mg Loue (QR)70%0.30 · 0.04–2.201/105/1514mg Vallejos (DB RCT)−33%1.33 · 0.30–5.724/2503/25124mg Reis (DB RCT)12%0.88 · 0.49–1.5521/67924/67984mgTOGETHERimpossible data, see notes Mayer55%0.45 · 0.32–0.633,266 (n)17,966 (n)151mg Borody92%0.08 · 0.01–0.790/6006/60096mgMD3 CT2 SC4 Abbas (DB RCT)−4%1.04 · 0.07–16.411/991/10384mg de Jesús Ascenci..15%0.85 · 0.68–1.06101/7,898303/20,15012mgCT2 Rezai (DB RCT)−5%1.05 · 0.07–16.681/2681/28184mg Bramante (DB RCT)−197%2.97 · 0.12–72.711/4080/39690mgCOVID-OUTOT1 Wijewic.. (DB RCT)−196%2.96 · 0.12–71.991/1270/12296mg
Early treatment37%0.63 · 0.43–0.92132/14,130352/41,09537% lower risk
Tau² = 0.11, I² = 38.2%, p = 0.015
Gorial71%0.29 · 0.01–5.760/162/7114mg Khan87%0.13 · 0.02–1.001/1159/13312mg Soto-Becerra17%0.83 · 0.71–0.9792/2031,438/2,63014mg Rajter (PSM)46%0.54 · 0.27–0.9913/9824/9814mg Hashim (SB RCT)92%0.08 · 0.00–1.440/596/7028mgCT2 Budhiraja99%0.01 · 0.00–0.150/34103/942n/a Ravikirti (DB RCT)89%0.11 · 0.01–2.050/554/5724mg Okumuş (DB RCT)33%0.67 · 0.27–1.646/309/3056mg Shahbazn.. (DB RCT)−197%2.97 · 0.13–70.511/350/3414mg Lima-Morales78%0.22 · 0.12–0.4115/48152/28712mgCT2 Beltran .. (DB RCT)14%0.86 · 0.29–2.565/366/3712mg Ahsan50%0.50 · 0.28–0.9017/11017/5521mgCT2 Abd-Elsalam (RCT)25%0.75 · 0.17–3.063/824/8236mg Hazan86%0.14 · 0.01–2.150/24synthetic24mgsee notes CT2 SC4 Elavarasi20%0.80 · 0.61–1.0648/283311/1,475n/a Rezk80%0.20 · 0.01–4.130/1602/16072mg Lim (RCT)69%0.31 · 0.09–1.113/24110/249112mgI-TECH Ozer75%0.25 · 0.06–1.132/608/6028mg Ferreira−5%1.05 · 0.32–3.433/2111/81n/a Jamir (ICU)−53%1.53 · 0.88–2.6732/7669/190n/aICU patients Mustafa64%0.36 · 0.12–1.143/7342/371varies Zubair−9%1.09 · 0.33–3.645/905/9812mg Thairu (PSM)88%0.12 · 0.01–2.140/214/2656mg Efimenko (PSM)69%0.31 · 0.20–0.481,072 (n)40,536 (n)n/aself-censored, see notes OT1 Soto−41%1.41 · 1.16–1.76280/484374/934n/a Ravikirti3%0.97 · 0.74–1.2453/171254/794varies George (RCT)30%0.70 · 0.25–1.935/358/3924mg Naggie (DB RCT)−202%3.02 · 0.12–74.081/7080/724168mgACTIV-6 Rezai (DB RCT)31%0.69 · 0.35–1.3913/31118/29884mg Ochoa-Ja.. (DB RCT)57%0.43 · 0.07–2.502/374/3828mg Sarojvisut (RCT)−2%1.02 · 0.06–16.151/1571/160112mg Munir48%0.52 · 0.22–1.2192 (n)908 (n)n/a Llenas-García17%0.83 · 0.38–1.8410/9612/9614mg Osati32%0.68 · 0.45–0.92448 (n)849 (n)n/a Hashmi (RCT)26%0.74 · 0.38–1.4781 (n)69 (n)56mgREMAP-CAPICU patients
Late treatment43%0.57 · 0.44–0.73614/6,0952,807/52,68143% lower risk
Tau² = 0.28, I² = 80.4%, p < 0.0001
Bernigaud99%0.01 · 0.00–0.100/69150/3,06284mg Tanioka (ECO.)88%0.12 · 0.03–0.46ecological14mg Kerr (PSM)70%0.30 · 0.19–0.4625/3,03479/3,03456mg
Prophylaxis90%0.10 · 0.02–0.5025/3,103229/6,09690% lower risk
Tau² = 1.39, I² = 76.5%, p = 0.005
All studies47%0.53 · 0.42–0.66771/23,3283,388/99,87247% lower risk
Tau² = 0.29, I² = 79.6%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
3 MD: minimal detail available currently
4 SC: study uses synthetic control arm
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 6. Random-effects meta-analysis for mortality results.
c19early.org
October 2026

Ivermectin COVID-19 mechanical ventilation results

StudyImprovementRR · 95% CITreatmentControlDose (4d)Relative Risk
Cadegiani94%0.06 · 0.00–0.990/1109/13742mgCT1 Krolewiecki (RCT)−152%2.52 · 0.11–58.111/270/14168mg Vallejos (DB RCT)−33%1.33 · 0.30–5.724/2503/25124mg Reis (DB RCT)23%0.77 · 0.43–1.3619/67925/67984mgTOGETHERimpossible data, see notes de Jesús Ascenci..9%0.91 · 0.70–1.1877/7,898216/20,15012mgCT1 Mirahma.. (DB RCT)67%0.33 · 0.03–3.141/1313/13024mg
Early treatment14%0.86 · 0.65–1.14102/9,095256/21,36114% lower risk
Tau² = 0.01, I² = 4.8%, p = 0.3
Rajter (PSM)64%0.36 · 0.12–1.104/9811/9814mg Camprubí40%0.60 · 0.18–2.013/135/1314mg Ravikirti (DB RCT)79%0.21 · 0.03–1.721/555/5724mg Shahbazn.. (DB RCT)−94%1.94 · 0.18–20.452/351/3414mg Lima-Morales52%0.48 · 0.20–1.188/43411/28712mgCT1 Pott-Junior (RCT)85%0.15 · 0.01–1.931/271/414mgcensored, see notes Abd-Elsalam (RCT)0%1.00 · 0.21–4.813/823/8236mg Lim (RCT)59%0.41 · 0.13–1.304/24110/249112mgI-TECH Ozer (PSM)13%0.87 · 0.11–5.583/602/6028mg Shimizu48%0.52 · 0.29–0.9339 (n)49 (n)14mg Rezai (DB RCT)50%0.50 · 0.24–1.07311 (n)298 (n)84mg Ochoa-Ja.. (DB RCT)−34%1.34 · 0.42–4.237/375/3828mg Hayward (RCT)−151%2.51 · 0.26–24.153/2,1491/1,80173mgPRINCIPLE Varnaseri (DB RCT)82%0.18 · 0.04–0.782/5511/5584mg
Late treatment48%0.52 · 0.38–0.7041/3,63666/3,12548% lower risk
Tau² = 0.00, I² = 0.0%, p < 0.0001
All studies35%0.65 · 0.50–0.83143/12,731322/24,48635% lower risk
Tau² = 0.04, I² = 14.3%, p = 0.0006300.511.52+
1 CT: study uses combined treatment
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 7. Random-effects meta-analysis for mechanical ventilation.
c19early.org
October 2026

Ivermectin COVID-19 ICU results

StudyImprovementRR · 95% CITreatmentControlDose (4d)Relative Risk
Mayer66%0.34 · 0.22–0.513,266 (n)17,966 (n)151mg Rezai (DB RCT)−9%1.09 · 0.07–17.32268 (n)281 (n)84mg
Early treatment65%0.35 · 0.23–0.533,534 (n)18,247 (n)65% lower risk
Tau² = 0.00, I² = 0.0%, p < 0.0001
Khan89%0.11 · 0.01–0.801/11511/13312mg Camprubí33%0.67 · 0.13–3.352/133/1314mg Ravikirti (DB RCT)14%0.86 · 0.28–2.675/556/5724mg Pott-Junior (RCT)85%0.15 · 0.01–1.931/271/414mgcensored, see notes Lim (RCT)22%0.78 · 0.27–2.206/2418/249112mgI-TECH Ozer (PSM)49%0.51 · 0.09–2.506/603/6028mg Shimizu43%0.57 · 0.32–1.0239 (n)49 (n)14mg Rezai (DB RCT)16%0.84 · 0.52–1.36311 (n)298 (n)84mg Ochoa-Ja.. (DB RCT)−37%1.37 · 0.53–3.578/376/3828mg Sarojvisut (RCT)−104%2.04 · 0.19–22.252/1571/160112mg Llenas-García−4%1.04 · 0.51–2.1196 (n)96 (n)14mg Hayward (RCT)−319%4.19 · 0.49–35.845/2,1491/1,80173mgPRINCIPLE Varnaseri (DB RCT)83%0.17 · 0.05–0.533/5518/5584mg
Late treatment33%0.67 · 0.46–0.9739/3,35558/3,01333% lower risk
Tau² = 0.12, I² = 28.8%, p = 0.036
All studies40%0.60 · 0.42–0.8839/6,88958/21,26040% lower risk
Tau² = 0.18, I² = 43.4%, p = 0.007800.511.52+
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 8. Random-effects meta-analysis for ICU admission.
c19early.org
October 2026

Ivermectin COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96hosp.0/602/5614mgOT1 CT2 Szente Fonseca−14%1.14 · 0.75–1.66hosp.340 (n)377 (n)24mg Cadegiani98%0.02 · 0.00–0.33hosp.0/11027/13742mgCT2 Biber (DB RCT)70%0.30 · 0.03–2.76hosp.1/473/4236mg Merino (QR)74%0.26 · 0.11–0.57hosp.77,381 (all patients)24mgcensored, see notes CS5 Vallejos (DB RCT)33%0.67 · 0.34–1.28hosp.14/25021/25124mg Reis (DB RCT)17%0.83 · 0.63–1.10hosp.79/67995/67984mgTOGETHERimpossible data, see notes Buonfrate (DB RCT)−211%3.11 · 0.13–73.31hosp.1/280/31336mgCOVER Borody93%0.07 · 0.04–0.13hosp.5/60070/60096mgMD3 CT2 SC4 de Jesús Ascenci..48%0.52 · 0.48–0.58hosp.485/7,8982,360/20,15012mgCT2 Rezai (DB RCT)−36%1.36 · 0.65–2.84hosp.268 (n)281 (n)84mg Mirahma.. (DB RCT)46%0.54 · 0.21–1.42hosp.6/13111/13024mg Schilling (RCT)67%0.33 · 0.01–7.97hosp.0/451/45168mgPLATCOV Bramante (DB RCT)61%0.39 · 0.08–2.00hosp.2/2065/20290mgCOVID-OUTOT1 Mouawia (RCT)56%0.44 · 0.14–1.37hosp.4/639/6312mg
Early treatment53%0.47 · 0.31–0.71597/10,7252,604/23,04453% lower risk
Tau² = 0.34, I² = 82.0%, p = 0.00035
Gorial42%0.58 · 0.45–0.75hosp. time16 (n)71 (n)14mg Khan40%0.60 · 0.44–0.81hosp. time115 (n)133 (n)12mg Spoorthi16%0.84 · 0.74–0.96hosp. time50 (n)50 (n)n/aCT2 Shahbazn.. (DB RCT)15%0.85 · 0.74–0.97hosp. time35 (n)34 (n)14mg Lima-Morales67%0.33 · 0.22–0.47hosp.44/48189/28712mgCT2 Beltran .. (DB RCT)−20%1.20 · 0.77–1.87hosp. time36 (n)37 (n)12mg Abd-Elsalam (RCT)20%0.80 · 0.63–1.03hosp. time82 (n)82 (n)36mg Hazan93%0.07 · 0.00–1.02hosp.0/24synthetic24mgsee notes CT2 SC4 Lim (RCT)−5%1.05 · 0.94–1.19hosp. time241 (n)249 (n)112mgI-TECH Ozer (PSM)−9%1.09 · 0.99–1.22hosp. time60 (n)60 (n)28mg Zubair−8%1.08 · 0.91–1.29hosp. time90 (n)98 (n)12mg Naggie (DB RCT)−5%1.05 · 0.43–2.58hosp.10/8179/774168mgACTIV-6 Rezai (DB RCT)−11%1.11 · 1.03–1.21hosp. time311 (n)298 (n)84mg Varnaseri (DB RCT)33%0.67 · 0.52–0.85hosp. time55 (n)55 (n)84mg
Late treatment18%0.82 · 0.71–0.9554/2,41398/2,22818% lower risk
Tau² = 0.06, I² = 88.3%, p = 0.008
Morgenstern (PSM)80%0.20 · 0.01–4.15hosp.0/2712/27156mg Kerr (PSM)67%0.33 · 0.23–0.46hosp.44/3,03499/3,03456mg
Prophylaxis67%0.33 · 0.23–0.4644/3,305101/3,30567% lower risk
Tau² = 0.00, I² = 0.0%, p < 0.0001
All studies34%0.66 · 0.56–0.79695/16,4432,803/28,57734% lower risk
Tau² = 0.14, I² = 91.7%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
3 MD: minimal detail available currently
4 SC: study uses synthetic control arm
5 CS: censored, see details
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reduces riskIvermectin
increases risk →
Fig. 9. Random-effects meta-analysis for hospitalization.
c19early.org
October 2026

Ivermectin COVID-19 recovery results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)46%0.54 · 0.40–0.73no recov.27/6047/5614mgOT1 CT2 Espitia-Hernandez70%0.30 · 0.16–0.55recov. time28 (n)7 (n)12mgCT2 Mahmud (DB RCT)94%0.06 · 0.04–0.09no recov.72/183100/18012mgCT2 Ghauri92%0.08 · 0.01–0.88no recov.0/377/5348mg Babalola (DB RCT)41%0.59 · 0.33–1.05∆Spo238 (n)18 (n)24mgOT1 Mohan (DB RCT)62%0.38 · 0.08–1.75no recov.2/406/4528mgRIVET-COV López-Me.. (DB RCT)15%0.85 · 0.56–1.25no recov.36/20042/19884mg Chahla (CLUS. RCT)87%0.13 · 0.03–0.54no disch.2/11020/14424mg Faisal (RCT)68%0.32 · 0.14–0.72no recov.6/5019/5048mg Aref (RCT)63%0.37 · 0.22–0.61recov. time57 (n)57 (n)n/a Reis (DB RCT)5%0.95 · 0.81–1.14no recov.679 (n)679 (n)84mgTOGETHERimpossible data, see notes Abbas (DB RCT)36%0.64 · 0.43–0.96no recov.26/9942/10384mg Manomai.. (DB RCT)43%0.57 · 0.20–1.46no recov.3/366/3648mg Rezai (DB RCT)−2%1.02 · 0.96–1.08no recov.268 (n)281 (n)84mg Mirahma.. (DB RCT)39%0.61 · 0.26–1.42no recov.8/13113/13024mg Mikamo (DB RCT)4%0.96 · 0.78–1.20no recov.502 (n)527 (n)63mgIVERMILCO Siripongbo.. (RCT)39%0.61 · 0.28–1.29symp. score30 (n)30 (n)168mgFINCOVCT2 Mouawia (RCT)31%0.69 · 0.53–0.90no recov.63 (n)63 (n)12mg
Early treatment51%0.49 · 0.36–0.67182/2,611302/2,65751% lower risk
Tau² = 0.32, I² = 93.4%, p < 0.0001
Gorial71%0.29 · 0.01–5.76no recov.0/162/7114mg Kishoria (RCT)−8%1.08 · 0.57–2.02no disch.11/197/1312mg Podder (RCT)16%0.84 · 0.55–1.12recov. time32 (n)30 (n)14mg Khan87%0.13 · 0.02–1.00no recov.1/1159/13312mg Chachar (RCT)10%0.90 · 0.44–1.83no recov.9/2510/2536mg Hashim (SB RCT)41%0.59 · 0.46–0.77recov. time70 (n)70 (n)28mgCT2 Spoorthi21%0.79 · 0.64–0.98recov. time50 (n)50 (n)n/aCT2 Ravikirti (DB RCT)89%0.11 · 0.01–2.05no disch.0/554/5724mg Shahbazn.. (DB RCT)32%0.68 · 0.47–1.00recov. time35 (n)34 (n)14mg Lima-Morales59%0.41 · 0.30–0.55no recov.75/481118/28712mgCT2 Beltran .. (DB RCT)−37%1.37 · 0.33–5.70no disch.4/363/3712mg Rezk33%0.67 · 0.35–1.27no recov.14/14520/13872mg Lim (RCT)−2%1.02 · 0.85–1.23no recov.116/241116/247112mgI-TECH Thairu (PSM)55%0.45 · 0.31–0.67no disch.61 (n)26 (n)56mg George (RCT)19%0.81 · 0.52–1.27recov. time35 (n)39 (n)24mg Naggie (DB RCT)2%0.98 · 0.89–1.09recovery708 (n)724 (n)168mgACTIV-6 Rezai (DB RCT)24%0.76 · 0.60–0.96no recov.311 (n)298 (n)84mg Aref (RCT)74%0.26 · 0.12–0.55recov. time49 (n)47 (n)n/aLONG COVID Sarojvisut (RCT)4%0.96 · 0.82–1.12recov. time157 (n)160 (n)112mg Wada (DB RCT)60%0.40 · 0.13–1.24no recov.4/10710/10714mg Hayward (RCT)16%0.84 · 0.78–0.92no recov.2,157 (n)1,806 (n)73mgPRINCIPLE Varnaseri (DB RCT)28%0.72 · 0.66–0.78recov. time55 (n)55 (n)84mg
Late treatment27%0.73 · 0.64–0.82234/4,960299/4,45427% lower risk
Tau² = 0.04, I² = 76.8%, p < 0.0001
Wagstaff (DB RCT)55%0.45 · 0.22–1.00DAFS36 (n)32 (n)
Prophylaxis55%0.45 · 0.22–0.9536 (n)32 (n)55% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.036
All studies38%0.62 · 0.55–0.71416/7,607601/7,14338% lower risk
Tau² = 0.10, I² = 89.1%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 10. Random-effects meta-analysis for recovery.
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October 2026

Ivermectin COVID-19 case results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1m)Relative Risk
Shouman (RCT)91%0.09 · 0.03–0.23symp. case15/20359/10136mg Carvallo96%0.04 · 0.00–0.63cases0/13111/9814mgsee notes CT2 Behera54%0.46 · 0.29–0.71cases41/117145/25542mg Carvallo100%0.00 · 0.00–0.02cases0/788237/40748mgsee notes CT2 Hellwig (ECO.)78%0.22 · 0.06–0.76casesecological14mg Bernigaud55%0.45 · 0.22–0.91cases7/69692/3,06284mg Alam91%0.09 · 0.04–0.25cases4/5844/6012mg IVERCOR PREP73%0.27 · 0.15–0.48cases13/38961/48648mgMD3 Chahla (RCT)84%0.16 · 0.04–0.46cases4/11725/11748mgCT2 Behera83%0.17 · 0.12–0.23cases45/2,199133/1,14742mg Seet (CLUS. RCT)50%0.50 · 0.33–0.76symp. case32/61764/61912mgOT1 Morgenstern (PSM)74%0.26 · 0.10–0.71cases5/27118/27156mg Mondal88%0.12 · 0.01–0.55symp. case128 (n)1,342 (n)n/a Samajdar80%0.20 · 0.11–0.38cases12/16429/81n/a Kerr (PSM)44%0.56 · 0.53–0.58casespopulation-based cohort56mg Desort-H.. (DB RCT)96%0.04 · 0.02–0.11high v. load4/20099/199203mgSAIVE Wagstaff (DB RCT)47%0.53 · 0.29–0.91viral+ time36 (n)32 (n)
Prophylaxis79%0.21 · 0.14–0.31182/5,4871,617/8,27779% lower risk
Tau² = 0.48, I² = 91.3%, p < 0.0001
All studies79%0.21 · 0.14–0.31182/5,4871,617/8,27779% lower risk
Tau² = 0.48, I² = 91.3%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
3 MD: minimal detail available currently
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reduces riskIvermectin
increases risk →
Fig. 11. Random-effects meta-analysis for cases.
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October 2026

Ivermectin COVID-19 viral clearance results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96viral+0/602/5614mgOT1 CT2 Espitia-Hernandez97%0.03 · 0.01–0.10viral+0/287/712mgCT2 Mahmud (DB RCT)39%0.61 · 0.44–0.83viral+14/18336/18012mgCT2 Ahmed (DB RCT)76%0.24 · 0.07–0.91viral+11/2220/2348mg Chaccour (DB RCT)95%0.05 · 0.00–0.67viral load12 (n)12 (n)28mgSAINT Babalola (DB RCT)64%0.36 · 0.10–1.27viral+40 (n)20 (n)24mgOT1 Bukhari (RCT)82%0.18 · 0.07–0.46viral+4/4125/4512mg Mohan (DB RCT)24%0.76 · 0.53–1.09viral+21/4031/4528mgRIVET-COV Biber (DB RCT)62%0.38 · 0.14–0.90viral+8/4717/4236mg Elalfy87%0.13 · 0.06–0.27viral+7/6244/5136mgCT2 Mourya89%0.11 · 0.05–0.25viral+5/5047/5048mg Aref (RCT)79%0.21 · 0.07–0.71viral+3/5714/57n/a Krolewiecki (RCT)66%0.34 · 0.10–1.16decay rate20 (n)12 (n)168mg Vallejos (DB RCT)−5%1.05 · 0.88–1.21viral+137/250131/25124mg Reis (DB RCT)0%1.00 · 0.68–1.47viral+106/142123/16584mgTOGETHERimpossible data, see notes Buonfrate (DB RCT)20%0.80 · 0.36–1.76viral load28 (n)29 (n)336mgCOVER Manomai.. (DB RCT)5%0.95 · 0.62–1.45viral+19/3620/3648mg de la Ro.. (DB RCT)2%0.98 · 0.81–1.18viral load30 (n)26 (n)36mg Rezai (DB RCT)−23%1.23 · 0.92–1.67viral+268 (n)281 (n)84mg Schilling (RCT)−9%1.09 · 0.88–1.27viral rate45 (n)45 (n)168mgPLATCOV Siripongbo.. (RCT)6%0.94 · 0.65–1.35viral+30 (n)30 (n)168mgFINCOVCT2 Wijewic.. (DB RCT)51%0.49 · 0.26–0.93viral load80 (n)81 (n)96mg Mouawia (RCT)62%0.38 · 0.30–0.48viral load63 (n)63 (n)12mg
Early treatment49%0.51 · 0.39–0.66335/1,634517/1,60749% lower risk
Tau² = 0.25, I² = 87.8%, p < 0.0001
Kishoria (RCT)−8%1.08 · 0.57–2.02viral+11/197/1312mg Khan73%0.27 · 0.12–0.58viral time115 (n)133 (n)12mg Camprubí−25%1.25 · 0.43–3.63viral+5/134/1314mg Ravikirti (DB RCT)−12%1.12 · 0.89–1.40viral+42/5539/5724mg Okumuş (DB RCT)80%0.20 · 0.05–0.81viral+2/165/856mg Pott-Junior (RCT)1%0.99 · 0.04–26.30viral+27 (n)3 (n)14mgcensored, see notes Rezk27%0.73 · 0.57–0.93viral time160 (n)160 (n)72mg Thairu (PSM)95%0.05 · 0.00–0.85viral+0/2110/2656mg Qadeer58%0.42 · 0.31–0.56viral+35/10584/10548mg Wada (DB RCT)−4%1.04 · 0.76–1.43viral+106 (n)106 (n)14mg
Late treatment31%0.69 · 0.48–0.9895/637149/62431% lower risk
Tau² = 0.19, I² = 81.5%, p = 0.037
All studies44%0.56 · 0.46–0.69430/2,271666/2,23144% lower risk
Tau² = 0.22, I² = 86.1%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 12. Random-effects meta-analysis for viral clearance.
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October 2026

Ivermectin COVID-19 peer reviewed studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96hosp.0/602/5614mgOT1 CT2 Espitia-Hernandez70%0.30 · 0.16–0.55recov. time28 (n)7 (n)12mgCT2 Carvallo85%0.15 · 0.02–1.28death1/323/1436mgCT2 Mahmud (DB RCT)86%0.14 · 0.01–2.75death0/1833/18312mgCT2 Szente Fonseca−14%1.14 · 0.75–1.66hosp.340 (n)377 (n)24mg Cadegiani78%0.22 · 0.01–4.48death0/1102/13742mgCT2 Ahmed (DB RCT)85%0.15 · 0.01–2.70symptoms0/173/1948mg Chaccour (DB RCT)96%0.04 · 0.00–1.01symptoms12 (n)12 (n)28mgSAINT Ghauri92%0.08 · 0.01–0.88no recov.0/377/5348mg Babalola (DB RCT)64%0.36 · 0.10–1.27viral+40 (n)20 (n)24mgOT1 Mohan (DB RCT)62%0.38 · 0.08–1.75no recov.2/406/4528mgRIVET-COV Biber (DB RCT)70%0.30 · 0.03–2.76hosp.1/473/4236mg Elalfy87%0.13 · 0.06–0.27viral+7/6244/5136mgCT2 López-Me.. (DB RCT)67%0.33 · 0.01–8.11death0/2001/19884mg Chahla (CLUS. RCT)87%0.13 · 0.03–0.54no disch.2/11020/14424mg Mourya89%0.11 · 0.05–0.25viral+5/5047/5048mg Loue (QR)70%0.30 · 0.04–2.20death1/105/1514mg Faisal (RCT)68%0.32 · 0.14–0.72no recov.6/5019/5048mg Aref (RCT)63%0.37 · 0.22–0.61recov. time57 (n)57 (n)n/a Krolewiecki (RCT)−152%2.52 · 0.11–58.11ventilation1/270/14168mg Vallejos (DB RCT)−33%1.33 · 0.30–5.72death4/2503/25124mg Reis (DB RCT)12%0.88 · 0.49–1.55death21/67924/67984mgTOGETHERimpossible data, see notes Buonfrate (DB RCT)−211%3.11 · 0.13–73.31hosp.1/280/31336mgCOVER Mayer55%0.45 · 0.32–0.63death3,266 (n)17,966 (n)151mg Abbas (DB RCT)−4%1.04 · 0.07–16.41death1/991/10384mg de Jesús Ascenci..59%0.41 · 0.36–0.47death/hosp.7,898 (n)20,150 (n)12mgCT2 Manomai.. (DB RCT)43%0.57 · 0.20–1.46no recov.3/366/3648mg de la Ro.. (DB RCT)−187%2.87 · 0.12–67.49misc.1/300/2636mg Rezai (DB RCT)−5%1.05 · 0.07–16.68death1/2681/28184mg Mirahma.. (DB RCT)67%0.33 · 0.03–3.14ventilation1/1313/13024mg Schilling (RCT)67%0.33 · 0.01–7.97hosp.0/451/45168mgPLATCOV Bramante (DB RCT)−197%2.97 · 0.12–72.71death1/4080/39690mgCOVID-OUTOT1 Mikamo (DB RCT)−205%3.05 · 0.12–74.69progression1/5020/52763mgIVERMILCO Siripongbo.. (RCT)0%1.00 · 0.07–15.26progression1/301/30168mgFINCOVCT2 Wijewic.. (DB RCT)−196%2.96 · 0.12–71.99death1/1270/12296mg Mouawia (RCT)56%0.44 · 0.14–1.37hosp.4/639/6312mg
Early treatment60%0.40 · 0.31–0.5267/15,372214/42,38060% lower risk
Tau² = 0.15, I² = 46.3%, p < 0.0001
Kishoria (RCT)−8%1.08 · 0.57–2.02no disch.11/197/1312mg Podder (RCT)16%0.84 · 0.55–1.12recov. time32 (n)30 (n)14mg Khan87%0.13 · 0.02–1.00death1/1159/13312mg Chachar (RCT)10%0.90 · 0.44–1.83no recov.9/2510/2536mg Rajter (PSM)46%0.54 · 0.27–0.99death13/9824/9814mg Hashim (SB RCT)92%0.08 · 0.00–1.44death0/596/7028mgCT2 Camprubí40%0.60 · 0.18–2.01ventilation3/135/1314mg Spoorthi21%0.79 · 0.64–0.98recov. time50 (n)50 (n)n/aCT2 Ravikirti (DB RCT)89%0.11 · 0.01–2.05death0/554/5724mg Okumuş (DB RCT)33%0.67 · 0.27–1.64death6/309/3056mg Shahbazn.. (DB RCT)−197%2.97 · 0.13–70.51death1/350/3414mg Lima-Morales78%0.22 · 0.12–0.41death15/48152/28712mgCT2 Beltran .. (DB RCT)14%0.86 · 0.29–2.56death5/366/3712mg Pott-Junior (RCT)85%0.15 · 0.01–1.93ventilation1/271/414mgcensored, see notes Ahsan50%0.50 · 0.28–0.90death17/11017/5521mgCT2 Abd-Elsalam (RCT)25%0.75 · 0.17–3.06death3/824/8236mg Elavarasi20%0.80 · 0.61–1.06death48/283311/1,475n/a Rezk80%0.20 · 0.01–4.13death0/1602/16072mg Lim (RCT)69%0.31 · 0.09–1.11death3/24110/249112mgI-TECH Ozer75%0.25 · 0.06–1.13death2/608/6028mg Ferreira−5%1.05 · 0.32–3.43death3/2111/81n/a Jamir (ICU)−53%1.53 · 0.88–2.67death32/7669/190n/aICU patients Mustafa64%0.36 · 0.12–1.14death3/7342/371varies Zubair−9%1.09 · 0.33–3.64death5/905/9812mg Thairu (PSM)88%0.12 · 0.01–2.14death0/214/2656mg Efimenko (PSM)69%0.31 · 0.20–0.48death1,072 (n)40,536 (n)n/aself-censored, see notes OT1 Soto−41%1.41 · 1.16–1.76death280/484374/934n/a George (RCT)30%0.70 · 0.25–1.93death5/358/3924mg Naggie (DB RCT)−202%3.02 · 0.12–74.08death1/7080/724168mgACTIV-6 Rezai (DB RCT)31%0.69 · 0.35–1.39death13/31118/29884mg Qadeer58%0.42 · 0.31–0.56viral+35/10584/10548mg Aref (RCT)74%0.26 · 0.12–0.55recov. time49 (n)47 (n)n/aLONG COVID Ochoa-Ja.. (DB RCT)57%0.43 · 0.07–2.50death2/374/3828mg Sarojvisut (RCT)−2%1.02 · 0.06–16.15death1/1571/160112mg Munir48%0.52 · 0.22–1.21death92 (n)908 (n)n/a Llenas-García17%0.83 · 0.38–1.84death10/9612/9614mg Wada (DB RCT)19%0.81 · 0.44–1.38progression19/10623/10614mg Hayward (RCT)−1%1.01 · 0.61–1.68death/hosp.34/2,15727/1,80673mgPRINCIPLE Varnaseri (DB RCT)82%0.18 · 0.04–0.78ventilation2/5511/5584mg Hashmi (RCT)26%0.74 · 0.38–1.47death81 (n)69 (n)56mgREMAP-CAPICU patients
Late treatment42%0.58 · 0.45–0.73583/7,8371,178/49,64942% lower risk
Tau² = 0.33, I² = 81.5%, p < 0.0001
Shouman (RCT)91%0.09 · 0.03–0.23symp. case15/20359/10136mg Behera54%0.46 · 0.29–0.71cases41/117145/25542mg Carvallo100%0.00 · 0.00–0.02cases0/788237/40748mgsee notes CT2 Hellwig (ECO.)78%0.22 · 0.06–0.76casesecological14mg Bernigaud99%0.01 · 0.00–0.10death0/69150/3,06284mg Alam91%0.09 · 0.04–0.25cases4/5844/6012mg Chahla (RCT)95%0.05 · 0.00–0.80m/s case0/11710/11748mgCT2 Behera83%0.17 · 0.12–0.23cases45/2,199133/1,14742mg Seet (CLUS. RCT)50%0.50 · 0.33–0.76symp. case32/61764/61912mgOT1 Morgenstern (PSM)80%0.20 · 0.01–4.15hosp.0/2712/27156mg Mondal88%0.12 · 0.01–0.55symp. case128 (n)1,342 (n)n/a Samajdar80%0.20 · 0.11–0.38cases12/16429/81n/a Kerr (PSM)70%0.30 · 0.19–0.46death25/3,03479/3,03456mg Wagstaff (DB RCT)55%0.45 · 0.22–1.00DAFS36 (n)32 (n)
Prophylaxis82%0.18 · 0.11–0.29174/7,801952/10,52882% lower risk
Tau² = 0.48, I² = 84.0%, p < 0.0001
All studies59%0.41 · 0.34–0.50824/31,0102,344/102K59% lower risk
Tau² = 0.51, I² = 85.9%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
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reduces riskIvermectin
increases risk →
Fig. 13. Random-effects meta-analysis for peer-reviewed studies. Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Davidson et al. also showed no important difference between meta-analysis results of preprints and peer-reviewed publications for COVID-19, based on 37 meta-analyses including 114 trials. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below.
Randomized Controlled Trials (RCTs)
Results restricted to Randomized Controlled Trials (RCTs) are shown in Fig. 14, 15, 16, and 17, Table 1, and Table 2. The supplementary data contains RCT results after exclusions.
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Fig. 14. Randomized Controlled Trials. The distribution of results for RCTs is similar to the distribution for observational studies.
c19early.org
October 2026

Ivermectin COVID-19 Randomized Controlled Trials

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96hosp.0/602/5614mgOT1 CT2 Mahmud (DB RCT)86%0.14 · 0.01–2.75death0/1833/18312mgCT2 Ahmed (DB RCT)85%0.15 · 0.01–2.70symptoms0/173/1948mg Chaccour (DB RCT)96%0.04 · 0.00–1.01symptoms12 (n)12 (n)28mgSAINT Babalola (DB RCT)64%0.36 · 0.10–1.27viral+40 (n)20 (n)24mgOT1 Bukhari (RCT)82%0.18 · 0.07–0.46viral+4/4125/4512mg Mohan (DB RCT)62%0.38 · 0.08–1.75no recov.2/406/4528mgRIVET-COV Biber (DB RCT)70%0.30 · 0.03–2.76hosp.1/473/4236mg López-Me.. (DB RCT)67%0.33 · 0.01–8.11death0/2001/19884mg Chahla (CLUS. RCT)87%0.13 · 0.03–0.54no disch.2/11020/14424mg Faisal (RCT)68%0.32 · 0.14–0.72no recov.6/5019/5048mg Aref (RCT)63%0.37 · 0.22–0.61recov. time57 (n)57 (n)n/a Krolewiecki (RCT)−152%2.52 · 0.11–58.11ventilation1/270/14168mg Vallejos (DB RCT)−33%1.33 · 0.30–5.72death4/2503/25124mg Reis (DB RCT)12%0.88 · 0.49–1.55death21/67924/67984mgTOGETHERimpossible data, see notes Buonfrate (DB RCT)−211%3.11 · 0.13–73.31hosp.1/280/31336mgCOVER Abbas (DB RCT)−4%1.04 · 0.07–16.41death1/991/10384mg Manomai.. (DB RCT)43%0.57 · 0.20–1.46no recov.3/366/3648mg de la Ro.. (DB RCT)−187%2.87 · 0.12–67.49misc.1/300/2636mg Rezai (DB RCT)−5%1.05 · 0.07–16.68death1/2681/28184mg Mirahma.. (DB RCT)67%0.33 · 0.03–3.14ventilation1/1313/13024mg Schilling (RCT)67%0.33 · 0.01–7.97hosp.0/451/45168mgPLATCOV Bramante (DB RCT)−197%2.97 · 0.12–72.71death1/4080/39690mgCOVID-OUTOT1 Mikamo (DB RCT)−205%3.05 · 0.12–74.69progression1/5020/52763mgIVERMILCO Siripongbo.. (RCT)0%1.00 · 0.07–15.26progression1/301/30168mgFINCOVCT2 Wijewic.. (DB RCT)−196%2.96 · 0.12–71.99death1/1270/12296mg Mouawia (RCT)56%0.44 · 0.14–1.37hosp.4/639/6312mg
Early treatment55%0.45 · 0.34–0.6157/3,580131/3,60555% lower risk
Tau² = 0.05, I² = 8.9%, p < 0.0001
Kishoria (RCT)−8%1.08 · 0.57–2.02no disch.11/197/1312mg Podder (RCT)16%0.84 · 0.55–1.12recov. time32 (n)30 (n)14mg Chachar (RCT)10%0.90 · 0.44–1.83no recov.9/2510/2536mg Hashim (SB RCT)92%0.08 · 0.00–1.44death0/596/7028mgCT2 Ravikirti (DB RCT)89%0.11 · 0.01–2.05death0/554/5724mg Okumuş (DB RCT)33%0.67 · 0.27–1.64death6/309/3056mg Shahbazn.. (DB RCT)−197%2.97 · 0.13–70.51death1/350/3414mg Beltran .. (DB RCT)14%0.86 · 0.29–2.56death5/366/3712mg Pott-Junior (RCT)85%0.15 · 0.01–1.93ventilation1/271/414mgcensored, see notes Huvemek (DB RCT)32%0.68 · 0.38–1.23no improv.13/5019/5084mg Abd-Elsalam (RCT)25%0.75 · 0.17–3.06death3/824/8236mg Lim (RCT)69%0.31 · 0.09–1.11death3/24110/249112mgI-TECH George (RCT)30%0.70 · 0.25–1.93death5/358/3924mg Naggie (DB RCT)−202%3.02 · 0.12–74.08death1/7080/724168mgACTIV-6 Rezai (DB RCT)31%0.69 · 0.35–1.39death13/31118/29884mg Aref (RCT)74%0.26 · 0.12–0.55recov. time49 (n)47 (n)n/aLONG COVID Ochoa-Ja.. (DB RCT)57%0.43 · 0.07–2.50death2/374/3828mg Sarojvisut (RCT)−2%1.02 · 0.06–16.15death1/1571/160112mg Wada (DB RCT)19%0.81 · 0.44–1.38progression19/10623/10614mg Hayward (RCT)−1%1.01 · 0.61–1.68death/hosp.34/2,15727/1,80673mgPRINCIPLE Varnaseri (DB RCT)82%0.18 · 0.04–0.78ventilation2/5511/5584mg Hashmi (RCT)26%0.74 · 0.38–1.47death81 (n)69 (n)56mgREMAP-CAPICU patients
Late treatment29%0.71 · 0.58–0.87129/4,387168/4,02329% lower risk
Tau² = 0.03, I² = 11.1%, p = 0.00085
Shouman (RCT)91%0.09 · 0.03–0.23symp. case15/20359/10136mg Chahla (RCT)95%0.05 · 0.00–0.80m/s case0/11710/11748mgCT2 Seet (CLUS. RCT)50%0.50 · 0.33–0.76symp. case32/61764/61912mgOT1 Desort-H.. (DB RCT)96%0.04 · 0.02–0.11high v. load4/20099/199203mgSAIVE Wagstaff (DB RCT)55%0.45 · 0.22–1.00DAFS36 (n)32 (n)
Prophylaxis85%0.15 · 0.05–0.4651/1,173232/1,06885% lower risk
Tau² = 1.23, I² = 90.9%, p = 0.0008
All studies53%0.47 · 0.36–0.61237/9,140531/8,69653% lower risk
Tau² = 0.41, I² = 62.2%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 15. Random-effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. Simplified dosages are shown for comparison, these are the total dose in the first four days for treatment, or the monthly dose for prophylaxis, for a 70kg person. For details of effect extraction and full dosage information see the appendix.
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October 2026

Ivermectin COVID-19 RCT mortality results

StudyImprovementRR · 95% CITreatmentControlDose (4d)Relative Risk
Mahmud (DB RCT)86%0.14 · 0.01–2.750/1833/18312mgCT2 López-Me.. (DB RCT)67%0.33 · 0.01–8.110/2001/19884mg Vallejos (DB RCT)−33%1.33 · 0.30–5.724/2503/25124mg Reis (DB RCT)12%0.88 · 0.49–1.5521/67924/67984mgTOGETHERimpossible data, see notes Abbas (DB RCT)−4%1.04 · 0.07–16.411/991/10384mg Rezai (DB RCT)−5%1.05 · 0.07–16.681/2681/28184mg Bramante (DB RCT)−197%2.97 · 0.12–72.711/4080/39690mgCOVID-OUTOT1 Wijewic.. (DB RCT)−196%2.96 · 0.12–71.991/1270/12296mg
Early treatment8%0.92 · 0.56–1.5029/2,21433/2,2138% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.74
Hashim (SB RCT)92%0.08 · 0.00–1.440/596/7028mgCT2 Ravikirti (DB RCT)89%0.11 · 0.01–2.050/554/5724mg Okumuş (DB RCT)33%0.67 · 0.27–1.646/309/3056mg Shahbazn.. (DB RCT)−197%2.97 · 0.13–70.511/350/3414mg Beltran .. (DB RCT)14%0.86 · 0.29–2.565/366/3712mg Abd-Elsalam (RCT)25%0.75 · 0.17–3.063/824/8236mg Lim (RCT)69%0.31 · 0.09–1.113/24110/249112mgI-TECH George (RCT)30%0.70 · 0.25–1.935/358/3924mg Naggie (DB RCT)−202%3.02 · 0.12–74.081/7080/724168mgACTIV-6 Rezai (DB RCT)31%0.69 · 0.35–1.3913/31118/29884mg Ochoa-Ja.. (DB RCT)57%0.43 · 0.07–2.502/374/3828mg Sarojvisut (RCT)−2%1.02 · 0.06–16.151/1571/160112mg Hashmi (RCT)26%0.74 · 0.38–1.4781 (n)69 (n)56mgREMAP-CAPICU patients
Late treatment34%0.66 · 0.47–0.9140/1,86770/1,88734% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.012
All studies27%0.73 · 0.55–0.9669/4,081103/4,10027% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.02300.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 16. Random-effects meta-analysis for RCT mortality results.
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October 2026

Ivermectin COVID-19 RCT case results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1m)Relative Risk
Shouman (RCT)91%0.09 · 0.03–0.23symp. case15/20359/10136mg Chahla (RCT)84%0.16 · 0.04–0.46cases4/11725/11748mgCT2 Seet (CLUS. RCT)50%0.50 · 0.33–0.76symp. case32/61764/61912mgOT1 Desort-H.. (DB RCT)96%0.04 · 0.02–0.11high v. load4/20099/199203mgSAIVE Wagstaff (DB RCT)47%0.53 · 0.29–0.91viral+ time36 (n)32 (n)
Prophylaxis82%0.18 · 0.07–0.4755/1,173247/1,06882% lower risk
Tau² = 1.08, I² = 91.6%, p = 0.00052
All studies82%0.18 · 0.07–0.4755/1,173247/1,06882% lower risk
Tau² = 1.08, I² = 91.6%, p = 0.0005200.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 18. Random-effects meta-analysis for RCT case results.
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October 2026

Ivermectin COVID-19 RCT viral clearance results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96viral+0/602/5614mgOT1 CT2 Mahmud (DB RCT)39%0.61 · 0.44–0.83viral+14/18336/18012mgCT2 Ahmed (DB RCT)76%0.24 · 0.07–0.91viral+11/2220/2348mg Chaccour (DB RCT)95%0.05 · 0.00–0.67viral load12 (n)12 (n)28mgSAINT Babalola (DB RCT)64%0.36 · 0.10–1.27viral+40 (n)20 (n)24mgOT1 Bukhari (RCT)82%0.18 · 0.07–0.46viral+4/4125/4512mg Mohan (DB RCT)24%0.76 · 0.53–1.09viral+21/4031/4528mgRIVET-COV Biber (DB RCT)62%0.38 · 0.14–0.90viral+8/4717/4236mg Aref (RCT)79%0.21 · 0.07–0.71viral+3/5714/57n/a Krolewiecki (RCT)66%0.34 · 0.10–1.16decay rate20 (n)12 (n)168mg Vallejos (DB RCT)−5%1.05 · 0.88–1.21viral+137/250131/25124mg Reis (DB RCT)0%1.00 · 0.68–1.47viral+106/142123/16584mgTOGETHERimpossible data, see notes Buonfrate (DB RCT)20%0.80 · 0.36–1.76viral load28 (n)29 (n)336mgCOVER Manomai.. (DB RCT)5%0.95 · 0.62–1.45viral+19/3620/3648mg de la Ro.. (DB RCT)2%0.98 · 0.81–1.18viral load30 (n)26 (n)36mg Rezai (DB RCT)−23%1.23 · 0.92–1.67viral+268 (n)281 (n)84mg Schilling (RCT)−9%1.09 · 0.88–1.27viral rate45 (n)45 (n)168mgPLATCOV Siripongbo.. (RCT)6%0.94 · 0.65–1.35viral+30 (n)30 (n)168mgFINCOVCT2 Wijewic.. (DB RCT)51%0.49 · 0.26–0.93viral load80 (n)81 (n)96mg Mouawia (RCT)62%0.38 · 0.30–0.48viral load63 (n)63 (n)12mg
Early treatment33%0.67 · 0.54–0.83323/1,494419/1,49933% lower risk
Tau² = 0.14, I² = 81.9%, p = 0.00036
Kishoria (RCT)−8%1.08 · 0.57–2.02viral+11/197/1312mg Ravikirti (DB RCT)−12%1.12 · 0.89–1.40viral+42/5539/5724mg Okumuş (DB RCT)80%0.20 · 0.05–0.81viral+2/165/856mg Pott-Junior (RCT)1%0.99 · 0.04–26.30viral+27 (n)3 (n)14mgcensored, see notes Wada (DB RCT)−4%1.04 · 0.76–1.43viral+106 (n)106 (n)14mg
Late treatment−3%1.03 · 0.81–1.3155/22351/1873% higher risk
Tau² = 0.02, I² = 29.2%, p = 0.84
All studies27%0.73 · 0.60–0.87378/1,717470/1,68627% lower risk
Tau² = 0.12, I² = 79.3%, p = 0.0007800.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
Rotate screen for more detailsIncrease width for more details
← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 17. Random-effects meta-analysis for RCT viral clearance results.
RCTs have many potential biases
RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases162, and analysis of double-blind RCTs has identified extreme levels of bias163. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, reporting, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
Conflicts of interest for COVID-19 RCTs
RCTs are expensive and many RCTs are funded by pharmaceutical companies or other organizations with conflicts of interest, for example governments that previously denied treatment with the study drug. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. Bekelman et al. and Lundh et al. show that industry-sponsored studies are more likely to be favorable. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.
RCTs for novel acute diseases requiring rapid treatment
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 227 treatments we have analyzed, 67% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.
RCT bias for widely available treatments
RCTs have a bias against finding an effect for interventions that are widely available—patients that believe they need the intervention are more likely to decline participation and take the intervention. RCTs for ivermectin are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments, and may be greater when the risk of a serious outcome is overstated. This bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Fig. 19. For COVID-19, observational study results do not systematically differ from RCTs, RR 0.97 [0.91‑1.03] across 227 treatments167.
Observational studies have been shown to be reliable
Evidence shows that observational studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates.
We performed a similar analysis across the 227 treatments we cover, showing no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]95. Similar results are found for all low-cost treatments, RR 0.98 [0.90‑1.06]. High-cost treatments show a non-significant trend towards RCTs showing greater efficacy, RR 0.93 [0.85‑1.02]. Details can be found in the supplementary data.
Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or remote survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see171,172.
RCTs may be less reliable
Concato et al. report a paradoxical finding—RCT results had higher variability, and only RCTs were found to sometimes report significant results the opposite of the overall result. The same trend is seen for the most popular (most politicized) COVID-19 treatments including ivermectin—considering all statistically significant results reported in studies, RCTs are slightly more likely to report a result in the opposite direction. In other words, for these COVID-19 treatments and for the topics covered by Concato et al., assuming causality from a single study is more likely to result in an incorrect conclusion for RCTs.
Increased risk of inconsistent results for RCTs suggests higher prevalence of bias, which may arise due to many issues including design bias, conflicts of interest, treatment differences by physicians aware of allocation, attrition bias, ascertainment bias, randomization failures, errors, or fraud.
Using all studies identifies efficacy 8+ months faster (9+ months for low-cost treatments)
Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 54% have been confirmed in RCTs, with a mean delay of 7.8 months (62% with 8.7 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
All studies must be carefully analyzed
Neither observational studies nor RCTs prove causation—any study can be flawed or fraudulent. We need much more, for example a combination of results from many independent teams, detailed understanding of each study, knowledge of conflicts/team reliability, dose-response relationships, delay-response relationships, logical results across outcomes, or details consistent with preclinical expectations.
All studies must be evaluated individually. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta-analyses.
Exclusions
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with critical issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be subject to bias. However, they can also be very high quality5.
A team of researchers has analyzed the data in ivermectin studies and identified several studies with concerns. Retracted studies are not in this analysis. All other studies that the team has identified are excluded here. For more details see the response section.
Detailed description of issues with Hayward, Reis, Bramante, Naggie, López-Medina, Vallejos, Beltran Gonzalez can be found in the study notes section.
Soto-Becerra is a database analysis covering anyone with ICD-10 COVID-19 codes, which includes asymptomatic PCR+ patients. Therefore many patients in the control group are likely asymptomatic with regards to SARS-CoV-2, but in the hospital for another reason. For those that had symptomatic COVID-19, there is also likely significant confounding by indication. KM curves show that the treatment groups were in more serious condition, with more than the total excess mortality at 30 days occurring on day 1. All treatments are worse than the control group at 30 days, while at the latest followup all treatments show lower mortality than control. The machine learning system used also appears over-parameterized and likely to result in significant overfitting and inaccurate results. There is also no real control group in this study - patients receiving the treatments after 48 hours were put in the control group. Authors also state that outcomes within 24 hours were excluded, however the KM curves show significant mortality at day 1 (only for the treatment groups). Several protocol violations have also been reported in this study178. Note that this study provides both 30 day mortality and weighted KM curves up to day 43 for ivermectin, we use the day 43 results as per our protocol. IVERCOR PREP reports prophylaxis results, however only very minimal details are currently available in a news report and an online presentation. Hellwig analyze African countries and COVID-19 cases in October 2020 as a function of whether widespread prophylactic use of ivermectin is used for parasitic infections. Tanioka perform a similar analysis for COVID-19 mortality in January 2021. These studies are excluded because they are not clinical trials. Shahbaznejad had only one death that occurred in a patient that was critically ill at the time of admission and died within the first 24 hours. Galan perform an RCT comparing ivermectin and other treatments with very late stage severe condition hospitalized patients, not showing significant differences between the treatments. Authors were unable to add a control arm due to ethical issues. The closest control comparison we could find is Baqui et al., which shows 43% hospital mortality in the northern region of Brazil where the study was performed, from which we can estimate the mortality with ivermectin in this study as 47% lower, RR 0.53. Further, the study is restricted to more severe cases, hence the expected mortality, and therefore the benefit of treatment, may be higher. Kishoria restrict inclusion to patients that did not respond to standard treatment, provide no details on the time of the discharge status, and there are very large unadjusted differences in the groups, with over twice as many patients in the ivermectin group with age >40, and all patients over 60 in the ivermectin group. Angkasekwinai does not make sense as reported, for details see187.
Summarizing, the studies excluded are as follows, and the resulting forest plot is shown in Fig. 20. The supplementary data shows results after restrictions and exclusions.
Abbas, very minimal patient information, three different results for the recovery outcome, selective omission of the statistically significant recovery p-value, and other inconsistencies.
Ahsan, unadjusted results with no group details.
Beltran Gonzalez, major inconsistencies reported and the data is no longer available190, although the authors state that it is available, and have shared it with an anti-treatment group.
Borody, preliminary report with minimal details.
Buonfrate, significant unadjusted group differences, with 3 times as many patients in the ivermectin arms having the baseline visit in a hospital setting, and arm C having large differences in baseline gender, weight, cough, pyrexia, and anosmia, excessive dose for arm C.
Cadegiani, control group retrospectively obtained from untreated patients in the same population.
Carvallo, concern about potential data issues.
Carvallo (B), concern about potential data issues.
Carvallo (C), minimal details of groups provided.
Chachar, multiple significant issues - pending author response.
de Jesús Ascencio-Montiel, unadjusted results with alternate outcome adjusted results showing significant changes with adjustments. Excluded results: death, mechanical ventilation, hospitalization, progression.
de la Rocha, data mismatch, no response from authors.
Elavarasi, unadjusted results with no group details.
Ferreira, unadjusted results with no group details; substantial unadjusted confounding by indication likely.
Hashmi, baseline severity favors control, post-hoc outcome and SAP changes, see discussion.
Hazan (B), study uses a synthetic control arm.
Hellwig, not a typical trial, analysis of African countries that used or did not use ivermectin prophylaxis for parasitic infections.
IVERCOR PREP, minimal details provided.
Kishoria, excessive unadjusted differences between groups.
López-Medina, strong evidence of patients in the control group self-medicating, ivermectin widely used in the population at that time, and the study drug identity was concealed by using the name D11AX22.
Mikamo, very low risk group with almost no progression leaves little room for improvement, unbalanced baseline dyspnea and high symptom scores, design and post-hoc changes favor null result.
Mustafa, unadjusted results with no group details.
Qadeer, minimal baseline details provided.
Ravikirti, exclusion of patients in less severe condition, data/analysis concerns.
Reis, multiple anomalies as per detailed analysis.
Rezai, multiple critical issues, see study page.
Rezai (B), multiple critical issues, see study page.
Samajdar, minimal details provided; unadjusted results with no group details; results may be significantly affected by survey bias.
Sarojvisut, many critical issues including missing primary outcome results, non-reproducible values, and protocol violations.
Schilling, post-hoc change to exclude patients treated before high viral load, population very low risk, recovering quickly without treatment, high baseline immunity, 2.2x greater baseline antibody negative for the treatment arm.
Siripongboonsitti, data consistency issues, very low risk patients/variants with almost no progression, all patients received known effective antiviral, baseline differences.
Soto, substantial unadjusted confounding by indication likely; substantial confounding by time possible due to significant changes in SOC and treatment propensity near the start of the pandemic.
Soto-Becerra, substantial unadjusted confounding by indication likely; includes PCR+ patients that may be asymptomatic for COVID-19 but in hospital for other reasons.
Szente Fonseca, result is likely affected by collinearity across treatments in the model.
Tanioka, not a typical trial, analysis of African countries that used or did not use ivermectin prophylaxis for parasitic infections.
Thairu, significant confounding by time possible due to separation of groups in different time periods.
Zubair, substantial unadjusted confounding by indication likely; unadjusted results with no group details.
c19early.org
October 2026

Ivermectin COVID-19 studies after exclusions

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96hosp.0/602/5614mgOT1 CT2 Espitia-Hernandez70%0.30 · 0.16–0.55recov. time28 (n)7 (n)12mgCT2 Mahmud (DB RCT)86%0.14 · 0.01–2.75death0/1833/18312mgCT2 Ahmed (DB RCT)85%0.15 · 0.01–2.70symptoms0/173/1948mg Chaccour (DB RCT)96%0.04 · 0.00–1.01symptoms12 (n)12 (n)28mgSAINT Ghauri92%0.08 · 0.01–0.88no recov.0/377/5348mg Babalola (DB RCT)64%0.36 · 0.10–1.27viral+40 (n)20 (n)24mgOT1 Bukhari (RCT)82%0.18 · 0.07–0.46viral+4/4125/4512mg Mohan (DB RCT)62%0.38 · 0.08–1.75no recov.2/406/4528mgRIVET-COV Biber (DB RCT)70%0.30 · 0.03–2.76hosp.1/473/4236mg Elalfy87%0.13 · 0.06–0.27viral+7/6244/5136mgCT2 Chahla (CLUS. RCT)87%0.13 · 0.03–0.54no disch.2/11020/14424mg Mourya89%0.11 · 0.05–0.25viral+5/5047/5048mg Loue (QR)70%0.30 · 0.04–2.20death1/105/1514mg Merino (QR)74%0.26 · 0.11–0.57hosp.77,381 (all patients)24mgcensored, see notes CS3 Faisal (RCT)68%0.32 · 0.14–0.72no recov.6/5019/5048mg Aref (RCT)63%0.37 · 0.22–0.61recov. time57 (n)57 (n)n/a Krolewiecki (RCT)−152%2.52 · 0.11–58.11ventilation1/270/14168mg Vallejos (DB RCT)−33%1.33 · 0.30–5.72death4/2503/25124mg Mayer55%0.45 · 0.32–0.63death3,266 (n)17,966 (n)151mg de Jesús Ascenci..59%0.41 · 0.36–0.47death/hosp.7,898 (n)20,150 (n)12mgCT2 Manomai.. (DB RCT)43%0.57 · 0.20–1.46no recov.3/366/3648mg Mirahma.. (DB RCT)67%0.33 · 0.03–3.14ventilation1/1313/13024mg Bramante (DB RCT)−197%2.97 · 0.12–72.71death1/4080/39690mgCOVID-OUTOT1 Wijewic.. (DB RCT)−196%2.96 · 0.12–71.99death1/1270/12296mg Mouawia (RCT)56%0.44 · 0.14–1.37hosp.4/639/6312mg
Early treatment68%0.32 · 0.25–0.4043/13,050205/39,97768% lower risk
Tau² = 0.08, I² = 35.3%, p < 0.0001
Gorial71%0.29 · 0.01–5.76death0/162/7114mg Podder (RCT)16%0.84 · 0.55–1.12recov. time32 (n)30 (n)14mg Khan87%0.13 · 0.02–1.00death1/1159/13312mg Rajter (PSM)46%0.54 · 0.27–0.99death13/9824/9814mg Hashim (SB RCT)92%0.08 · 0.00–1.44death0/596/7028mgCT2 Camprubí40%0.60 · 0.18–2.01ventilation3/135/1314mg Spoorthi21%0.79 · 0.64–0.98recov. time50 (n)50 (n)n/aCT2 Budhiraja99%0.01 · 0.00–0.15death0/34103/942n/a Ravikirti (DB RCT)89%0.11 · 0.01–2.05death0/554/5724mg Okumuş (DB RCT)33%0.67 · 0.27–1.64death6/309/3056mg Shahbazn.. (DB RCT)−197%2.97 · 0.13–70.51death1/350/3414mg Lima-Morales78%0.22 · 0.12–0.41death15/48152/28712mgCT2 Pott-Junior (RCT)85%0.15 · 0.01–1.93ventilation1/271/414mgcensored, see notes Huvemek (DB RCT)32%0.68 · 0.38–1.23no improv.13/5019/5084mg Abd-Elsalam (RCT)25%0.75 · 0.17–3.06death3/824/8236mg Rezk80%0.20 · 0.01–4.13death0/1602/16072mg Lim (RCT)69%0.31 · 0.09–1.11death3/24110/249112mgI-TECH Ozer75%0.25 · 0.06–1.13death2/608/6028mg Jamir (ICU)−53%1.53 · 0.88–2.67death32/7669/190n/aICU patients Efimenko (PSM)69%0.31 · 0.20–0.48death1,072 (n)40,536 (n)n/aself-censored, see notes OT1 George (RCT)30%0.70 · 0.25–1.93death5/358/3924mg Naggie (DB RCT)−202%3.02 · 0.12–74.08death1/7080/724168mgACTIV-6 Aref (RCT)74%0.26 · 0.12–0.55recov. time49 (n)47 (n)n/aLONG COVID Ochoa-Ja.. (DB RCT)57%0.43 · 0.07–2.50death2/374/3828mg Munir48%0.52 · 0.22–1.21death92 (n)908 (n)n/a Llenas-García17%0.83 · 0.38–1.84death10/9612/9614mg Wada (DB RCT)19%0.81 · 0.44–1.38progression19/10623/10614mg Osati32%0.68 · 0.45–0.92death448 (n)849 (n)n/a Hayward (RCT)−1%1.01 · 0.61–1.68death/hosp.34/2,15727/1,80673mgPRINCIPLE Varnaseri (DB RCT)82%0.18 · 0.04–0.78ventilation2/5511/5584mg
Late treatment50%0.50 · 0.38–0.65166/6,569412/47,81450% lower risk
Tau² = 0.27, I² = 67.2%, p < 0.0001
Shouman (RCT)91%0.09 · 0.03–0.23symp. case15/20359/10136mg Behera54%0.46 · 0.29–0.71cases41/117145/25542mg Bernigaud99%0.01 · 0.00–0.10death0/69150/3,06284mg Alam91%0.09 · 0.04–0.25cases4/5844/6012mg Chahla (RCT)95%0.05 · 0.00–0.80m/s case0/11710/11748mgCT2 Behera83%0.17 · 0.12–0.23cases45/2,199133/1,14742mg Seet (CLUS. RCT)50%0.50 · 0.33–0.76symp. case32/61764/61912mgOT1 Morgenstern (PSM)80%0.20 · 0.01–4.15hosp.0/2712/27156mg Mondal88%0.12 · 0.01–0.55symp. case128 (n)1,342 (n)n/a Kerr (PSM)70%0.30 · 0.19–0.46death25/3,03479/3,03456mg Desort-H.. (DB RCT)96%0.04 · 0.02–0.11high v. load4/20099/199203mgSAIVE Wagstaff (DB RCT)55%0.45 · 0.22–1.00DAFS36 (n)32 (n)
Prophylaxis83%0.17 · 0.10–0.29166/7,049785/10,23983% lower risk
Tau² = 0.53, I² = 86.4%, p < 0.0001
All studies66%0.34 · 0.28–0.42375/26,6681,402/98,03066% lower risk
Tau² = 0.33, I² = 76.6%, p < 0.000100.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
3 CS: censored, see details
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← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 20. Random-effects meta-analysis excluding studies with significant issues. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below.
Fed vs. Fasting Administration
Ivermectin is highly lipophilic and should be administered with a high-fat meal to maximize absorption. Guzzo et al. show that the plasma concentration of ivermectin is much higher when administered with food, with a geometric mean AUC 2.6 times higher (Fig. 21). Many ivermectin studies specify fasting, and other studies do not specify administration. Fasting administration is expected to significantly reduce efficacy for COVID-19 due to lower plasma and tissue concentrations. Fasting administration may result in insufficient distribution to specific tissues such as the lung, resulting in no efficacy. Note that COVID-19 use is very different to anthelmintic use in the gastrointestinal tract where fasting is recommended and distribution to other tissues is not required.
Concentration (ng/mL)Time (hr)FastedFed0102030405060025020015010050
Fig. 21. Ivermectin plasma concentration is significantly higher when administered with a meal. The graph shows mean plasma concentration profiles following single 30mg oral doses for fed and fasted administration, from Guzzo et al.
Fig. 22 shows random-effects meta-analysis for studies that specify fed vs. fasting administration. Studies specifying fed administration show 3.1 times higher efficacy, p = 0.03. Ideally, we could further compare high-fat vs. low-fat meals, however most studies do not specify or provide details of the fat content of the meals.
c19early.org
October 2026

Ivermectin COVID-19 fasting vs. fed studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (4d)Relative Risk
Chowdhury (RCT)81%0.19 · 0.01–3.96hosp.0/602/5614mgOT1 CT2 Biber (DB RCT)70%0.30 · 0.03–2.76hosp.1/473/4236mg López-Me.. (DB RCT)67%0.33 · 0.01–8.11death0/2001/19884mg Huvemek (DB RCT)32%0.68 · 0.38–1.23no improv.13/5019/5084mg Chahla (CLUS. RCT)87%0.13 · 0.03–0.54no disch.2/11020/14424mg Reis (DB RCT)12%0.88 · 0.49–1.55death21/67924/67984mgTOGETHERimpossible data, see notes Buonfrate (DB RCT)−211%3.11 · 0.13–73.31hosp.1/280/31336mgCOVER Abbas (DB RCT)−4%1.04 · 0.07–16.41death1/991/10384mg Manomai.. (DB RCT)43%0.57 · 0.20–1.46no recov.3/366/3648mg de la Ro.. (DB RCT)−187%2.87 · 0.12–67.49misc.1/300/2636mg Naggie (DB RCT)−202%3.02 · 0.12–74.08death1/7080/724168mgACTIV-6 Bramante (DB RCT)−197%2.97 · 0.12–72.71death1/4080/39690mgCOVID-OUTOT1 Mikamo (DB RCT)−205%3.05 · 0.12–74.69progression1/5020/52763mgIVERMILCO Wada (DB RCT)19%0.81 · 0.44–1.38progression19/10623/10614mg Hayward (RCT)−1%1.01 · 0.61–1.68death/hosp.34/2,15727/1,80673mgPRINCIPLE
Fasting21%0.79 · 0.61–1.0199/5,220126/4,92421% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.061
Elalfy87%0.13 · 0.06–0.27viral+7/6244/5136mgCT2 Krolewiecki (RCT)−152%2.52 · 0.11–58.11ventilation1/270/14168mg Mayer55%0.45 · 0.32–0.63death3,266 (n)17,966 (n)151mg Lim (RCT)69%0.31 · 0.09–1.11death3/24110/249112mgI-TECH Schilling (RCT)67%0.33 · 0.01–7.97hosp.0/451/45168mgPLATCOV Siripongbo.. (RCT)0%1.00 · 0.07–15.26progression1/301/30168mgFINCOVCT2
Fed67%0.33 · 0.16–0.6912/3,67156/18,35567% lower risk
Tau² = 0.36, I² = 57.2%, p = 0.0034
All studies43%0.57 · 0.39–0.81111/8,891182/23,27943% lower risk
00.511.52+
1 OT: ivermectin vs. other treatment
2 CT: study uses combined treatment
Rotate screen for more detailsIncrease width for more details
← Ivermectin
reduces riskIvermectin
increases risk →
Fig. 22. Random-effects meta-analysis for studies that specify fed vs. fasting administration. Studies specifying fed administration show 3.1 times higher efficacy, p = 0.03.
Many studies do not specify administration, however we can classify these into outpatient vs. hospitalized administration. When unspecified, many hospital systems will default to the labeled instructions for anthelmintic use, which is fasting; however lower-resource hospitals globally may not have the same systems and processes, patients on enteral nutrition may not have feeding suspended, and some trials may use fed administration without reporting it. In contrast, for unspecified outpatient use, patients are more likely to take their medication near meals, while some medication may be delivered with standard fasting instructions which some patients will diligently follow. Therefore, we can group trials into four categories with an increasing percentage of fed administration: a) trials specifying fasting administration, b) unspecified hospitalized administration, c) unspecified outpatient administration, and d) trials specifying fed administration. Assigning these ordinal numbers 1 to 4, random-effects meta-regression shows a significant trend with increasing efficacy for higher percentages of fed administration. For every step between the groups, meta-regression shows that the Risk Ratio decreases by a factor of 1.35 (slope β = -0.30 [-0.49 to -0.10]; p = 0.0026).
These results reinforce the reliability of the overall finding that ivermectin reduces risk for COVID-19. If the observed efficacy was due to a systematic bias increasing efficacy in results, we would not expect to find a significant trend across administration that matches the biological mechanisms.
There is strong rationale for administration with a high-fat meal to have greater benefits than what is indicated by the higher plasma concentration seen in studies. Consider the following comparison:
•Empty stomach: the smaller amount of absorbed drug enters the portal vein, binding primarily to albumin. This blood flows directly to the liver, subjecting the drug to hepatic first-pass metabolism before it reaches the rest of the body or lungs.
•With a high-fat meal: dietary fat triggers the intestine to package absorbed lipids, and the lipophilic ivermectin dissolved within them, into chylomicrons (large lipoprotein particles). Because chylomicrons are too large for the portal vein capillaries, they go into the lymphatic system: intestinal lymphatics → thoracic duct → left subclavian vein. The subclavian vein empties into the heart, which pumps the blood immediately to the lungs. This physical bypass of the liver allows the drug to hit the lung tissue first.
This is simplified and studies showing the degree different routes are used have not been done. Some additional notes supporting this route:
•Solubility: while solubilization aids initial entry into the intestinal cell, the presence of long-chain triglycerides biologically requires the cell to repackage that fat into chylomicrons. Ivermectin's high logP (~5.8) suggests partitioning into this chylomicron core, and use of the lymphatic route regardless of solubility alone.
•Lipoprotein affinity: rabbit studies showing correlations between lipid levels and drug concentration (even via IV) confirm ivermectin's strong affinity for lipoproteins. This supports the mechanism: if the drug avidly binds lipoproteins in the blood (IV), it will also bind the nascent lipoproteins (chylomicrons) formed during digestion, which are the specific vehicles that travel the lymphatic pathway.
•Flow vs. density: while adipose tissue has a higher density of Lipoprotein Lipase (LPL), the lungs receive 100% of the cardiac output. Every chylomicron passes through the pulmonary capillary bed first. This exposure creates a sieve-like effect, allowing pulmonary LPL to act on the drug payload before it reaches peripheral fat.
•Mechanism of release (supersaturation): as pulmonary LPL hydrolyzes the chylomicron to extract fuel, the particle's core shrinks. This creates a state of supersaturation for the drug cargo. Thermodynamic principles suggest that the drug must leave the shrinking oil droplet and partition into the nearest lipophilic membrane, in this case the lung endothelium, resulting in high local tissue concentration.
Efficacy by Treatment Delay
Fig. 23 shows a mixed-effects meta-regression of efficacy as a function of treatment delay in COVID-19 ivermectin studies, showing that efficacy declines significantly with treatment delay (p = 0.0038). Early treatment is critical. This significant efficacy gradient reinforces the reliability of the overall finding that ivermectin reduces risk for COVID-19. If the observed efficacy was due to a systematic bias increasing efficacy in results, we would not expect to find a significant gradient in efficacy as a function of treatment delay that matches the biological mechanisms.
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Fig. 23. Early treatment is more effective (p = 0.0038). Meta-regression showing efficacy as a function of treatment delay in COVID-19 ivermectin studies.
For comparison, Fig. 24 shows a meta-regression for all studies providing specific values across 227 treatments. Efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Fig. 24. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.
Efficacy Gradient By Stage
Preclinical studies show mechanisms relevant to all stages: blocking initial infection, minimizing viral replication, and mitigating secondary complications. Specifically, for blocking initial infection: spike-ACE2 disruption59, lipid raft attachment inhibition76, glycan binding60,218, and CD147 inhibition72-75; for minimizing viral replication: importin-α/β nuclear transport blockade47,49,51,58, 3CLpro inhibition66, and RdRp interference67; and for mitigating secondary complications: inhibiting fibrin clots65, reducing lung fibrosis63,79,80, protecting against myocarditis69,81, and dampening cytokine storm77,78.
If there are beneficial mechanisms relevant to all stages, we would expect to see benefits combine and increase for earlier treatment, and again for prophylactic treatment—as treatment is delayed the benefit of specific mechanisms is reduced or lost. Random-effects meta-regression shows a significant association with increasing efficacy for early treatment and prophylaxis. For every step towards earlier intervention (late treatment → early treatment → prophylaxis), meta-regression shows that the Risk Ratio decreases by a factor of 1.79 (slope β = -0.58 [-0.78 to -0.38]; p < 0.0000001). In other words, the protective effect becomes stronger as intervention is done earlier.
This gradient reinforces the reliability of the overall finding that ivermectin reduces risk for COVID-19. If the observed efficacy was due to a systematic bias increasing efficacy in results, we would not expect to find a trend across stages that matches the biological mechanisms.
Interactions Matching Biological Mechanisms
With 106 studies for ivermectin, we can analyze interactions and trends across studies and groups. Significant interactions that match the biological mechanisms provide further confirmation of efficacy. We find significant interactions for: meta-regression of efficacy vs. treatment delay (p = 0.0038), efficacy gradient across stages (p < 0.0000001), and fed vs. fasting administration (p = 0.03).
This reinforces the reliability of the overall finding that ivermectin reduces risk for COVID-19. If the observed efficacy was due to a systematic bias increasing efficacy in results, we would not expect analysis across studies to find these significant interactions that match the biological mechanisms. These results would not happen without a real treatment effect - it would require an unrealistic conspiracy where many well-respected and independent teams in many countries conspire to harm humanity and coordinate fraudulent results.
c19early.org
Results vary as expected across studies—only possible with a real treatment effect
While meta-analysis shows very strong confidence in efficacy, emergent results across studies that match biological mechanisms provides additional confirmation.
Treatment delay Meta-regression of efficacy as a function of treatment delay matches expectations (p = 0.0038).
Gradient across stages Preclinical studies show mechanisms relevant to all stages: blocking initial infection, minimizing viral replication, and mitigating secondary complications; therefore we expect benefits to combine and increase for earlier treatment, and again for prophylaxis—as intervention is delayed the benefit of specific mechanisms is reduced or lost. Meta-regression confirms this relationship with p < 0.0000001. (Specifically, for blocking initial infection: spike-ACE2 disruption59, lipid raft attachment inhibition76, glycan binding60,218, and CD147 inhibition72-75; for minimizing viral replication: importin-α/β nuclear transport blockade47,49,51,58, 3CLpro inhibition66, and RdRp interference67; and for mitigating secondary complications: inhibiting fibrin clots65, reducing lung fibrosis63,79,80, protecting against myocarditis69,81, and dampening cytokine storm77,78).
Fed vs. fasting Administration with a high-fat meal is expected to significantly increase bioavailability, but also support distribution to the lung, bypassing hepatic first-pass metabolism. Studies specifying fed vs. fasting administration show 3.1 times greater efficacy, interaction p = 0.03).
Media Censorship
Low-cost treatments were subject to bias and censorship during the pandemic. Scientific bias is seen in the design, analysis, presentation, and selective reporting of studies, which often favored negative results. A similar bias is seen in the media coverage for low-cost treatments. While broadly seen, bias was particularly notable for ivermectin and hydroxychloroquine, e.g., Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."91.
We analyze media coverage for the 227 treatments we cover using Altmetric219, which reports the number of ~12,000 tracked news outlets that covered each study220. Studies are considered to have received significant media coverage if they were covered by at least 0.5% of the tracked news outlets. Fig. 25, 26, 27, and 28 show the bias toward negative results for low-cost treatments, in contrast to the opposite bias for high-profit treatments. Fig. 29 shows the bias toward coverage of negative results for ivermectin. This may result in widespread incorrect perceptions on the relative efficacy of high-profit and low-cost treatments. The impact is significant—increased cost limits the use of high-profit treatments and treatment equity, and high-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.
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Fig. 25. Media was more likely to cover negative or inconclusive results for low-cost treatments, and positive results for high-profit treatments.
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Fig. 26. Comparison of media bias for different treatments.
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Fig. 27. Mainstream media was biased against positive results for low-cost treatments.
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Fig. 28. In contrast to the results for low-cost treatments, mainstream media was biased towards positive results for high-cost treatments.
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Fig. 29. Mainstream media was biased against positive results for ivermectin.
A combination of factors may have led to the media's suppression of low-cost treatments:
•
Politicization
led to a media environment where coverage was often framed to support a political narrative rather than to provide objective scientific information. As Scott Alexander said:
"if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."
There was strong social pressure to discredit low-cost treatments.
•
Censorship
of information conflicting with selected authorities. For example, individuals and organizations presenting conflicting science were often banned on Twitter and YouTube.
•
FDA requires "no adequate, approved, and available alternatives"
in order to grant an EUA for novel high-profit interventions, creating a strong incentive for authorities to ignore or downplay existing low-cost treatments.
•
Regulatory capture
biases authorities towards high-profit interventions.
•
Authorities ignored most evidence for low-cost treatments
, for example the NIH references only 2% of studies in delayed, rarely-updated, biased commentaries with no quantitive analysis.
•
Media coverage of science is often not very accurate
, e.g., misunderstanding confounding issues. For example the media widely considered the RECOVERY HCQ RCT to be conclusive on efficacy, but very late treatment of late stage patients (mostly on oxygen already) with an excessive toxic dose (shown dangerous in a dose comparison RCT) provides no information on the recommended early/prophylactic treatment. With difficulting in understanding basic confounders like treatment delay and dose, the media may favor deferring to authorities. Many studies for low-cost treatments require greater expertise to analyze. Relatively few journalists have a strong ability to analyze clinical trials and are outnumbered by the rest.
•
Substantial funding from pharmaceutical advertising
biases editorial decisions towards high-profit interventions.
•
PR power
- companies/teams with strong PR presence are favored in the media, which correlates with high-profit and high conflict of interest studies.
•
The media was very negative in general
, inflating risk, fear, and anxieties. A negative bias may improve ratings and revenue, increasing motivation to continue watching coverage. A combination of low-cost treatments greatly reducing risk conflicts with the negative narrative.
Erasing Efficacy
❝
[Cochrane] has degenerated into a politically expedient organisation that doesn't care much about the trustworthiness of the science or the public…
Peter C. Gøtzsche, Cochrane co-founder221
For HCQ, meta-analysis including late stage / excessive dose trials—where results are poor and trials were large—hides positive results for early treatment and prophylaxis222.
For ivermectin overdosing is more difficult and late treatment shows efficacy. How did Cochrane generate a null result? Their meta-analysis223,224 required unprecedented measures for a politically acceptable outcome, and has extreme COI—many authors also did the paxlovid analysis.
•Authors created a retrospective analysis excluding most studies, splitting results across multiple analyses, and disregarding efficacy if independent evidence is combined223.
•Authors pledged to update the analysis but only did so once in >5 years. The second version required voiding the protocol and using novel mechanisms to exclude even more studies, for example specific registration requirements (applied selectively)224.
•Are authors restricting to high quality trials? No, their quality evaluation is highly inaccurate, e.g., including (violating their own protocol225) and claiming low risk of bias for the Together trial, however the trial has very high actual known bias—reporting multiple impossible numbers, refusing to release data despite pledging to, breaking blinding (even externally), and having randomization failure, extreme COI, many protocol violations, and other issues98.
•Three pre-exposure prophylaxis RCTs, all showing significant efficacy, were ignored by specifying post-exposure only. In contrast, many of the same authors include pre-exposure studies for paxlovid. In 2023, an ivermectin post-exposure RCT showed efficacy with p<0.00000000001—no update has been made since 2022.
•Authors claimed updates would be made as new results were available, however they have only released two versions, both retrospective with separate protocols specified after all included results were known.
For more details see the analysis of each version223,224.
Authority Delay
25 low-cost treatments were approved in one or more countries, yet many countries approved no low-cost treatments. The countries that did adopt low-cost treatments analyzed the evidence early and made timely approvals. With few exceptions, authorities did not change their initial views, regardless of how much evidence accumulated showing either efficacy or harm. Why?
The harms of smoking here hidden for 25 yearsC. Authorities did not analyze the data in real-time, failing to act when harm was known. Widespread acknowledgement of harm came only after attempts by two new surgeon generals, along with pressure from health advocates and a new president, and a review of 7,000 studies.
Similarly for COVID-19, most authorities and experts did not proactively analyze data in real-time. This guarantees delayed recognition of efficacy or harm, by which time moral, legal, career, and reputational liabilities strongly disincentivize any admission of error. Claims of no efficacy (for effective treatments) or safety (for harmful treatments) were often made prior to strong data being available. Correction would require admitting to errors that increased mortality, which is unlikely with the same generation of officials.
Analysis of potential treatments was rarely done, and when done these were typically minimal efforts. For example, NIH reviews were highly delayed, cover only a tiny fraction of treatments, reference only 2% of studies for the treatments covered, and include no quantitative analysis. They appear as rarely updated side projects from external panels implicitly tasked with justifying prior failures. As with smoking, the thousands of studies could (and should) have been analyzed and acted on in real-time.
A key structural improvement, applicable to all current and future diseases, is for authorities to implement real-time proactive analysis of clinical evidence. This does not remove all bias, but does make it possible to act on evidence, whereas delayed action may be unlikely due to moral, legal, career, and reputational liabilities.
The US FDA recommended against ivermectin in an article stating that "The FDA has not reviewed data to support use of ivermectin in COVID-19 patients to treat or to prevent COVID-19"226. This confirms that the FDA had not reviewed the evidence at all, let alone in real-time.
c19early.org
Delayed public health acknowledgments
Official acknowledgment of efficacy or harm is often delayed—legal, career, and status risks disincentivize admission of error.
Evidence Official Acknowledgment Approx. Delay
Citrus Fruit (vitamin C) for Scurvy (effectiveness) 1747: James Lind conducted one of the first-ever controlled clinical trials, proving that oranges and lemons cured scurvy in sailors. 1795: The British Royal Navy finally made a daily ration of lemon juice a standard issue for all its sailors, effectively eliminating the disease. 48 years
Handwashing (lower mortality) 1847: Dr. Ignaz Semmelweis provided conclusive proof that having doctors wash their hands with a chlorine solution before delivering babies reduced maternal mortality rates from over 18% to around 1%. ~1870s: Semmelweis's findings were rejected and he was ridiculed. His work was only validated decades later (after his death). ~20+ years
Helicobacter pylori (bacteria causes ulcers) 1982-1984: Marshall and Warren discovered that Helicobacter pylori bacteria causes ulcers, confirmed via direct exposure. Officials maintained that ulcers were caused by stress and spicy food. 1994: The US NIH released a consensus statement officially recommending antibiotics as the standard treatment for peptic ulcers, overturning decades of acid-suppression therapy. ~12 years
Asbestos (causes asbestosis & cancer) 1924: The British Medical Journal published the first case study of a death from "asbestosis." By 1918, U.S. insurance companies had stopped selling life insurance to asbestos workers. 1971 (US): The Occupational Safety and Health Administration (OSHA) was formed and began regulating asbestos as a carcinogen, setting the first federal workplace safety standards for it. ~47 years
Leaded Gasoline (neurotoxicity) ~1924: Dangers of low-level lead exposure were known. Experts like Alice Hamilton warned the U.S. Surgeon General that adding lead to gasoline would cause widespread public poisoning. 1973 (US): The Environmental Protection Agency (EPA) ordered the first phasedown of lead in gasoline, following the Clean Air Act of 1970. A full ban for on-road vehicles took effect in 1996. ~49 years
Harms of Smoking (causes lung cancer) 1939: Franz Müller (Germany) published the first case-control epidemiological study strongly linking tobacco smoking to lung cancer. This was followed by major U.S. & U.K. studies in the 1950s. 1964 (US): The U.S. Surgeon General's report, "Smoking and Health," was released. It was the first U.S. government report to definitively link smoking to lung cancer and heart disease. 25 years
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours227,228. Baloxavir marboxil studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar et al. report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir marboxil for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases229
<24 hours-33 hours symptoms230
24-48 hours-13 hours symptoms230
Inpatients-2.5 hours to improvement231
As shown in the treatment delay section, the efficacy of COVID-19 treatment declines as a function of the treatment delay. Early treatment is critical for COVID-19.
Patient demographics
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results, for example as in López-Medina et al.
SARS-CoV-2 variants
Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants232, for example the Gamma variant shows significantly different characteristics233-236. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants237,238.
Treatment regimen
Effectiveness may depend strongly on the dosage and treatment regimen. Higher dosage has shown increased efficacy239. The method of administration is critical. Administration with a high-fat meal shows significantly higher concentrations, and may mean the difference between efficacy and no efficacy within specific tissues, as detailed in the administration section.
Medication quality
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
Other treatments
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic116,132,133,135,137,142,145,155,216,241-256, therefore efficacy may depend strongly on combined treatments.
Effect measured
Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.
Meta-analysis
The distribution of studies will alter the outcome of a meta-analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. All meta-analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta-analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
Ivermectin
Ivermectin studies vary widely in all the factors above, which makes the consistently positive results remarkable. A failure to detect an association after combining heterogeneous studies does not mean the treatment is not effective (it may only work in certain cases), however the reverse is not true — an identified association is valid, although the magnitude of the effect may be larger for more optimal cases, and lower for less optimal cases. Note that trial with a design favoring null results have become common, and are likely to dominate future trials. For example, the Together Trial tested ivermectin in locations known to have a high degree of self-medication, up to 7 days from onset (while claiming to be an early treatment trial), and using low doses compared to clinical recommendations for the dominant variant. The ACTIV-6 trial had a median treatment delay of 6 days and very low risk patients.
Pooled Effects
Pooled effects are no longer required to show efficacy as of September 2020
This section validates the use of pooled effects for COVID-19, which enables earlier detection of efficacy, however pooled effects are no longer required for ivermectin as of September 2020. Efficacy is now known based on specific outcomes for all studies and when restricted to RCTs. Efficacy based on specific outcomes was delayed by 1.3 months compared to using pooled outcomes.
Combining studies is required
For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results. Pooling the results of studies reporting different outcomes allows us to use more of the available information. Logically we should, and do, use additional information when evaluating treatments—for example dose-response and treatment delay-response relationships provide additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
Specific outcome and pooled analyses
We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.
Ethical and practical issues limit high-risk trials
Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster and safer collection of evidence.
Validating pooled outcome analysis for COVID-19
For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from all 227 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Fig. 30 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 31 shows that improved recovery is very strongly associated with lower mortality (p < 0.0000000001). Considering the extremes, Singh et al. show an association between viral clearance and hospitalization or death, with p = 0.003 after excluding one large outlier from a mutagenic treatment, and based on 44 RCTs including 52,384 patients. Fig. 32 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.0000000074 to p = 0.00000000015.
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Fig. 30. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 31. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 30. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Pooled outcomes identify efficacy 5 months faster (7 months for RCTs)
Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months. Fig. 33 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Fig. 33. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
Limitations
Pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral clearance may show no efficacy if most studies only examine viral clearance. In practice, it is rare for a non-antiviral treatment to report viral clearance and to not report clinical outcomes; and in practice other sources of heterogeneity such as differences in treatment delay are more likely to hide efficacy.
Summary
Analysis validates the use of pooled effects and shows significantly faster detection of efficacy on average. However, as with all meta-analyses, it is important to review the different studies included. We also present individual outcome analyses, which may be more informative for specific use cases.
Polytherapy
There are many complementary mechanisms of action for COVID-19 treatments, with over 500 therapeutic targets96. Studies show complementary and synergistic effects with polytherapy116,132,133,135,137,142,145,155,216,241-256. For example, Jitobaom et al.137 shows >10x reduction in IC50 with ivermectin and niclosamide, an RCT by Said et al.245 showed the combination of nigella sativa and vitamin D was more effective than either alone, and an RCT by Wannigama et al.258 showed improved results with fluvoxamine combined with additional treatments, compared to fluvoxamine alone.
SARS-CoV-2 can rapidly acquire mutations altering infectivity, disease severity, and drug resistance even without selective pressure259-266. Antigenic drift can undermine more variant-specific treatments like monoclonal antibodies and more specific antivirals. Treatment with targeted antivirals may select for escape mutations267. The efficacy of treatments varies depending on cell type268 due to differences in viral receptor expression, drug distribution and metabolism, and cell-specific mechanisms. Efficacy may also vary based on genetic variants269-279.
Variable efficacy across variants, cell types, tissues, and host genetics, along with the complementary and synergistic actions of different treatments, all point to greater efficacy with polytherapy. In many studies, the standard of care given to all patients includes other treatments—efficacy seen in these trials may rely in part on synergistic effects. Less variant specific treatments and polytherapy targeting multiple viral and host proteins may be more effective. Meta-analysis of all early treatment trials shows 71% [60‑79%] lower risk for studies using combined treatments, compared to 27% [24‑30%] for single treatments.
Polytherapy vs. monotherapy for all treatments
Monotherapy Polytherapy
Early treatment
27% [24–30%]
71% [60–79%]
Late treatment
18% [16–20%]
49% [41–55%]
Prophylaxis
28% [25–30%]
90% [48–98%]
Polytherapy (combined treatment) is more effective than monotherapy. Analysis of results across 227 treatments. p = 0.000000057, p < 0.0000000001, and p = 0.02.
While not reaching statistical significance for all stages, ivermectin studies using polytherapy also show greater efficacy than monotherapy studies.
Polytherapy vs. monotherapy for ivermectin
Monotherapy Polytherapy
Early treatment
57% [41–69%]
74% [58–84%]
Late treatment
38% [23–49%]
61% [19–82%]
Prophylaxis
81% [72–87%]
99% [88–100%]
Polytherapy (combined treatment) is more effective than monotherapy. Analysis of results for ivermectin studies. p = 0.091, p = 0.22, and p = 0.022.
c19early.org
Combined treatment is more effective
SARS-CoV-2 involves the complex interplay of 500+ host and viral proteins and factors, providing many therapeutic targets. Many compounds have antiviral activity for SARS-CoV-2, with many different mechanisms including blocking attachment, entry, and replication. Combinations of treatments, with careful attention to potential side effects, allows improved efficacy via complementary and synergistic mechanisms.
Tissue and cell coverage Different compounds have different tissue penetration profiles and efficacy across cell types
Variant coverage Compounds with different viral resistance profiles
Intra/extracellular Disabling and removing intracellular and extracellular viral particles
Resistance Minimizing persistence and emergence of resistant variants
Genetics Robustness against individual variations in efficacy based on genetics
Lower doses Potentially lower doses of individual agents, reducing toxicity
Viral and host-directed Antivirals targeting viral and host proteins
Immune system function Supporting or enhancing natural immune system function
Disease phases Addressing multiple disease phases (viral replication, inflammation, secondary complications)
Discussion
WHO
WHO updated their treatment recommendations on 3/30/2021280. For ivermectin they reported a mortality odds ratio of 0.19 [0.09-0.36] based on 7 studies with 1,419 patients. They do not specify which trials they included. The report is inconsistent, with a forest plot that only shows 4 studies with mortality results. WHO's recommendation has not been updated for days.
Despite this extremely positive result, they recommended only using ivermectin in clinical trials. The analysis contains many flaws281:
•Of the 106 studies (54 RCTs), they only included 16.
•They excluded all 18 prophylaxis studies (5 RCTs).
•There was no protocol for data exclusion.
•Trials included in the original UNITAID search protocol were excluded.
•They excluded all epidemiological evidence, although WHO has considered such evidence in the past.
•They combine early treatment and late treatment studies and do not provide heterogeneity information. As above, early treatment is more successful, so pooling late treatment studies will obscure the effectiveness of early treatment. They chose not to do subgroup analysis by disease severity across trials, although treatment delay is clearly a critical factor in COVID-19 treatment, the analysis is easily done (as above), and it is well known that the studies for ivermectin and many other treatments clearly show greater effectiveness for early treatment.
•WHO downgraded the quality of trials compared to the UNITAID systematic review team and a separate international expert guideline group that has long worked with the WHO5.
•They disregarded their own guidelines that stipulate quality assessments should be upgraded when there is evidence of a large magnitude effect (which there is), and when there is evidence of a dose-response relationship (which there is). They claim there is no dose-response relationship, while the UNITAID systematic review team found a clear relationship, along with individual studies239.
•Their risk of bias assessments do not match the actual risk of bias in studies. For example they classify López-Medina as low risk of bias, however this study has many issues making the results unreliable282, even prompting an open letter from over 170 physicians concluding that the study is fatally flawed283. Beltran Gonzalez is also classified as low risk of bias, but is a study with very late stage severe condition high-comorbidity patients. There is a clear treatment delay-response relationship and very late stage treatment is not expected to be as effective as early treatment. Conversely, much higher quality studies were classified as high risk of bias.
•Although WHO's analysis is called a "living guideline", it is rarely updated and very out of date. As of May 14, 2021, four of the missing RCTs are known to WHO and labeled "RCTs pending data extraction"284. We added these 4, 4, 2, and one month earlier.
•A single person served as Methods Chair, member of the Guidance Support Collaboraton Committee, and member of the Living Systematic Review/NMA team.
•Public statements from people involved in the analysis suggest substantial bias. For example, a co-chair reportedly said that "the data available was sparse and likely based on chance"285. The clinical team lead refers to their analysis of ivermectin as "fighting this overuse of unproven therapies ... without evidence of efficacy"285, despite the extensive evidence of efficacy from the 106 studies by 1,299 scientists with 220,588 patients. People involved may be more favorable to late stage treatment of COVID-19, for example the co-chair recommended treating severe COVID-19 with remdesivir286.
In summary, although WHO's analysis predicts that over 2 million fewer people would be dead if ivermectin was used from early in the pandemic, they recommend against use outside trials. This appears to be based primarily on excluding the majority of the evidence, and by assigning bias estimates that do not match the actual risk of bias in studies.
Use early in the pandemic was proposed by Kitasato University including the co-discoverer of ivermectin, Dr. Satoshi Ōmura. They requested Merck conduct clinical trials of ivermectin for COVID-19 in Japan, because Merck has priority to submit an application for an expansion of ivermectinʼs indications. Merck declined287.
Merck
Merck has recommended against ivermectin288, however this recommendation has not been updated for days.
They stated that there is "no scientific basis for a potential therapeutic effect against COVID-19 from pre-clinical studies". This is contradicted numerous studies, including Jitobaom, Li, Fauquet, Boschi, Shahin, Abd-Elmawla, Ma, Vottero, Mody, Parvez, Li (B), Gao, Bagheri-Far, Lefebvre, Zhang, DiNicolantonio, Zhao, Zaher, Liu (B), Liu (C), Gayozo, Kofler, Munson, Yan, Haque, de Oliveira Só, Agamah, Oranu, Chellasamy, Umar, Alvarado, Aminpour, Parvez (B), Francés-Monerris, González-Paz, González-Paz (B), Rana, Muthusamy, Qureshi, Schöning, Bello, Udofia, Choudhury, Kern, Saha (B), Eweas, Francés-Monerris (B), Kalhor, Swargiary, Maurya, Lehrer, Suravajhala, How, Lew, García-Aguilar, De Forni, Saha (C), Jitobaom (B), Croci, Zheng, Delandre, Segatori, Jitobaom (C), Mountain Valley MD, Yesilbag, Jeffreys, Surnar, Caly, Uematsu, Albariqi, Errecalde, Madrid, de Melo, Arévalo, Chaccour, Zatloukal, Zaidi, Wehbe, Kalfas, Jans, Heidary.
They state that there is "no meaningful evidence for clinical activity or clinical efficacy in patients with COVID-19 disease". This is contradicted by numerous studies including de Jesús Ascencio-Montiel, Babalola, Chowdhury, Espitia-Hernandez, Mahmud, Chaccour (B), Ghauri, Bukhari, Mohan, Elalfy, Chahla, Mourya, Loue, Merino, Faisal, Aref, Mayer, Manomaipiboon, Khan, Hashim, Budhiraja, Ravikirti (B), Okumuş, Lima-Morales, Huvemek, Baguma, Shimizu, Aref (B), Varnaseri, Behera, Bernigaud, Alam, Chahla (B), Behera (B), Seet, Morgenstern, Mondal, Kerr.
They also claim that there is "a concerning lack of safety data in the majority of studies". Safety analysis is found in Guzzo, Kory, Descotes, Errecalde, Madrid, and safety data can be found in most studies, including López-Medina, Vallejos, Shahbaznejad, Kishoria, Carvallo (C), Hazan (B), Szente Fonseca, Zubair, Babalola, Chowdhury, Espitia-Hernandez, Mahmud, Chaccour (B), Ghauri, Bukhari, Mohan, Elalfy, Mourya, Loue, Aref, Khan, Okumuş, Lima-Morales, Huvemek, Chahla (B), Behera (B), Seet, Morgenstern, Ahmed, Biber, Krolewiecki, Gorial, Camprubí, Spoorthi, Pott-Junior, Abd-Elsalam, George, Shouman (B), Bhattacharya.
Merck has a number of conflicts of interest:
•Merck has committed to give ivermectin away for free "as much as needed, for as long as needed" in the Mectizan® Donation Program342, to help eliminate river blindness.
•Merck has their own new COVID-19 treatments MK-7110 (formerly CD24Fc)343 and Molnupiravir (MK-4482)344,345. Merck has a ~$US1.2B agreement to supply molnupiravir to the US government, if it receives EUA or approval346. Over $US10B in near-term orders are expected if approved347.
•Ivermectin is off-patent, there are many manufacturers, and Merck is unlikely to be able to compete with low-cost manufacturers.
•Promoting the use of low-cost off-patent medications compared to new products may be undesirable to some shareholders.
•Japan requested Merck conduct clinical trials early in the pandemic and they declined. Merck may be reluctant to admit this mistake287.
For other concerns regarding Merck's statement and prior actions related to Vioxx, see Scheim (E).
FDA
The US FDA recommended against ivermectin on March 5, 2021, however they stated that "The FDA has not reviewed data to support use of ivermectin in COVID-19 patients to treat or to prevent COVID-19". There is still no indication that the FDA has reviewed the clinical trials days later.
The FDA notes that they "received multiple reports of patients who have required medical support and been hospitalized after self-medicating with ivermectin intended for horses". The number of reports was 4349. For comparison, acetaminophen overdose results in ~33,000 yearly hospitalizations in the USA (~12,000 unintentional)350. The FDA's recommendation may increase cases of self-medication with animal ivermectin, because it reduces the percentage of prescribing physicians.
They state that "Ivermectin is not an anti-viral", however many studies contradict this Caly, Buonfrate, Qadeer, Thairu, Babalola, Chowdhury, Espitia-Hernandez, Mahmud, Bukhari, Mohan, Elalfy, Mourya, Aref, Khan, Okumuş, Ahmed, Biber, Wijewickrema, Mouawia, Rezk, including 12 RCTs.
They note that "some initial research is underway", however there had been many studies completed and published prior to the FDA recommendation: López-Medina, Beltran Gonzalez, Hellwig, Cadegiani, Carvallo (C), Chachar, Babalola, Chowdhury, Espitia-Hernandez, Mahmud, Chaccour (B), Ghauri, Bukhari, Mohan, Elalfy, Khan, Hashim, Budhiraja, Ravikirti (B), Okumuş, Lima-Morales, Behera, Bernigaud, Alam, Chahla (B), Ahmed, Biber, Gorial, Camprubí, Spoorthi, Shouman (B), Podder, Rajter, including 17 RCTs.
Sep 3, 2021: The FDA revised their statement slightly. They removed the false claim that invermectin is not an antiviral, and they removed the statement that they have not reviewed the data. However, there is still nothing to indicate that they have reviewed the clinical trials. Indeed, they state "currently available data do not show ivermectin is effective against COVID-19" and "ivermectin has not been shown to be safe or effective for these indications", which are both false.
NIH
Update: NIH has updated the recommendation, based heavily on the Together Trial, while making no mention of the impossible data, blinding, randomization, and protocol failures, or that the co-principal investigator privately reported that "There is a clear signal that IVM works in COVID patients".
NIH has reported that there is insufficient evidence to recommended for or against ivermectin356. A table with summaries of 7 studies is provided, dated Dec 16, 2021, and they reference another 23 studies without analysis, however there are 106 studies to date. No quantitative analysis is provided. The NIH recommendation is "insufficient evidence", indicating that they must review new evidence immediately. Lack of updates suggest bias.
The likely members of the panel have been revealed by FOIA requests357. In the first request, all but two member names were redacted358, however all are visible in a second request359. Major conflicts of interest have been reported357,360. 7 of 9 panel members appear to have conflicts of interest. Submit Corrections or Updates
Prof. Adaora Adimora
•Merck: advisory board, consultant, research support361,362
•Gilead (maker of remdesivir): consultant, research support363,364 "Aadimora AA has received consulting fees from Viiv, and Gilead and her institution has received funding from Gilead for her research"
•Open Payments shows $62,000 from Merck and $88,000 from Gilead365
Prof. Roger Bedimo
•Merck: advisory board361,362
•Gilead: honoraria364
•Open Payments shows $149,000 from Merck, $76,000 from Sanofi, and $23,000 from Gilead366
Prof. Rajesh Gandhi
•Gilead: grants, advisory board, personal fees367,368 "Dr. Gandhi reports grants and personal fees from Gilead, personal fees from Merck, grants and personal fees from Theratechnologies, grants from ViiV, grants from Janssen."
•Merck: advisory board and personal fees364,367,368
•Janssen: grants367
•Open Payments shows $45,000 from Merck and $10,000 from Gilead369
Prof. David Glidden
•Merck: advisory board361,362
•Gilead: consultant361,364
Prof. Roy Gulick
•Merck: grants370 (deleted from current version371) "Roy M. Gulick, MD, MPH, has disclosed that he has received grants for clinical research from Abbott, Boehringer Ingelheim, Merck, Pfizer, Schering, and Tibotec, and has received grants for educational activities from Gilead and Monogram. Dr. Gulick has also disclosed that he has served as an ad-hoc advisor or consultant to Abbott, Boehringer Ingelheim, Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Pfizer, Schering, and Tibotec", principal investigator on A5391 with funding in part from Merck372
•Pfizer: grants and ad-hoc advisor or consultant370 (deleted from current version371)
•Gilead: grants and ad-hoc advisor or consultant370 (deleted from current version371)
•GlaxoSmithKline: ad-hoc advisor or consultant370 (deleted from current version371)
•NIAID/NIH: grants373,374
Prof. Susanna Naggie
•Gilead: grants, travel/meeting support361,364,368
•AbbVie, NIH: grants368
•Vir Biotechnology: advisory board, stockholder361
•Ivermectin trial grant: $155 million grant after the insufficient evidence recommendation362
•Open Payments shows $2.4 million from AbbVie, $1.2 million from Gilead, $34,000 from Janssen, and $19,000 from Merck375
Prof. Andrew Pavia
•Merck: consultant364,368
•Genentech: consultant364
•GlaxoSmithKline: consultant368
•Open Payments shows $14,000 from Pfizer, $5,000 from Merck, and $4,000 from Janssen376
Strongyloides
See also
Do Strongyloides Worms Explain Positive Ivermectin Trials?
Strongyloides Hypothesis: Summary Conclusions and What's Next
Did Use Of Ivermectin In Latin America Sabotage Clinical Trials and Confuse The World Of Medicine?
One theory for the beneficial effect of ivermectin for COVID-19 is related to strongyloides and the use of steroids — control group patients with strongyloides may be at risk due to steroid use, while ivermectin patients are protected. While this mechanism may contribute to efficacy in some cases, it is inconsistent with the data. If this was the case, we would expect to see greater benefit in late stage trials where steroids are used more often, and we would expect to see greater benefit for outcomes that occur after steroids are used. However, we see a very strong opposite effect for treatment time, and we see comparable or stronger efficacy for earlier outcomes.
The theory has gained renewed interest based on a new analysis by Bitterman et al. However, this analysis is confounded by treatment delay, dose, conflicts of interest, and other factors, and the effect disappears when analyzing all studies, all RCTs, or all mortality results, as shown in Fig. 34.
Although the first author has responded to the confounders on Twitter, we do not see mention of them in the paper. Author is also aware that the larger sets of all trials, all RCTs, or all mortality results do not show the effect, however we also do not see this mentioned in the paper. These omissions suggest investigator bias. Author claims they could not discuss these issues due to publication delays, however the paper was accepted Jan 31, 2022, and author was aware of the issues months before, for example discussing treatment delay and dose in Nov 2021. These confounders are also basic and not really possible to miss.
The meta-analysis for Hashim includes critical patients, however these patients were always allocated to the treatment arm for ethical reasons, therefore including them is not logical and introduces substantial bias. According to the author response, this appears to have been known, suggesting investigator bias. Authors include Shahbaznejad where the only death was a critical patient that died within 24 hours of admission.
Although authors note following PRISMA guidelines, we do not see registration of the protocol or discussion thereof. We note that the current protocol is the result of multiple changes to the original methodology as posted on Twitter: from 3 groups to 2 groups, altering the included studies, and switching from using one source for prevalence estimates to selecting estimate sources on a per study basis, which allows potential bias in the selection. Notably, this resulted in moving the Together Trial (Brazil) into the low prevalence category.
Author's results rely on trials with a very small number of mortality events — the high stronglyoides prevalance group has trials with 1, 3, 4, and 13 events. Authors do mention limitations due to the small number of events and the reliability of strongyloides estimates.
Authors indicate no conflicts of interest, however the first author has been an investigator on a Pfizer trial, which may be NCT04092452, showing completion in January 2022378,379.
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B
C
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Fig. 34. Mixed-effects meta-regression showing efficacy as a function of strongyloides prevalence. A. all studies. B. all RCTs. C. all mortality results.
The following refers to the first author's analysis posted earlier on Twitter. The author selected 10 of the 106 studies, with 3 in a high strongyloides prevalence group where a greater benefit is seen. This was used to draw strong conclusions about the mechanism of ivermectin efficacy.
There are several limitations to this analysis. One of the 3 studies does not mention steroids in the list of SOC medications, while a second reports 6% usage for the control group. Author has added a fourth paper in a revised grouping with 11 studies.
We perfomed a similar analysis for all studies (except the 2 ecological studies), which shows no significant effect, with the high prevalence group actually showing lower improvement (50% [35‑62%] vs. 65% [57‑71%] for the low prevalence group). Details can be found in the supplementary data. Results are similar when restricting to mortality results or when restricting to RCTs.
Why does the smaller analysis with 11 studies show a greater benefit in high strongyloides prevalence regions? The effect is based on relatively few events - 1, 3, 4, and 13 respectively for the high prevalence group. More importantly, the result is confounded by treatment delay and dose.
Treatment delay. All meta-analyses combine heterogeneous studies which results in limitations. For example in pooled analysis we combine hospitalization and mortality. In terms of evaluating efficacy for COVID-19 treatments, reduction in hospitalization reasonably leads to reduction in mortality for high-risk populations. Both are indicators of efficacy, and both are valuable. In the largest series of COVID-19 treatment trials, hospitalization and mortality estimates are very similar. The same does not apply to treatment delay for antivirals. A trial showing efficacy with early treatment provides no information on late treatment, and a trial showing no efficacy with late treatment provides no information on early treatment. Ivermectin, as with many COVID-19 treatments, shows a strong treatment delay relationship — early treatment shows significantly higher efficacy.
The high prevalence group in the 11 study analysis has more early treatment trials, and the low prevalence group has more late treatment trials. The result is confounded by treatment delay, and reflects the greater efficacy of early treatment.
Only one trial in the high prevalence group is classified as late treatment, I-TECH, which was very close to the cutoff. Moreover, of all trials in the 11 trial analysis, this one uses the highest dose.
Dose. The average dosage used in the high prevalence group is about twice the dose in the low prevalence group, and would be close to three times higher if the Together Trial was not moved to the low prevalence group. The result is confounded by dose, and reflects the greater efficacy of higher dosages.
Variants. Efficacy may vary based on variants. Notably, the Gamma variant was most common for one trial in the low prevalance group. This variant shows dramatically different characteristics236, and clinicians report that significantly higher dosage and/or earlier treatment is required, as may be expected for variants where the peak viral load is significantly higher and/or reached earlier233,234.
Conflicts of interest. Two trials have very high (>$US1B) negative conflicts of interest which may introduce bias towards null effects. The trial in the low prevalance group shows a lower effect size. The trial in the high prevalence group also shows a lower effect size for the primary outcome. This trial shows a larger mortality effect, however with only one event this has very low significance.
Summary. In summary, the greater benefit in high strongyloides prevalence regions is only seen with the small subset of 11 trials and is not seen with all trials, or after restriction to mortality results, or restriction to RCTs. Within the 11 trial sample, all trials except one in the low prevalence group have confounding due to treatment delay and/or low dosage, where a lower effect size is expected. The only remaining trial in the group is unpublished, has an unknown treatment delay (a significant percentage of patients may have been treated very late), has very high negative conflicts of interest, and the Gamma variant was most common, in addition to other issues.
In vitro evidence on required concentration
Some authors claim that Caly showed that therapeutic concentrations are not easily reached in humans. This is incorrect. The authors explain why their in vitro study cannot be used to determine the effective dose in vivo, and state that the concentration required is very unlikely to be an issue380. The study used monkey kidney cells (the only choice at the time of the experiments), which lack adaptive immune responses and do not produce interferon. Authors also note that ivermectin accumulates in lung and other tissues, that subsequent experiments with lung cells show many times greater concentrations, and that the average lung concentration shown in modeling studies exceeds the effective level shown in their research. Tissue concentrations of ivermectin can be much higher than plasma concentration381,382. Authors note that ivermectin works with the immune system and a 1:1 ratio of drug to virus is unlikely to be required. In Bray, authors reply that "ivermectin's key direct target in mammalian cells is a not a viral component, but a host protein important in intracellular transport; the fact that it is a host-directed agent (HDA) is almost certainly the basis of its broad-spectrum activity against a number of different RNA viruses in vitro. The way a HDA can reduce viral load is by inhibiting a key cellular process that the virus hijacks to enhance infection by suppressing the host antiviral response. Reducing viral load by even a modest amount by using a HDA at low dose early in infection can be the key to enabling the body's immune system to begin to mount the full antiviral response before the infection takes control." In further research, authors note that they find efficacy for prophylactic use, and that smaller repeated doses are more efffective than a single larger dose380. Moreover, there are now 28 In Vitro studies that support the efficacy of ivermectin for COVID-1950,56,59,60,62,66,76,77,81,82,85,87,132-147.
Publication bias
Publishing is often biased towards positive results, which we would need to adjust for when analyzing the percentage of positive results. For ivermectin, there is strong evidence of a growing negative publication bias.
As Scott Alexander said in November 2021, "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele." In many locations, publishing positive ivermectin results is not conducive to maintaining employment or friendships. This can be seen in the design of recent trials, and the extreme measures taken to avoid presenting statistically significant positive results, as detailed in the study notes below.
One method to evaluate bias is to compare prospective vs. retrospective studies, although this method has become less useful with ivermectin due to the increase of prospective studies with designs favoring null results. Prospective studies are likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results. Fig. 35 shows a scatter plot of results for prospective and retrospective studies. Prospective studies show 60% [48‑68%] improvement in meta-analysis, compared to 62% [51‑71%] for retrospective studies, showing no significant difference. Bryant also perform a funnel plot analysis, which they found did not suggest evidence of publication bias.
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Fig. 35. Prospective vs. retrospective studies. The diamonds show the results of random-effects meta-analysis.
There is substantial evidence that journals are rejecting and delaying the publication of positive studies, for example by accepting a paper for review, holding it for some time, and then rejecting it without review384,385. One group performed prophylaxis and early treatment studies, with only the less positive study being formally published to date175,179, suggesting a negative publication bias. Dr. Eli Schwartz's332 double blind RCT was rejected without review by The Lancet and Clinical Infectious Diseases386. Authors of Efimenko do not plan to submit the very positive results to a journal, and have self-censored the conference publication, providing further evidence of a negative publication bias. Trials with pending and possibly delayed publication often involve researchers that may be restricted due to politics — publishing positive results may be incompatible with continued employment, whereas negative results can receive priority treatment at certain well-known journals, support the positions of employers or funding organizations, and receive substantial press. For more details of censorship and negative publication bias in ivermectin research see388-390.
News coverage of ivermectin studies is extremely biased. Only studies with designs favoring null results have received significant press coverage in western media98,173,174, all of which have multiple critical issues as discussed below, but ignored by the press.
Physician case series results
Table 4 shows the reported results of physicians that used combined early treatment protocols for COVID-19, compared to results for physicians following guidelines focusing on late treatment (Dr. Uip reportedly prescribed early treatment for himself, but not for patients391). The protocols vary, but include ivermectin, HCQ, vitamin D, vitamin C, zinc, melatonin, quercetin, budesonide, and other treatments. These results are subject to selection and ascertainment bias and more accurate analysis requires details of the patient populations and follow-up, however results are consistently better across many teams, and consistent with the extensive controlled trial evidence that shows a significant reduction in risk with many early treatments, and improved results with the use of multiple treatments in combination.
Table 4. Physicians using early combined treatment protocols had much lower hospitalization and mortality rates compared with those following guidelines focusing on late treatment. Results are subject to selection and ascertainment bias and accurate analysis requires details of the patient populations and followup, however the results are consistent across many teams, and consistent with the extensive controlled clinical evidence showing a significant reduction in risk with many early treatments, and complementary/synergistic benefits with combined treatments. (*) Dr. Uip reportedly prescribed early treatment for himself, but not for patients391. (**) Dr. Scott reports treating hundreds of patients and losing over a hundred, but has not provided specific numbers392. Dr. Scott reports following (and helping create) US guidelines.
LATE TREATMENT
Physician / TeamLocationPatients HospitalizationHosp. MortalityDeath
Dr. David Uip (*) Brazil 2,200 38.6% (850) 2.5% (54)
Dr. Jake Scott (**) USA 1,000 10.0% (100)
Average 38.6% 6.2%
EARLY TREATMENT PROTOCOLS - 40 physicians/teams
Physician / TeamLocationPatients HospitalizationHosp. MortalityDeath
Dr. Roberto Alfonso Accinelli
0/360 deaths for treatment within 3 days		 Peru 1,265 0.6% (7)
Dr. Mohammed Tarek Alam
patients up to 84 years old		 Bangladesh 100 0.0% (0)
Dr. Oluwagbenga Alonge Nigeria 310 0.0% (0)
Dr. Raja Bhattacharya
up to 88yo, 81% comorbidities		 India 148 1.4% (2)
Dr. Flavio Cadegiani Brazil 3,450 0.1% (4) 0.0% (0)
Dr. Alessandro Capucci Italy 350 4.6% (16)
Dr. Shankara Chetty South Africa 8,000 0.0% (0)
Dr. Deborah Chisholm USA 100 0.0% (0)
Dr. Ryan Cole USA 400 0.0% (0) 0.0% (0)
Dr. Marco Cosentino
earlier treatment results were better		 Italy 392 6.4% (25) 0.3% (1)
Dr. Jeff Davis USA 6,000 0.0% (0)
Dr. Dhanajay India 500 0.0% (0)
Dr. Bryan Tyson & Dr. George Fareed USA 20,000 0.0% (6) 0.0% (4)
Dr. Raphael Furtado Brazil 170 0.6% (1) 0.0% (0)
Rabbi Yehoshua Gerzi Israel 860 0.1% (1) 0.0% (0)
Dr. Heather Gessling USA 1,500 0.1% (1)
Dr. Ellen Guimarães Brazil 500 1.6% (8) 0.4% (2)
Dr. Syed Haider USA 4,000 0.1% (5) 0.0% (0)
Dr. Mark Hancock USA 24 0.0% (0)
Dr. Sabine Hazan USA 1,000 0.0% (0)
Dr. Mollie James USA 3,500 1.1% (40) 0.0% (1)
Dr. Roberta Lacerda Brazil 550 1.5% (8) 0.4% (2)
Dr. Katarina Lindley USA 100 5.0% (5) 0.0% (0)
Dr. Ben Marble USA 150,000 0.0% (4)
Dr. Edimilson Migowski Brazil 2,000 0.3% (7) 0.1% (2)
Dr. Abdulrahman Mohana Saudi Arabia 2,733 0.0% (0)
Dr. Carlos Nigro Brazil 5,000 0.9% (45) 0.5% (23)
Dr. Benoit Ochs Luxembourg 800 0.0% (0)
Dr. Ortore Italy 240 1.2% (3) 0.0% (0)
Dr. Valerio Pascua
one patient already on oxygen died		 Honduras 415 6.3% (26) 0.2% (1)
Dr. Sebastian Pop Romania 300 0.0% (0)
Dr. Brian Proctor USA 869 2.3% (20) 0.2% (2)
Dr. Anastacio Queiroz Brazil 700 0.0% (0)
Dr. Didier Raoult France 8,315 2.6% (214) 0.1% (5)
Dr. Karin Ried
up to 99yo, 73% comorbidities		 Turkey 237 0.4% (1)
Dr. Roman Rozencwaig
patients up to 86 years old		 Canada 80 0.0% (0)
Dr. Vipul Shah India 8,000 0.1% (5)
Dr. Silvestre Sobrinho Brazil 116 8.6% (10) 0.0% (0)
Dr. Unknown Brazil 957 1.7% (16) 0.2% (2)
Dr. Vladimir Zelenko USA 2,200 0.5% (12) 0.1% (2)
Average 2.2% 0.1%
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Fig. 36 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05393-400. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Log Risk Ratio Standard Error 1.406 1.055 0.703 0.352 0 -3 -2 -1 0 1 2 A: Simulated perfect trials p > 0.05 Log Risk Ratio Standard Error 1.433 1.074 0.716 0.358 0 -4 -3 -2 -1 0 1 2 B: Simulated perfect trials with varying treatment delay p < 0.0001
Fig. 36. Example funnel plot analysis for simulated perfect trials.
Conflicts of interest
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Ivermectin for COVID-19 lacks this because it is off-patent, has many manufacturers, and is very low cost. In contrast, most COVID-19 ivermectin trials have been run by physicians on the front lines with the primary interest of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, ensuring accurate dosing), many ivermectin trials do not represent the optimal conditions for efficacy.
Two ivermectin trials to date involve very large financial conflicts of interest98,174 — companies closely involved with the trial or organizers stand to lose billions of dollars if ivermectin efficacy becomes more widely known. The design of these trials favors producing a null outcome as detailed in98,174. Note that biasing an RCT to produce a false positive result is difficult (suppressing adverse events is relatively easy401), but biasing a trial to produce a false negative result is very easy — for example, in a trial of an antiviral that works within the first 24 hours of symptom onset, trial organizers only need to avoid treating people within the first 24 hours; or with a disease like COVID-19, organizers only need to select a low-risk population where most people recover quickly without treatment. We note that, even under the very suboptimal designs, these trials produced positive results, although without statistical significance.
Drug interactions
Other treatments may reduce the efficacy of ivermectin, for example dexamethasone may interfere with the pharmacokinetics of ivermectin, significantly reducing plasma concentration and efficacy402.
Designed to fail
Several major trials have been designed in a way that favors finding no effect, with a number of methods including late treatment, selecting low-risk patients, fasting administration, very high conflict of interest medication sourcing, and dosing below current clinical practice. For discussion see403. One patient reported their experience with one of the remote outpatient ivermectin/fluvoxamine trials: they were offered enrollment 7 days after symptoms (receipt of medication would be even later), were offered $400 to participate, and reportedly target healthy people404. Details of issues with several trials are included in the study notes.
Notes
The 106 studies are from 96 independent research teams. 5 studies compare against other treatments rather than placebo. Currently ivermectin shows better results than these other treatments, however ivermectin may show greater improvement when compared to placebo. 17 of 106 studies combine treatments, for example ivermectin + doxycycline. The results of ivermectin alone may differ. 5 of 54 RCTs use combined treatment, three with doxycycline, and one with iota-carrageenan. 2 of 106 studies currently have minimal published details available.
Reviews
Many reviews cover ivermectin for COVID-19, presenting additional background on mechanisms, formulations, and related results, including3,61,70,72-75,78,83,88,287,290,291,293,294,383,388,405-437.
Limitations
Summary statistics from meta-analysis necessarily lose information. As with all meta-analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses for specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials with conflicts of interest may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone116,132,133,135,137,142,145,155,216,241-256. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
Meta-analyses
Typical meta-analyses involve subjective selection criteria, effect extraction rules, and study bias evaluation, which can be used to generate a preferred outcome. In order to avoid bias we include all studies and use a pre-specified method to extract results from all studies, in addition to presenting results for higher-quality studies after exclusions. Results to date are overwhelmingly positive, consistent, and insensitive to potential selection criteria, effect extraction rules, and/or bias evaluation. Additional meta-analyses confirming the effectiveness of ivermectin can be found in Kory, Lawrie, Bryant, Nardelli, Hariyanto, Ragó. Kory et al. also review epidemiological data and provide suggested treatment regimens.
Evidence base
The evidence supporting ivermectin for COVID-19 far exceeds the typical amount of evidence used for the approval of treatments. Lee shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Table 5 and Table 6 compare the amount of evidence for ivermectin compared to that used for other COVID-19 approvals, and that used by WHO for the approval of ivermectin for scabies and strongyloidiasis. Table 7 compares US CDC recommendations for ibuprofen and ivermectin. Fig. 37 compares the results of different meta-analyses.
Table 5. WHO ivermectin approval status.
IndicationStudiesPatientsStatus
Strongyloidiasis4395591Approved
Scabies43910852Approved
COVID‑19106220,588 Pending
COVID‑19 RCTs5417,836
Table 6. Evidence base used for other COVID-19 approvals compared with the ivermectin evidence base.
MedicationStudiesPatientsImprovementStatus
Molnupiravir (UK)177550%Approved
Budesonide (UK)11,77917%Approved
Remdesivir (USA EUA)11,06331%Approved
Casiri/imdevimab (USA EUA)179966%Approved
Ivermectin evidence106220,588 61% [53‑67%] Pending
Table 7. Comparison of CDC recommendations439.
IbuprofenIvermectin
(for scabies)		Ivermectin
(for COVID-19)
Lives saved00>500,000
Deaths per year~450<1<1
CDC recommendedYesYesNo
Based on0 RCTs10 RCTs
852 patients		 54 RCTs
17,836 patients
Fig. 37. Comparison of meta-analysis results.
Perspective
Results compared with other treatments
SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors38-45, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk46, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 38 shows an overview of the results for ivermectin in the context of multiple COVID-19 treatments, and Fig. 39 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Fig. 38. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random-effects meta-analysis. 0.5% of 12,000+ proposed treatments show efficacy440.
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Fig. 39. Efficacy vs. cost for COVID-19 treatments.
Conclusion
Ivermectin is an effective treatment for COVID-19. Treatment is more effective when used early. Meta-analysis using the most serious outcome shows 62% [52‑70%] and 84% [75‑89%] lower risk for early treatment and prophylaxis, with similar results for higher quality studies, primary outcomes, peer-reviewed studies, and for RCTs. Significantly lower risk is seen for mortality, ventilation, ICU admission, hospitalization, recovery, cases, and viral clearance. All remain significant for higher quality studies. 66 studies from 60 independent teams in 28 different countries show significant improvements.
Results are very robust—in worst case exclusion sensitivity analysis 65 of 106 studies must be excluded before statistical significance is lost. Emergent results matching biological mechanisms confirm efficacy: meta-regression of efficacy vs. treatment delay (p = 0.0038), efficacy gradient across stages (p < 0.0000001), and fed vs. fasting administration (p = 0.03).
Optimal use of ivermectin may involve synergy with combined treatments, administration taking into account the lipophilic nature, and sublingual, spray, or inhaled formulations for direct treatment to the respiratory tract. Pharmacokinetics show significant inter-individual variability1. Injectable formulations may reduce variability and provide much faster onset of action143. Liposomal formulations show increased antiviral activity and lower cytotoxicity138. Synergistic results are seen with polytherapy135,137,142. Efficacy varies depending on the manufacturer2, underdosed and contaminated ivermectin is common441-444, and fake tablets with no active ingredient have been reported414,445.
 
Contact.
Contact us on X at @CovidAnalysis.
Funding.
We have received no funding or compensation in any form, and do not accept donations. This is entirely volunteer work.
Conflicts of interest.
We have no conflicts of interest. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.
AI.
We use AI models (Gemini, Grok, Claude, and ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.
Updates.
Our COVID-19 meta-analyses involve the extraction of over 228,000 datapoints from thousands of papers for 227 treatments. We thank the thousands of scientists, physicians, and other contributors that have provided updates, suggestions, feedback, and corrections. These are all welcome and can be submitted at https://c19early.org/imeta.html.
Dedication.
This work is dedicated to top evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce mortality and morbidity during the pandemic. In alphabetical order: Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty, Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr. Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch, Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.
Public domain.
This is a public domain work distributed in accordance with the Creative Commons CC0 1.0 Universal license, which dedicates the work to the public domain by waiving all rights worldwide under copyright law. You can distribute, remix, adapt, and build upon this work in any medium or format, including for commercial purposes, without asking permission. Referenced material and third-party images retain any original copyrights or restrictions. See: https://creativecommons.org/publicdomain/zero/1.0/.
TLDR
As Scott Alexander says: "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele." The extreme politicization means we can only evaluate the data directly.
With 106 controlled studies, 54 RCTs, and extensive supporting evidence, few people have the time and experience to analyze all or most of the evidence. However, the PRINCIPLE trial92 provides a single large trial showing efficacy. Despite being perhaps the most biased trial, with extreme bias against showing efficacy in the design, operation, analysis, and reporting, authors failed to eliminate efficacy and hid the results for 600 days (810 days from the expected announcement time, which resulted in a pause and a supply issue claim that was contradicted by the manufacturer, and continuation with increased bias).
PRINCIPLE showed 36% lower long COVID (pre-specified ongoing persistent COVID-19 specific symptoms), p < 0.000192 (43%, p < 0.0001 if excluding the more common fatigue that may be unrelated). The primary recovery outcome shows superiority of ivermectin with significantly faster recovery and a probability of superiority > 0.999. These results are remarkable given the design for failure—very late low-dose incorrectly-administered truncated treatment with low-risk patients. Significantly better results are expected with recommended usage. The authors claim the significant improvements in this trial are unlikely to be “clinically meaningful”, likely for the reason Scott Alexander illuminates. However, they are not consistent across their own trials with this interpretation, and we cannot find anyone that agrees that 36% lower long COVID is not meaningful (even among known paid industry representatives). Further, from all 227 treatments we cover, improved recovery is very significantly associated with lower mortality, p < 0.0000000001, as shown in Fig. 40.
c19early.org
Widely cited RCTs show efficacy
Despite very poor design and many critical issues, the RCTs typically used to claim a lack of efficacy all report either efficacy or strong signs of efficacy.
PRINCIPLE92 36% lower ongoing persistent COVID-19 specific symptoms, p<0.0001, hidden on page 358 of the appendix92. The primary outcome shows superiority of ivermectin with significantly faster recovery and a probability of superiority > 0.999.
TOGETHER98 "There is a clear signal that IVM works in COVID patients.. that would be significant if more patients were added.." - co-principal investigator98.
ACTIV‑6173 99%, 98%, 97% superiority for time unwell, progression @14,7 days173 (clinical progression reported in the preprint99, video100, and slides447—changed in the journal version, unexplained for over 1,400 days).
COVID‑OUT101 61% lower hospitalization (ivermectin vs. placebo, unreported)101. Authors detail why the hypoxemia results are unusable, however analysis shows the ER results are similarly uninformative and not related to symptoms.
I-TECH448 69% lower mortality, p = 0.09, (75% lower, p = 0.02 among severe patients). This is the only one of the commonly cited RCTs that correctly used fed administration, which shows 3.1 times higher efficacy446.
c19early.org
Results vary as expected across studies—only possible with a real treatment effect
While meta-analysis shows very strong confidence in efficacy, emergent results across studies that match biological mechanisms provides additional confirmation.
Treatment delay Meta-regression of efficacy as a function of treatment delay matches expectations (p = 0.0038).
Gradient across stages Preclinical studies show mechanisms relevant to all stages: blocking initial infection, minimizing viral replication, and mitigating secondary complications; therefore we expect benefits to combine and increase for earlier treatment, and again for prophylaxis—as intervention is delayed the benefit of specific mechanisms is reduced or lost. Meta-regression confirms this relationship with p < 0.0000001. (Specifically, for blocking initial infection: spike-ACE2 disruption59, lipid raft attachment inhibition76, glycan binding60,218, and CD147 inhibition72-75; for minimizing viral replication: importin-α/β nuclear transport blockade47,49,51,58, 3CLpro inhibition66, and RdRp interference67; and for mitigating secondary complications: inhibiting fibrin clots65, reducing lung fibrosis63,79,80, protecting against myocarditis69,81, and dampening cytokine storm77,78).
Fed vs. fasting Administration with a high-fat meal is expected to significantly increase bioavailability, but also support distribution to the lung, bypassing hepatic first-pass metabolism. Studies specifying fed vs. fasting administration show 3.1 times greater efficacy, interaction p = 0.03).
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Fig. 40. Improved recovery is associated with lower mortality. Meta-regression across 227 treatments.
Responses
Primary outcome analysis
We use fixed pre-specified effect extraction to avoid bias and to focus on the most clinically relevant results. For comparison, we have also performed analysis using the primary outcome of studies (shown in the supplementary data), with results showing similar effect sizes. Prophylaxis results are very similar with 100% (18 of 18) positive effects. Early treatment shows 85% (33 of 39) positive effects, improved due to the very small event count negative serious outcomes in Krolewiecki, Vallejos, and Buonfrate no longer having priority. Late treatment shows 73% (36 of 49) positive effects, reduced slightly, primarily due to viral clearance results being the primary outcome in some studies, and viral clearance being less successful with late treatment. Overall, the primary outcome analysis shows 82% (87 of 106) positive effects, compared to 81% (86 of 106) in the main protocol analysis.
BBC
The team referenced in this article is unreliable. Dr. Kyle Sheldrick posted a schedule A statement as a result of a defamation lawsuit admitting to false and defamatory claims regarding one of the world's most highly published and respected critical care physicians449; Dr. Nick Brown has called for trials for crimes against humanity for scientists that "tried to scam the world with their fake treatments" including vitamin D450,451. Gideon Meyerowitz-Katz has posted many false claims detailed below452.
Update: authors indicated that their data would be available "soon" as of Sep 14, 2021, however it has not been released over 1,800 days later453, therefore it is not possible to analyze their methods regarding ivermectin research in detail. However, Dr. Sheldrick posted false and defamatory accusations regarding a highly respected physician that has saved countless lives before and during the pandemic. In this case, detailed methods were published, revealing highly flawed analysis, and a basic misunderstanding of statistics, as detailed by multiple statisticians454,455. Author deleted the blog post. Further, we note that the team's disregard for major issues with the Together Trial, ACTIV-6, López-Medina et al., and Beltran Gonzalez et al. suggest substantial bias.
A BBC article raises questions due to data issues in some studies, based on an analysis from a team of researchers. One of the researchers reports that data in some trials could have been manipulated, while noting that human error can not be ruled out. Others in the team directly accuse authors of malfeasance. Regardless of the cause, concern over these studies is valid. Currently, 2 studies have been retracted, one was withdrawn by a preprint server, and another has been reported as pending retraction, although the journal reports that no retraction is pending. None of these studies are in our analysis.
Existence of some lower quality studies is typical in large evidence bases. The percentage of studies with issues is not greater than reported averages, and is not close to removing evidence of efficacy (and may actually improve evidence as detailed below). We performed an absolute worst case sensitivity analysis, where positive studies are excluded in order of the effect size, with the largest effect first. 61%, or 65 of 106 studies must be excluded to avoid finding statistically significant efficacy (this is in addition to the four papers not in this analysis).
The summary statistics from meta-analysis necessarily obscure most of the information in the evidence base. For those that have read all of the research, knowledge of efficacy is supported by extensive additional information, including for example relationships between outcomes within a study, dose-response relationships within and across studies, treatment delay-efficacy relationships within and across studies, variant-efficacy relationships, etc. Notably, removal of Elgazzar, Samaha, and Niaee improve the treatment delay-efficacy and dose-response relationships and may further increase confidence when considering all information.
Concerns about Cadegiani, Carvallo, Carvallo (B), Carvallo (C) have also been reported. All of these studies are excluded in our exclusion analysis.
Studies Prophylaxis Early treatment Late treatment PatientsAuthors
With GMK/BBC exclusionsw/GMK/BBC exclusions 70 83% [71‑90%] 68% [60‑75%] 50% [35‑62%] 202,648 834
RCTs w/GMK/BBC exc.RCTs w/GMK/BBC exc. 39 85% [54‑95%] 64% [52‑73%] 39% [18‑55%] 12,804 535
Percentage improvement with ivermectin treatment after exclusion of all studies reported by this team
We note that, while malfeasance cannot be ruled out, reported concerns may also be caused by typos, data collection errors not affecting analyzed outcomes, and expected results from multiple tests. Authors, without any prior registration or statistical analysis plan, perform thousands of statistical tests across data in the studies and report results without correcting for multiple tests. For example, reporting the occurrence of a 1 in 1,000 event as evidence of randomization failure, while performing more than this number of tests across studies.
This group often dismisses studies based on an arbitrary statistical significance threshold for a specific outcome, a misunderstanding of statistics456, and indefensible as a pre-filter in meta-analysis.
This group has made many claims unsupported by the data. For Niaee, one author claimed the study "made a HUGE difference". It has no effect on early treatment or prophylaxis. For late treatment, which is not recommended, the change was relatively minor. For Elgazzar, the author claimed that it could be "the most consequential medical fraud ever committed". There was almost no difference in our analysis after removing this paper (excluding 1 of 109 studies has very little effect, and the exclusion actually improves the treatment delay-response relationship).
Statements by the group suggest significant bias. The main author first referred to ivermectin as "something else to debunk" in December 2020, and later as a "horse dewormer". Another group member has called for charging scientists that recommend vitamin D with "crimes against humanity".
The group has made claims about all ivermectin evidence based on the existence of some studies with issues. It is inappropriate to generalize about the entire group of 1299 scientists and researchers based on the mistakes or actions of a few individuals.
This group has focused on finding issues in papers reporting large positive effects, which introduces a significant bias. Notably, the few studies that contribute most to minimizing the effects in meta-analysis include studies with very high conflicts of interest and many reported protocol violations and data issues, however this group disregards all of these issues.
The article claims "The largest and highest quality ivermectin study published so far is the Together trial" which "found no benefit", however this study has not been published, is one of the lowest quality trials with many documented design, execution, and analysis issues, has extremely high conflicts of interest, there is a history of inaccurate reporting prior to publication for a previous treatment in the same trial, and the trial actually reported 18% lower mortality (not statistically significant).
The article reports that 26 studies were examined, however there are 109 studies, authors have not reported their results for all 26, and authors have not provided their data after repeated requests. Currently they have not even provided a list of the 26 studies.
The group has an excessive focus on RCTs, which have a fundamental bias against finding an effect for interventions like ivermectin that are widely known and easily available — patients that believe they need treatment are more likely to decline participation and take the treatment457 (this does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable and unfamiliar).
The main author of the group is also against vitamin D. Of the 138 vitamin D COVID-19 treatment studies, author suggests only one trial is worth looking at458. This gives us a simple case to examine potential bias. Murai is a small trial providing no statistically significant effects (mortality p = 0.43, other outcomes are positive while also not significant). Author acknowledges that the trial is too small for a conclusion. More importantly, this trial provides no information about whether vitamin D reduces the risk of a serious COVID-19 case, because the patients in this trial already had a serious COVID-19 case (90% already on oxygen treatment at baseline). Author does not mention this. The trial also has poorly matched arms in terms of gender, ethnicity, hypertension, diabetes, and baseline ventilation, all favoring the control group. Further, this study uses an inappropriate form of vitamin D — cholecalciferol. In reality physicians would use calcifediol or calcitriol with late stage treatment, because they avoid a very long delay for conversion. We are unaware of a reason to use cholecalciferol in this case (other than to produce a null result). In summary, author's chosen study is the study providing the least useful information from the 138 vitamin D treatment studies to date, suggesting biased analysis.
We fully support this team's effort to clean up the evidence base. This is extremely valuable and improves the integrity of the evidence base (and the accuracy if done equally for all studies). We hope this or other teams can do the same for all treatments. However the analysis plan should be published, details of all tests should be provided, results should be corrected for multiple testing, results for all studies and tests should be provided, and equal attention should be given to studies with non-statistically significant results, especially those with major reported data issues that have been disregarded by this team (for example data suggesting substantial protocol violations including confounding by time in Reis and control arm use of treatments in López-Medina).
For coverage of other errors in the BBC article, and illumination of the stark contrast between Dr. Lawrie's response to the BBC before publication and what they chose to report, see BiRD Group, Elijah, Lawrie (B), Campbell.
More details can be found in the following response regarding the main author of this group.
On September 6, 2021, author is on video saying "what this web site does is it goes through all of those 10 things, finds the positive one, and only reports the positive one ... they'll pick a number that is the lowest number"463 @46:48. This is false, we report individual outcome analyses, and for pooled results we use the most serious outcome with a detailed protocol. Notably, author knows this is false, having posted the protocol on Twitter a month earlier. Moreover, in most cases there could be no ambiguity on the most serious outcome even without a detailed protocol. At the time of author's statement, the most serious outcome was actually the worst of multiple reported outcomes 26% of the time.
Author indicated their data would be released "soon" on Sep 14, 2021. The data was used for conclusions in the BBC and BMJ along with a message that data transparency is needed. No data has been released over 1,800 days later453.
TLDR
As a quick way to assess reliability, consider that author describes the Together Trial as "incredibly well-done" and a “masterpiece of science". The trial actually reported multiple impossible numbers, has refused to release data despite pledging to do so, has extreme conflicts of interest, and had blinding failure, randomization failure, and multiple protocol violations as detailed below. Author similarly disregards major issues with many other trials, including ACTIV-6, López-Medina et al., and Beltran Gonzalez et al. This suggests substantial bias.
See also
The Potemkin Argument: How Scott Alexander and GidMK Caricatured the Work of Honest Scientists
The Potemkin Argument, part I (extension): The Sullying of Babalola et al.
Incorrect, misleading, hyperbolic, and unsupported statements have been made by an influential anti-treatment Twitter personality, journalist, and PhD student known for defending Monsanto Roundup against carcinogenic claims (later settled for $US 11 billion). Author is notable as the only known researcher that reports having read a majority of the 109 (including retracted) studies, but does not find the evidence to be positive. However, their opinion appears to have been formed before reading the studies — they first referred to ivermectin as "something else to debunk". We note that the author has made valuable contributions identifying significant issues with some studies, which has helped to improve the quality of the ivermectin evidence base, and has improved the dose-response and treatment delay-response relationships.
Analysis with GMK's recommended exclusions can be found in the supplementary data, which shows 46% [35‑56%] improvement, p = 0.00000000077.
Author has been paid for writing anti-treatment articles, and has also referred to ivermectin as a "horse dewormer". Author has experienced personal tragedy with multiple family members having died of COVID-19, which may introduce a bias against acknowledging errors in treatment advice.
Author's attempt to discredit the scientists performing ivermectin research centers on the false assertion that excluding a small number of lower quality trials results in a negative outcome. It should be clear from the forest plot that this is not possible, but we can be more specific. We perform a worst case sensitivity analysis, where positive studies are excluded in order of the effect size, with the largest effect first. How many studies do we need to exclude before the meta-analysis RR has a confidence interval exceeding 1.0? 61%, or 65 of 106 studies must be excluded to avoid finding statistically significant efficacy. As with all data in this paper, this analysis will automatically update as the evidence base evolves. Also note that this is after exclusion of withdrawn papers - one has never been in this analysis, the second was removed on the same day it was withdrawn, and the other two were removed in advance of retraction based on author's notification that retraction is pending (only one has been retracted, the journal for Niaee et al. has reported that no retraction is pending).
Author claims that we include several papers that are already excluded in the 11 exclusion analyses.
Author claims that there is a greater percentage of low quality studies for ivermectin and COVID-19 compared to other treatments. This is unsupported for such a large evidence base, and does not match previous studies.
Author often makes a basic error by equating positive effects that are not statistically significant at a specific level with "no effect", a misunderstanding of statistics456. For example, if a study reports 50% improvement with a p value of 0.1, we cannot say that the study shows the treatment is ineffective, or in the words of the author shows "no benefit at all". Author repeatedly makes false claims in this way.
On Sep 14, 2021, author indicated that their team had reviewed about 30 ivermectin studies and their data would be available soon, however it has not been released five years later.
Author appears to favor pharmaceutical company affiliated/operated trials. For example, the author has no problem with the lack of IPD for many pharmaceutical affiliated COVID-19 trials that support the author's treatment positions, yet considers the lack of IPD in a positive ivermectin trial to be problematic. Author believes the pharmaceutical affiliated Together Trial is the highest quality trial so far, yet not only have the authors declined to release IPD that they previously pledged to release, there was not even a preprint when GMK made the statement, and the trial has many critical and serious flaws, extremely high conflicts of interest, and a history of inaccurate reporting prior to publication for another treatment arm. GMK has subsequently published a paper with one of the original co-lead's of the Together Trial (who later joined the trial DSMC).
Author has disregarded treatment delay in analysis, which results in incorrect conclusions. For example, author claims that the RECOVERY trial proved that another treatment is not effective, and would provide definitive data if the same was done for ivermectin. The trial provided valuable data on very late use (9 days after symptoms) with an excessively high dose and very late stage patients. However, it did not provide information on early treatment. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours227,228. Paxlovid was tested with a maximum of 3 days from symptom onset (the mean delay is unknown). For ivermectin, author believes the PRINCIPLE trial will provide strong data on efficacy, however this trial includes low risk patients less than 15 days from symptom onset, and may only provide information on late treatment in a low risk population with lower risk variants. Fig. 41 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 227 treatments. Efficacy declines rapidly with treatment delay.
Late treatment results
are not representative
Fig. 41. GMK believes that results for treatment delayed 9 days from symptom onset provides definitive information on treatment efficacy. However early treatment is critical for antivirals, as shown with antivirals for other respiratory diseases, and in meta-regression of studies from 227 COVID-19 treatments.
Author has an unwarranted focus on a specific outcome (mortality) and a specific subset of trials (RCTs). This would be reasonable in many cases when sufficient high-quality data is available, however this is not the case for off-patent COVID-19 treatment trials, where RCTs often involve delayed treatment, low-risk patients where mortality is rare, or very high conflicts of interest. Widely accepted and effective (for specific variants) treatments like casirivimab/imdevimab, bamlanivimab, and sotrovimab were all approved without statistically significant mortality benefits. Other outcomes are also important — accelerating viral clearance, and reducing cases, hospitalization, ICU admission, ventilation, etc. are all very valuable, for example reducing serious "long COVID" problems, reducing transmission of the virus, and reducing the burden on the healthcare system. These outcomes are also likely to correlate with reduced mortality among larger or higher-risk populations. We note that there is extensive evidence for the mortality outcome when not restricting to RCTs. RCTs have mostly been run with relatively low risk populations where mortality is low, leading to limited statistical significance. However RCTs are inherently biased towards low mortality and towards not finding an effect in this case — ivermectin is well-known to be beneficial for COVID-19 and is easily available, therefore participants that believe they may be at serious risk are more likely to decline participation in the RCT and take the recommended medications. Patients that do choose to participate are also more likely to have low adherence. This bias of RCTs is likely to be even larger in locations where ivermectin is widely used in the community and very easily obtained, which correlates with the observed RCT results.
Author suggests that we have chosen the wrong outcome in some cases. While mistakes are possible, for example we corrected errors with Espitia-Hernandez et al. and Jain et al., the claims made suggest that the author has not read the studies and/or our protocol carefully. Details are below. We note that the author disregards the existence of the individual outcome analyses and the primary outcome analysis.
Most errors have not been corrected by the author over five years later. Many false, misleading, and defamatory statements continue to be available, highly-ranked in search results, and highly influential. Other errors include:
•that excluding Elgazzar et al. completely changes the results and could be "the most consequential medical fraud ever committed". Excluding 1 of 109 studies has very little effect, and the exclusion improves the treatment delay-response relationship.
•that Niaee et al. "made a HUGE difference". It has no effect on early treatment or prophylaxis. For late treatment, which is not recommended, the change was minor, and the exclusion improves the treatment delay-response relationship.
•making basic errors suggesting very superficial reading of studies, for example claiming the RR in Szente Fonseca is the risk of being treated.
•making basic errors suggesting very superficial reading of this paper, for example claiming that a result for prophylaxis studies is based on the number of patients from all studies.
•equating a high degree of COVID-19 in a country partially adopting a treatment with a lack of efficacy, disregarding obvious confounding such as heavily affected areas being more likely to adopt treatment (analysis of results in regions or time periods adopting treatment, while not equivalent to controlled studies, is more informative and shows efficacy306,464-466).
•confusing heterogeneity due to dose, treatment delay, etc. and due to bias.
•disregarding treatment delay to dilute or obscure effects by including late treatment (author has also used this method with other treatments).
•disregarding the existence of specific outcome analyses, RCT analysis, and exclusion-based sensitivity analysis.
•suggesting that efficacy over longer periods is not possible because ivermectin has a half-life of "about a day". Author disregards known efficacy for other conditions over much longer periods, and mischaracterizes the half-life. Antiparasitic efficacy can persist for several months after a single dose467. Plasma half-life is longer in some studies, and significant plasma concentration can persist for over 2 weeks in some patients468. More importantly, ivermectin is highly lipophilic and may accumulate in the lung and other tissues where concentrations may be many times higher469,470.
•misunderstanding funnel plot analysis and explanations other than selective reporting (and providing no evidence of unreported negative studies, while there is substantial evidence of difficulty publishing positive studies384,385).
•suggesting that it is not reasonable to combine evidence from mortality and hospitalization (for example), but happily combining late treatment and early treatment in order to obscure efficacy. If a treatment reduces disease severity requiring hospitalization, reduced mortality in at-risk populations logically follows, whereas lack of efficacy several days after onset can not be extrapolated to early treatment — treatments for a viral infection are often less effective when delayed.
•making serious claims about individual studies without contacting authors (for example claiming patients were excluded for reaching the endpoint too quickly in one study, whereas authors report exclusions due to baseline negative status).
•author is unaware of different variants, suggesting that results should be identical for treatment at a given delay, even when the predominant variants have markedly different peak viral load, time to peak viral load233-235, and mortality (for example Gamma vs. non-Gamma aHR 4.73 [1.15-19.41]236).
The cases where author suggests we have chosen the wrong outcome indicate that the author has not read the studies and/or our protocol carefully:
•suggesting that the risk of a good outcome should be selectively used instead of the risk of a bad outcome (author would like to do this when it reduces the effect size). This is similar to using the risk of surviving instead of the risk of death. 99% survival may only be a 4% improvement over 95% survival, but most people would appreciate the 80% lower risk of death.
•suggesting that hospitalization time should be used for symptomatic recovery in a study where discharge is based on viral clearance (and only tested weekly).
•suggesting that a specific symptom such as cough should be used (author would prefer a less positive result for the study).
•suggesting that viral load is more important than symptomatic results.
•suggesting that mortality should be used in populations with zero mortality (for low-risk populations with no mortality, reduction in mortality is not possible, this does not mean a reduction in hospitalization, for example, is not valuable).
•suggesting that unadjusted results should be used in a study where the adjustments clearly make a significant difference (author wants to cherry-pick unadjusted cough results).
•suggesting that, for example, in a study of viral load where all patients recover, it is not valuable if treated patients recover faster (or are less likely to transmit the virus to others).
•suggesting that study selected outcomes should have priority rather than using a consistent pre-specified protocol, disregarding the added bias and the fact that this actually improves results for ivermectin (for example the very small event count negative serious outcomes in Krolewiecki, Vallejos, and Buonfrate would no longer have priority).
•suggesting that cough is a more important symptom than low SpO2 or fever. Cough can persist for a long time after more serious symptoms resolve, and persistent cough may be caused by many conditions.
•suggesting that combined low dose treatment results should be used in a study that had a combined ivermectin/doxycycline arm (single dose ivermectin, 5 days doxycline) and an ivermectin arm with treatment for 5 days.
We note that this personality has an extensive history of incorrect advice, including for example:
•claiming that flu is more dangerous than COVID-19
•claiming that SARS-CoV-2 is not airborne
•claiming that it's impossible to improve immune system functioning
•even believing and propagating a made up story that claimed ivermectin overdose was causing gunshot victims to wait at an ER
Author has taken a public position against early treatments for COVID-19 since at least July 2020. Given this longstanding and influential negative position, they may tend to view information with a negative filter and confirmation bias, and may be reluctant to admit errors. They acknowledge not having read all of the studies (and appear to have very superficially read others). They submitted zero feedback to us, suggesting that they know their comments are incorrect or that they have a motivation other than correcting errors. Author claims that they could not contact us, however there are over 50 feedback links throughout this article. We also note that the author is not open to critical feedback and routinely blocks Twitter users correcting mistakes or expressing anything critical on their feed. Reports suggest that the author also pre-emptively blocks people that have not even interacted with them, but are connected to other users reporting on their errors. Author ackowledges using a tool called MegaBlock that blocks all people that liked a specific tweet.
The author is also against vitamin D. Of the 138 vitamin D COVID-19 treatment studies, author suggests only one trial is worth looking at458. This gives us a simple case to examine potential bias. Murai is a small trial providing no statistically significant effects (mortality p = 0.43, other outcomes are positive while also not significant). Author acknowledges that the trial is too small for a conclusion. More importantly, this trial provides no information about whether vitamin D reduces the risk of a serious COVID-19 case, because the patients in this trial already had a serious COVID-19 case (90% already on oxygen treatment at baseline). Author does not mention this. The trial also has poorly matched arms in terms of gender, ethnicity, hypertension, diabetes, and baseline ventilation, all favoring the control group. Further, this study uses an inappropriate form of vitamin D — cholecalciferol. In reality physicians would use calcifediol or calcitriol with late stage treatment, because they avoid a very long delay for conversion. We are unaware of a reason to use cholecalciferol in this case (other than to produce a null result). In summary, author's chosen study is one of least useful studies from the 138 studies to date, suggesting biased analysis.
Based on many comments, author appears to focus on superficial criteria such as typesetting and quality of writing. While many of the studies have been performed by non-native English speakers with minimal budgets, this does not imply the researchers are less reliable. Indeed, the author is highly critical of the program used to create a graph, for example, but is unable to see flaws in high budget high conflict of interest trials, even when they prompt >100 scientists to write an open letter requesting retraction283.
Five years later, the author has still not contacted us, making content-free comments on Twitter such as calling us "sh*tty". Other individuals pointing out errors with detailed and careful feedback get similar treatment, such as being called a "d*ckhead" and being blocked.
More details can be found in the BBC response.
Scott Alexander
Feb 2, 2023 update: Scott acknowledges that his analysis was incorrect, that the topic is outside his "expertise and competence level", and that his self-and-GidMK-filtered subset of a 29 study subset of 106 studies actually shows much stronger efficacy than previously claimed. Scott further acknowledges several false and defamatory claims, while standing by others. Scott acknowledges that the strongyloides theory is not very strong. Scott now relies on publication bias to discredit the 670 scientists reporting efficacy, without even reading many of their studies.
For publication bias, author is saying that the statistically significant positive studies were chance events, i.e., there are actually >1,000 ivermectin studies, but there is an extremely strong bias towards publishing only the positive studies.
This is in direct opposition to the evidence on publication bias. High profile journals refuse to publish positive ivermectin studies, while negative ivermectin studies are guaranteed acceptance, widespread press, and fame, just as Scott has received fame for his negative article. As Scott said in November 2021, "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele." Ivermectin has the strongest negative publication bias we have ever seen.
Scott finally points at our overview of efficacy across treatments, claiming that multiple effective treatments is proof of publication bias. As if somehow the existence of several effective treatments is impossible.
Scientists have identified over 2,000 compounds potentially beneficial for COVID-19. It is trivial to disable SARS-CoV-2 in vitro, hundreds of compounds do so, many with existing known and positive safety records. What is the chance that none of these can safely reach the location of SARS-CoV-2 infection? In the nasopharynx, oropharynx? Lung? Other tissues?
Is it really surprising that PVP-I for example, very effective in vitro, can be applied to the nasopharynx/oropharynx in effective concentrations, and is helpful, especially if used before infection spreads further?
Are there really no inputs to the human system that improve functioning of the immune system?
Does Scott really believe that paxlovid, casirivimab/imdevimab, sotrovimab, tixagevimab/cilgavimab, bebtelovimab, ensovibep, molnupiravir, and remdesivir are actually ineffective, all were approved in error because hundreds of other studies were not published?
While Scott dismisses the work of over 11,000 scientists reporting statistically significant positive effects of low-cost treatments (over 600 for ivermectin), he appears to have blind faith in trials having perhaps the highest moral and financial conflicts in the history of medicine, while simultaneously missing many critical issues (ACTIV-6, TOGETHER, COVID-OUT, PRINCIPLE) creating unreliable results, and signs of efficacy despite best efforts to avoid it.
Extreme moral conflicts arise for authors and institutions that played a critical role in the suppression of early treatments — they share responsibility for the results of any mistakes that impacted policies. The extreme financial conflicts come from authors and institutions associated with companies where billions of dollars of profit relies on no effective inexpensive treatments existing.
We challenge Scott to do a more serious analysis. For example: examine all of the studies rather than only 27%; directly examine studies rather than relying on a personality that thinks a study treating patients 9 days after onset is conclusive (a study so extremely late that 17% were on ventilation/ECMO); take into account major issues with studies like ACTIV-6, TOGETHER, COVID-OUT, and PRINCIPLE; and review the ~100 additional supporting papers for understanding of the mechanisms of action, censorship, and other issues.
Previous response: For a much better and more thorough analysis of Scott Alexander's essay, including numerous errors, extreme bias, incorrect statistical analysis, contradictory standards, and failure to make corrections, see the Potemkin Argument series. Part 21 illustrates the potentially catastrophic result of humanity's difficulty in objectively creating and evaluating scientific evidence related to critical world problems.
See also
The Potemkin Argument: Index
The Potemkin Argument, Part 2: The Devil's Advocate
The Potemkin Argument, Part 3: The Misportrayal of Dr. Flavio Cadegiani
The Potemkin Argument, Part 4: How Scott Alexander and GidMK Caricatured the Work of Honest Scientists
The Potemkin Argument, Part 5: The Sullying of Babalola et al.
The Potemkin Argument, Part 6: The Erratic Disciples of Dr. John Carlisle
The Potemkin Argument, Part 7: Scott Alexander's Statistical Power Struggle
The Potemkin Argument, Part 8: Scott's Synthetic Scorn
The Potemkin Argument, Part 9: Scott's Observational Opprobrium
The Potemkin Argument, Part 10: The Ballad of Lopez-Medina
The Potemkin Argument, Part 11: TOGETHER vs. Carvallo, A Tale of Two Studies
The Potemkin Argument, part 12: Bayes Wept
The Potemkin Argument, Part 13: Consequentialism for Me, Deonticism for Thee
The Potemkin Argument, part 14: Achilles' Analytical Heel
The Potemkin Argument, Interlude I: Scott Alexander and the Worm of Inconsistency
The Potemkin Argument, Part 16: Worm Games
The Potemkin Argument, Part 17: Viral Funnel Plot Blues
The Potemkin Argument, Part 18: Scott's Scientific Takeaway
The Potemkin Argument, part 19: My Scientific Takeaway -or- Why You Still Can't Box Intelligence
The Potemkin Argument, Part 20: Scott's Sociological Takeaway—or—The Bret and Pierre Special
The Potemkin Argument, Part 21: The Political Takeaway
Ivermectin: Much Less Than You Needed To Know
The analysis by SSC / Scott Alexander has a number of major issues. For many other uncorrected errors in SSC's analysis, see doyourownresearch.substack.com (B), twitter.com (E).
Analysis with SSC's recommended exclusions can be found in the supplementary data.
Update: after exclusions chosen by SSC, exclusions by GMK, excluding all late treatment, and excluding all prophylaxis studies, SSC found the results in Fig. 42, showing statistically significant efficacy of ivermectin with p = 0.04. The method for computing this p value is not specified. We used the same event results and performed random-effects inverse variance DerSimonian and Laird meta-analysis as shown in Fig. 43, finding much higher significance with p = 0.005. We note that the effect extraction appears biased against ivermectin, choosing the excessive dose arm in Buonfrate, and using the post-hoc exclusion in López-Medina. The Krolewiecki treatment count appears to have been scaled for the different group sizes. We do not know where SSC's Mahmud counts are from.
Fig. 42. SSC's analysis. SSC reports p = 0.04, with an unspecified method.
Fig. 43. Random-effects meta-analysis per SSC's chosen results, finding much higher significance.
Author appears to be against all treatments, labeling them all "unorthodox" and "controversial", even those approved by western health authorities, including casirivimab/imdevimab, bamlanivimab, sotrovimab, and paxlovid. Update: author's original article still refers to all treatments we follow as unorthodox and controversial, however they report that they actually recommend fluvoxamine, paxlovid, casirivimab/imdevimab, bamlanivimab/etesevimab, and sotrovimab, and suggest that they support all western health authority approved treatments which additionally includes remdesivir, budesonide, bebtelovimab, tixagevimab/cilgavimab, and molnupiravir. Author also has positive comments for zinc (but reports there is no proof). i.e., author appears to actually support at least 11 of the 227 treatments we follow. We note that the methodology is the same for all treatments.
We encourage the author to at least direct readers to government approved treatments, for which there are several in the author's country, and many more in other countries (including ivermectin). While approved treatments in a specific country may not be as effective (or as inexpensive) as current evidence-based protocols combining multiple treatments, they are better than dismissing everything as "unorthodox". Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all variants — we need to embrace all safe and effective means.
The third-party analysis that author references for the strongyloides theory is confounded by treatment delay and dosage — the high prevalence group has more early treatment trials and a higher average dose, i.e., the analysis reflects the greater efficacy of early treatment and the greater efficacy of higher dosage. More details can be found in the strongyloides section.
Author refers to studies with positive but not statistically significant results as "negative"301, or "[the] original outcome would also have shown ivermectin not working"174, which are incorrect conclusions456. Update: author believes this means we abandon statistical significance. We do not know where this comes from — all of our results report confidence intervals, and the first two words of this paper are "statistically significant". What is incorrect is making a negative conclusion based on an insignificant result. For example, if one study reports 50% lower mortality without reaching statistical signifiance, this does not mean that the treatment is useless. Consider if there are 10 studies all reporting ~50% lower mortality, the combined evidence may be strong even if each individual result is not statistically significant.
Author notes that: "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists", suggesting an environment that may bias the information that the author sees, and could unconsciously bias analysis. We note that similar environments influence the design, operation, and publication of some existing (and many upcoming) ivermectin trials.
Author looks at 29 of the 106 studies, which we note is much better than most commenters, but still ignores the majority of studies, including the prophylaxis studies.
The author finds efficacy at p = 0.04 in their analysis of 11 of the 29 studies they looked at. We note that simply looking at the other 77 studies will result in much higher confidence in efficacy. We also note that even at p = 0.04 with 11 independent studies, a rational risk-benefit analysis results in immediate adoption into protocols (pending stronger data with other combinations of treatments), and immediate collection of more data from sources without conflicts of interest.
However, ultimately the author at least partially supports the two prevailing theories that are commonly used by those against treatment. These theories require disregarding extensive contradictory evidence:
The steps required to accept the no-significant-effect outcome are extreme — one needs to find a reason to exclude most of the studies, disregard the strong treatment-delay response relationship, and disregard all prophylaxis studies. Even after this, the result is still positive, just not statistically signficant. This does not support a negative recommendation. Widely accepted and effective (subject to dependence on viral variants) treatments like casirivimab/imdevimab, bamlanivimab, and sotrovimab were all approved without statistically significant mortality benefits.
The steps required to accept the strongyloides-mechanism-only conclusion are also extreme - we need to disregard the majority of outcomes occuring before steroid use, and disregard the strong treatment-delay response relationship which is contradictory. Fig. 34 shows analysis by strongyloides prevalence. The third-party analysis referenced by the author is confounded by treatment delay and dosage.
Author seems biased against believing any large effect size. We note that large effect sizes have been seen in several COVID-19 treatments approved by western health authorities, including paxlovid which the author is very positive about, and also that better results may be expected when studies combine multiple effective treaments with complementary mechanisms of action (as physicians that treat COVID-19 early typically do). Update: author confirms this bias but appears to disregard it for paxlovid.
Author is suspicious about a study based on the country of the researchers, and also appears biased against non-native speakers, with comments such as "unreadable" for one paper, compared to "written up very nicely in real English" for another. Update: author confirms being biased against certain countries.
Author calls a physician that has reported zero deaths and 5 hospitalizations with 2,400 COVID-19 patients "a crazy person" that "put his patients on every weird medication he could think of".
Author disregards the dramatically higher mortality for Gamma vs non-Gamma variants (aHR 4.73 [1.15-19.41]236), instead concluding that higher mortality indicates fraud in one instance, while in another instance assuming that the related confounding by time in the Together Trial is not significant.
Author's review of the 29 studies appears relatively cursory, for example author appears unaware that the ivermectin dosage is very different in the ivermectin + doxycycline arm of Ahmed.
Author appears to accept the analysis and accusations of GMK as correct, however that author is often incorrect.
Author is concerned that we detail problems with López-Medina, while correctly noting that the outcomes in this trial are actually positive and in favor of ivermectin (while not statistically significant in isolation).
Author is concerned that we specifically comment on Reis, López-Medina. We note that it has been others that have focused on these trials — we comment on them because they have received special attention, including being held up as sole evidence overriding all other trials, despite having major issues.
Author claims that nobody can find issues with Vallejos, which suggests that they have not read the study, or our analysis.
Elgazzar
This study was withdrawn and was removed from this analysis on the same day. There was no significant change (excluding 1 of 109 studies has very little effect, and the exclusion actually improves the treatment delay-response relationship).
Samaha
This study was removed from this analysis within an hour of notification that it was pending retraction. There was no significant change in the results, and the exclusion improves the dose-response relationship.
Merino
This preprint was censored by the original preprint host. Censors claim that the government treatment program, which used approved medications and saved over 500 people from hospitalization, was unethical. In part they also indicate that studies of "the effects of a medication on a disease outcome" are outside the scope of their site.
The author's response (not provided by the censors) can be found here: twitter.com (F). Author's provide the data and code for the study, and the results have been independently verified.
Pott-Junior
This paper appears to have been censored at the request of the journal's founding editor474. An external review is mentioned but is not provided, and there is no reply from the authors, or indication that the authors were notified. Conclusions in this study are limited due to the small size, however we should consider all information in the context of the full body of research.
Efimenko
The conference publication for this analysis was self-censored by the authors, not due to any error in the analysis, but because authors believe ivermectin "has proven to be ineffective in clinical trials". This is incorrect, 66 studies show statistically significant positive results for one or more outcomes (39 prospective and 27 retrospective studies, including 29 Randomized Controlled Trials).
Carvallo
Concerns have been raised about Carvallo. There appears to be some valid concerns with potential data issues, and this study is excluded in the exclusion analysis. There is no significant change in results, with only a minor reduction in prophylaxis efficacy to 83% [71‑90%]. However, it is difficult to trust information from the personality reporting the concerns. The author suggests that the study may not have happened at all, claiming for example that the team could not have afforded the medications without funding, and that a busy clinician would not have enough time. However, with just basic checks, the author would know that a drug company has confirmed donating the medications, that they confirmed authorization for the study was received, that the main hospital for the study requested additional supplies, and that the hospital confirmed ethics committee approval. For additional details see O'Reilly. We also note that the combined treatment in this study has been independently shown to be effective, and the complementary mechanisms of action support improved efficacy of the combination476.
Study Notes
For discussion of all studies see c19early.org/i. A few studies have received special attention, with some considering them to be very strong evidence overriding the other 105 studies. We note limitations of these studies here.
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Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial
See also
The Publication of Fraudulent Ivermectin Trials by the High Impact Medical Journals
The Potemkin Argument, Part VI: The Ballad of Lopez-Medina
More Fraud Uncovered In The Lopez-Medina Ivermectin Trial Published In JAMA
Phone survey based RCT with low risk patients, 200 ivermectin and 198 control, showing lower mortality, lower disease progression, lower treatment escalation, and faster resolution of symptoms with treatment, without reaching statistical significance. Authors find the results of this trial alone do not support the use of ivermectin. However the effects are all positive, especially for serious outcomes which are unable to reach statistical significance with the very small number of events in the low risk population.
An open letter, signed by >100 physicians, concluding this study is fatally flawed can be found at jamaletter.com.
With the low risk patient population, there is little room for improvement with an effective treatment - 59/57% (IVM/control) recovered within the first 2 days to either "no symptoms" or "not hospitalized and no limitation of activities"; 73/69% within 5 days. Less than 3% of all patients ever deteriorated.
The primary outcome was changed mid-trial, it was originally clinical deterioration, which is more meaningful, and shows greater benefit. The new outcome of resolution of symptoms includes "not hospitalized and no limitation of activities" as a negative outcome and is not very meaningful in terms of assessing how much treatment reduces serious outcomes. Using this measure could completely invalidate results - for example a treatment that eliminates all COVID-19 symptoms but has a temporary minor adverse event could be seen as worse.
Authors state that "preliminary reports of other randomized trials of ivermectin as treatment for COVID-19 with positive results have not yet been published in peer-reviewed journals", however there were 8 peer-reviewed RCTs with positive effects published prior to this paper(and 19 total peer-reviewed studies with positive effects).
Authors advised taking ivermectin on an empty stomach, reducing lung tissue concentration by ~2.5x1.
76 patients were excluded due to control patients receiving ivermectin. However, there was a similar percentage of adverse events like diarrhea, nausea, and abdominal pain in both treatment and control groups. These are potential non-serious side effects of treatment and suggest that it is possible that many more control patients received some kind of treatment.
No pre-registered protocol documentation has been found, the same organization is associated with other COVID trials with extremely high financial conflicts of interest with this trial, and the official registration shows a different code to the paper (IVE-PA_CEIP vs. PI-CEP-1390)726.
Ivermectin was widely used in the population and available OTC at the time of the study. The paper claims that patients were excluded if they used ivermectin within the last 5 days, however this conflicts with the trial registration which shows that use of ivermectin within the previous 2 days was an exclusion criterion. A post-hoc change to 5 days was made on December 16, 2020727,728, which is after enrollment ended (July 15 to November 30, 2020). Ivermectin may retain efficacy far beyond 2 or 5 days. Note that, with 75% of patients having symptoms for 4+ days at baseline, the trial registration allows patients to take ivermectin for a few days after symptoms and then join the placebo arm two days later729.
The study reports 11.5% blurry vision with ivermectin, consistent with known side effects. However, the study also reports 11.6% blurry vision in the placebo group, which is not consistent with expected side effects of placebo. One possible explanation is that many placebo patients received ivermectin.
This study reportedly has an ethical issue whereby participants were told the study drug was "D11AX22"730. The editor-in-chief of JAMA initially offered to help with this issue, but later indicated that "JAMA does not review consent forms", however the lead author reportedly confirmed the issue731-733.
The study protocol specifically allows "the use of other treatments outside of clinical trials". The paper provides no information on what other treatments were used, but other treatments were commonly used at the time. Additionally, the control group did about 5x better than anticipated for deterioration, also suggesting that the control patients used some kind of treatment. Patients that enroll in such a study may be more likely to learn about and use other treatments, especially since they do not know if they are receiving the study medication.
The study protocol was amended 4 times. Amendments 2-4 are provided but amendment 1 is missing. Amendment 2 increased the inclusion criteria to within 7 days of onset, including more later stage patients and reducing the expected effectiveness. The trial protocol lists “the duration of supplemental oxygen” as an outcome but the results for this outcome are missing.
RCTs have a fundamental bias against finding an effect for interventions that are widely available — patients that believe they need treatment are more likely to decline participation and take the intervention457, i.e., RCTs are more likely to enroll low-risk participants that do not need treatment to recover (this does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable). This trial was run in a community where ivermectin was available OTC and very widely known and used.
Grants and/or personal fees, including in some cases during the conduct of the study, were provided by Sanofi Pasteur, GlaxoSmithKline, Janssen, Merck, and Gilead. For more details see734.
For other confounding issues see735 and additional issues can be found in the comments of the article736. Re-analysis of the raw data has been reported to show a significant positive effect737.
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Ivermectin to prevent hospitalizations in patients with COVID-19 (IVERCOR-COVID19) a randomized, double-blind, placebo-controlled trial
See also
The Publication of Fraudulent Ivermectin Trials by the High Impact Medical Journals
With only 7% hospitalization, this trial is underpowered. The trial primarily includes low-risk patients that recover quickly without treatment, leaving minimal room for improvement with treatment. 74 patients had symptoms for ≥7 days. Among the 7 patients requiring ventilation, authors note that the earlier requirement in the ivermectin group may be due to those patients having higher severity at baseline. However, authors know the answer to this - it is unclear why it is not reported. There were more adverse events in the placebo group than the ivermectin group, suggesting a possible issue with dispensing or non-trial medication usage. 25+% of patients were hospitalized within 2/3 days for the placebo/treatment groups (Figure S2).
The companion prophylaxis study IVERCOR PREP has reported results in the press and an online presentation738,739, however these results have not yet been formally published. The prophylaxis study results are very positive and statistically significant, and would be expected to receive priority publication due to the predicted impact on the pandemic and confirmation of previous prophylaxis studies. The lack of formal publication suggests a negative publication bias that may be due to politicization in the authors' location.
Authors pre-specify multivariate analysis but do not present it, however multivariate analysis could significantly change the results. Consider for example if just a few extra patients in the ivermectin group were in severe condition based on baseline SpO2. The lower mean SpO2 in the ivermectin group, and the shorter time to ventilation, are consistent with this being the case. Additionally, there are 14% more male patients in the ivermectin group.
An extremely large percentage of patients (55%) were excluded based on ivermectin use in the last 7 days. However, ivermectin may retain efficacy much longer (for example antiparasitic activity may persist for months467). A significant number of patients may also misrepresent their prior and future usage — the population is clearly aware of ivermectin, and patients with progressing disease may be motivated to take it, knowing that they may be in the control group. Another report states that 12,000 patients were excluded for recent use of ivermectin740).
RCTs have a fundamental bias against finding an effect for interventions that are widely available — patients that believe they need treatment are more likely to decline participation and take the intervention457, i.e., RCTs are more likely to enroll low-risk participants that do not need treatment to recover (this does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable). This trial was run in a community where ivermectin was very widely known and used.
For other issues see trialsitenews.com (K).
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Efficacy and Safety of Ivermectin and Hydroxychloroquine in Patients with Severe COVID-19: A Randomized Controlled Trial
Another study reports results on a larger group of patients in the same hospital, showing ivermectin mortality RR 0.81 [0.53-1.24]742.
Questions have been raised about this study and the early termination of the study and discontinuation of treatments, because the hospital statistics show a dramatically lower (~75%) case fatality rate during the period of the study743 (data from gob.mx).
DateCasesDeathsCFR
3/20202150%
4/20204125%
5/20201318%
6/20203725%
7/20206558%
8/2020792329%
9/2020541222%
10/2020622134%
11/2020802633%
12/2020411332%
Several other inconsistencies have been reported190.
Although the data from this study is reported to be available and has been shared with an anti-treatment group, independent researchers have been unable to obtain the data for verification190,745.
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Ivermectin for preventing and treating COVID-19
See also
The Publication of Fraudulent Ivermectin Meta-Analyses and Editorials by the High-Impact Medical Journals
The uses and abuses of systematic reviews: the case of ivermectin in Covid-19
Rapid Response: Ivermectin in Covid-19
This meta analysis is designed to exclude most studies. Authors select a small subset of studies, with a majority of results based on only 1 or 2 studies. Authors split up studies which dilutes the effects and results in a lack of statistical significance for most outcomes. Authors perform 16+ meta analyses with very few studies in each analysis, and do not combine the evidence from all studies. However, we can consider the probability of the observed results across all outcomes.
Authors find positive results for 11 of 12 primary efficacy outcomes with events, or 16 of 18 including secondary outcomes. One of the primary outcomes and two of the secondary outcomes show statistically significant improvements in isolation. If we assume independence, the probability that 11+ of 12 primary efficacy outcomes were positive for an ineffective treatment is p = 0.003. For 16+ of 18 outcomes we get p = 0.0007. This simple analysis does not take into account the magnitude of positive effects, or the dependence due to some studies contributing multiple outcomes, however observation suggests that a full analysis of the combined evidence is likely to show efficacy.
The study is entirely retrospective in the current version. The protocol is dated April 20, 2021, and the most recent study included is from March 9, 2021. The protocol was modified after publication in order to include a close to null result (Beltran Gonzalez "patients discharged without respiratory deterioration or death at 28 days"), so the current protocol is dated July 28, 2021.
Authors excluded many studies by requiring results at a specific time, for example mortality, ventilation, etc. required results at exactly 28 days. Authors excluded all prophylaxis studies by requiring results at exactly 14 days.
Studies comparing with other medications were excluded, however these studies confirm efficacy of ivermectin. The only case where they could overstate the efficacy of ivermectin is if the other medication was harmful. There is some evidence of this for excessive dosage/very late stage use, however that does not apply to any of the studies here.
Studies using combined treatment were excluded, even when it is known that the other components have minimal or no effect. 3 of 4 RCTs with combined treatment use doxycycline in addition, which was shown to have no significant effect in Butler (C). Other studies were excluded by requiring PCR confirmation.
Authors are inconsistent regarding active comparators. They state that hydroxychloroquine “does not work”, yet excluded trials comparing ivermectin to a drug they hold to be inactive. On the other hand, remdesivir was an acceptable comparator, although it is considered to be effective standard of care in some locations423.
Authors fail to recognize that Risk of Bias (RoB) domains such as blinding are far less important for the objective outcome of mortality.
Authors include Beltran Gonzalez as "moderate" COVID-19, however patients in this study were in severe condition (baseline SatO2 83).
Fordham summarizes several problems:
•unsupported assertions of adverse reactions to ivermectin, and the outdated claim that unsafe dosing would be needed to be effective;
•a demand for PCR or antigen testing, without analysis of reliability and not universally available even in developed countries at the start of the pandemic;
•contradictions in the exclusion criteria, including placebo and approved SoC comparators, but rejecting hydroxychloroquine, though held to be ineffective (and an approved SoC in some jurisdictions);
•inclusion of “deemed active” comparators whilst excluding “potentially active” ones;
•exclusion of combination therapies, though the norm among practising clinicians;
•the rejection of other than RCTs when the objective is a “complete evidence profile”;
•arbitrary time-points for outcome measures, excluding non-compliant trials;
•fragmentation of data by location of care under varying hospitalisation criteria;
•the resulting focus on a small fraction of the available clinical evidence, with most comparisons based on single studies with no meta-analysis possible;
•a resulting inpatient mortality comparison with fewer patients than a June 2020 confounder-matched study;
•no conclusion on the headline mortality outcome, when multiple lines of evidence from elsewhere (including the WHO) point to significant mortality advantage.
Cochrane was reputable in the past, but is now controlled by pharmaceutical interests. For example, see the news related to the expulsion of founder Dr. Gøtzsche and the associated mass resignation of board members in protest747-749. For another example of bias see ebm.bmj.com.
The BiRD group gave the following early comment: "Yesterday’s Cochrane review surprisingly doesn’t take a pragmatic approach comparing ivermectin versus no ivermectin, like in the majority of other existing reviews. It uses a granular approach similar to WHO’s and the flawed Roman et al paper, splitting studies up and thereby diluting effects. Consequently, the uncertain conclusions add nothing to the evidence base. A further obfuscation of the evidence on ivermectin and an example of research waste. Funding conflicts of interests of the authors and of the journal concerned should be examined."
For discussion of issues added in the updated version see Popp (B).
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Please submit updates and corrections at the bottom of this page.Please submit updates and corrections at https://c19early.org/imeta.html.
8/31/2026: Updated media discussion.
8/27/2026: We added synergistic combinations in the preclinical discussion.
8/15/2026: We updated Sarojvisut to the full paper. Exclusion analyses now exclude this study due to many critical issues including missing primary outcome results, non-reproducible values, and protocol violations.
8/3/2026: Control group size correction for the viral decay rate333, and updated computation and details for the gene E and gene N combination298.
7/23/2026: Added missing median number of days to clinical recovery outcome98.
5/25/2026: Discussion updates.
5/8/2026: We updated Hashmi to the journal version.
5/1/2026: We added Mouawia, corrected Ravikirti (B) to show late treatment, and excluded Roy for data integrity issues.
3/28/2026: We added Zaher.
2/8/2026: Updated discussion of treatment delay.
2/7/2026: Updated discussion of fed vs. fasting administration.
1/22/2026: TLDR updates.
1/19/2026: Preclinical updates. Summary of emergent results matching biological mechanisms.
1/13/2026: We added analysis of the efficacy gradient across stages.
1/6/2026: Updated discussion of administration.
11/14/2025: Updated study notes.
11/1/2025: Media coverage analysis and discussion updates.
9/16/2025: We added Wagstaff (D).
6/20/2025: PRINCIPLE trial discussion updates.
4/26/2025: Preclinical updates.
3/20/2025: Preclinical updates.
12/7/2024: Discussion updates.
11/1/2024: We added Bagheri-Far.
7/22/2024: We added Wijewickrema.
6/13/2024: We added Hashmi.
5/3/2024: We added Varnaseri.
4/18/2024: We added Shahin, Gao.
3/30/2024: Updated discussion of pooled outcomes.
3/29/2024: We added Siripongboonsitti.
3/9/2024: Discussion updates.
2/29/2024: We added Hayward.
2/23/2024: RCT discussion updates.
2/12/2024: We added Oranu.
1/24/2024: We updated the introduction.
12/27/2023: We added Mikamo.
9/23/2023: Preclinical updates.
8/10/2023: Preclinical updates.
7/25/2023: We added Osati.
7/17/2023: We updated the introduction.
6/11/2023: We added Llenas-García.
6/6/2023: We added Wada.
4/21/2023: We added Munir.
4/18/2023: We updated Desort-Henin to the published version.
2/23/2023: We updated Schilling to the journal version.
2/2/2023: Scott Alexander response updates.
1/6/2023: We added Sarojvisut, Desort-Henin.
12/20/2022: We updated the discussion of heterogeneity and RCTs.
12/15/2022: We added the ACTIV-6 600µg/kg arm Naggie (B).
12/9/2022: We updated de la Rocha to the journal version.
10/27/2022: We added Ochoa-Jaramillo.
10/21/2022: We updated the ACTIV-6 trial to the journal version.
9/23/2022: We added Aref (B).
9/9/2022: We added Qadeer.
8/18/2022: We added Bramante.
7/26/2022: We added Schilling.
6/24/2022: We added Mirahmadizadeh.
6/16/2022: We updated the ACTIV-6 analysis.
6/16/2022: We added Rezai, Rezai (B).
6/12/2022: We added Naggie.
6/1/2022: We updated the Together Trial analysis.
5/30/2022: We added George.
5/30/2022: We updated the Together Trial analysis.
5/27/2022: We added de la Rocha.
4/25/2022: SSC discussion updates.
4/17/2022: We added a section on preclinical research.
4/16/2022: We added discussion of the NIH recommendation.
4/9/2022: We updated the Together Trial analysis.
4/8/2022: We added Ravikirti.
4/5/2022: We added preprint discussion based on Zeraatkar, and updated the Together Trial analysis.
4/2/2022: We updated the Together Trial analysis.
3/30/2022: We updated Reis to the journal version.
3/21/2022: Strongyloides discussion updates.
3/3/2022: We updated Beltran Gonzalez to the journal version.
3/2/2022: We added Soto.
2/28/2022: We added Efimenko.
2/25/2022: We added Thairu.
2/23/2022: We updated Mayer to the journal version.
2/18/2022: We updated Lim to the journal version.
2/2/2022: We added Manomaipiboon.
1/28/2022: We added de Jesús Ascencio-Montiel.
1/21/2022: We added Zubair.
1/17/2022: We added an explanation of why funnel plot analysis is not valid in this case.
1/16/2022: We added RCT viral clearance analysis and corrected missing symptomatic case results in the case analysis.
1/15/2022: We updated Kerr to the journal version.
1/15/2022: We corrected hospitalization group sizes in Buonfrate.
1/13/2022: We added Abbas, Baguma.
1/11/2022: We updated Kerr to the latest results, and added discussion of Beltran Gonzalez.
1/7/2022: We updated Buonfrate to the journal version, and we updated Kerr to the latest results.
12/31/2021: We added Shimizu.
12/29/2021: We added Mustafa.
12/26/2021: We updated Kerr to the revised version of the paper.
12/16/2021: We added Jamir.
12/11/2021: We added Kerr.
12/8/2021: We added analysis of the number of independent research groups reporting statistically significant positive results.
12/5/2021: We added Ferreira.
12/5/2021: We added Rezk.
12/3/2021: A note on Bernigaud: continuity correction uses the reciprocal of the contrasting arm Sweeting, as detailed in the appendix. We previously limited the size of the control group when showing the total number of patients, however this was confusing for people that did not read the details, as discussed below. The full group size has always been used when computing the RR.
12/1/2021: Strongyloides discussion updates.
11/30/2021: We corrected Ghauri to use the event counts.
11/24/2021: We added Ozer.
11/24/2021: SSC discussion updates.
11/21/2021: Strongyloides discussion updates.
11/20/2021: Strongyloides discussion updates.
11/19/2021: We added analysis by strongyloides prevalence, and updated it to match the revised classification used in the comparable analysis.
11/19/2021: We added additional exclusion analyses in the supplementary data.
11/18/2021: We incorrectly included López-Medina as a study not reporting use of steroids, however they report 6% usage in the control group.
11/18/2021: We added Samajdar.
11/17/2021: SSC response.
11/16/2021: Discussion updates.
11/12/2021: We now show the number of studies reporting statistically significant results for any outcome, primary outcomes, and the most serious outcome.
11/9/2021: Discussion updates.
11/5/2021: We added discussion of strongyloides, comparison with the recent molnupiravir approval, and notes on recruitment for remote outpatient delayed treatment trials.
11/3/2021: We added Lim.
11/3/2021: Discussion updates.
10/29/2021: Discussion updates including GMK vitamin D analysis.
10/28/2021: Discussion updates.
10/26/2021: We updated the GMK response.
10/24/2021: We added additional exclusion analyses for individual outcomes.
10/21/2021: We added Borody.
10/19/2021: Discussion updates.
10/18/2021: Ghauri was updated to the journal version.
10/16/2021: We added a summary plot for all results.
10/13/2021: We added primary outcome analysis and additional exclusion analyses. Niaee et al. has been reported as pending retraction and has been removed. 10/27 update: the journal has reported that this is incorrect — no retraction is pending.
10/11/2021: Discussion updates. Niaee et al. exclusion. Updates to the study notes including discussion of Vallejos et al. and additional issues in the Together Trial. Discussion of inherent bias in RCTs for widely available interventions.
10/8/2021: Discussion updates.
10/7/2021: Samaha et al. has been reported as pending retraction and has been removed. There was no significant change in the results.
10/4/2021: Merck discussion updates.
9/29/2021: We corrected a display error causing a few points to be missing in Fig. 4.
9/27/2021: We added Mayer.
9/24/2021: We added a graph of variants over time for the Together Trial discussion and corrected outcome discussion for Popp et al.
9/22/2021: Discussion updates.
9/20/2021: Discussion updates.
9/18/2021: We added Buonfrate, and updated discussion of the Together Trial.
9/17/2021: We added study notes.
9/15/2021: Discussion updates.
9/14/2021: FDA discussion updates.
9/9/2021: We added sensitivity analysis to compute the minimum number of studies that need to be excluded in order to avoid showing efficacy. Discussion updates.
9/7/2021: Discussion updates.
9/6/2021: We corrected Espitia-Hernandez to use the reported recovery time and added missing recovery and viral clearance results.
9/3/2021: We updated discussion and excluded Carvallo et al. in the exclusion analysis.
8/27/2021: We updated Morgenstern (B) with the journal version of the article.
8/26/2021: We updated Mohan with the journal version of the article.
8/16/2021: We updated Reis with event counts.
8/15/2021: We updated discussion and made the abstract more concise.
8/12/2021: We added Reis, Elavarasi.
8/8/2021: We updated discussion in the responses.
8/6/2021: We updated Behera (B) with the journal version of the article.
8/5/2021: We added Mondal.
8/4/2021: We added discussion of the FDA recommendation.
8/3/2021: We added discussion in the responses section.
8/2/2021: We added analysis restricted to serious outcomes and analysis restricted to recovery, and we added discussion in the responses section.
7/31/2021: We added discussion in the responses section related to in vitro evidence and therapeutic concentrations.
7/29/2021: We added discussion in the responses section.
7/20/2021: We updated Hashim with the journal version of the article.
7/16/2021: We updated Ravikirti (B) with the journal version of the article.
7/15/2021: Elgazzar et al. was withdrawn by the preprint server and has been removed.
7/9/2021: We added Hazan (B).
7/8/2021: We updated Cadegiani to the journal version.
7/6/2021: We previously limited the size of the control group for Bernigaud when calculating the total number of patients, however this was confusing for many people that did not read the details. We now show the original counts and note the larger size of the control group in the text.
7/3/2021: We added Vallejos.
7/2/2021: We updated Niaee et al. to the journal version.
6/21/2021: We added more information to the abstract.
6/19/2021: We updated Bryant to the journal version.
6/19/2021: Beltran Gonzalez was incorrectly included in the peer-reviewed analysis.
6/18/2021: We added Krolewiecki.
6/15/2021: We added Aref.
6/7/2021: We added Hariyanto.
6/5/2021: We added Ahsan.
6/2/2021: We added Abd-Elsalam.
5/31/2021: Biber was updated to the preprint.
5/26/2021: Samaha et al. was updated to the journal version.
5/18/2021: We added analysis of Merck's recommendation.
5/17/2021: We added Szente Fonseca.
5/15/2021: We updated the discussion of the WHO analysis.
5/13/2021: We updated Mahmud to the journal version.
5/10/2021: We added Faisal.
5/10/2021: We added additional information in the abstract.
5/8/2021: We added Merino.
5/7/2021: We updated Shahbaznejad to the journal version, which includes additional outcomes not reported earlier.
5/6/2021: We updated Chahla to the Research Square preprint.
5/6/2021: We added a comparison of CDC recommendations.
5/6/2021: We added mechanical ventilation and ICU admission analysis.
5/6/2021: We updated discussion based on peer review including discussion of heterogeneity, exclusion based sensitivity analysis, and search criteria.
5/5/2021: We updated Okumuş to the journal paper.
5/5/2021: We previously limited the size of the control group in Bernigaud to be the same as the treatment group for calculation of the total number of patients. This is now also reflected and noted in the forest plots.
5/4/2021: We added Loue.
4/30/2021: We added analysis of the WHO meta-analysis and updated Kory to the journal version.
4/28/2021: We added the WHO meta-analysis results for comparison.
4/27/2021: We added analysis restricted to hospitalization results and a comparison with the evidence base used in the approval of other COVID-19 treatments.
4/26/2021: We added notes on heterogeneity.
4/25/2021: We updated Biber to the latest results reported at the International Ivermectin for Covid Conference.
4/18/2021: We updated Morgenstern to the preprint.
4/16/2021: We added Morgenstern.
4/14/2021: We added Seet.
4/10/2021: We added Kishoria.
4/9/2021: We corrected a duplicate entry for Bukhari.
4/7/2021: We identified studies where minimal detail is currently available in the forest plots.
4/5/2021: We added Mourya.
4/4/2021: We added event counts to the forest plots.
3/31/2021: We updated Chahla (B) to the preprint.
3/30/2021: We added Chahla.
3/28/2021: We highlighted and added discussion for studies that use combined treatments.
3/26/2021: We added Tanioka.
3/25/2021: We added Huvemek.
3/17/2021: We added Nardelli.
3/12/2021: We added Bryant, Roy.
3/10/2021: We added Pott-Junior.
3/6/2021: We added Chowdhury and we identify studies that compare with another treatment.
3/5/2021: We added discussion of pooled effects (we show both pooled effects and individual outcome results).
3/4/2021: We added López-Medina, and we added more information in the abstract.
3/3/2021: We updated the graphs to indicate the time period for the dosage column, now showing the dosage over one month for prophylaxis and over four days for other studies.
3/2/2021: We updated IVERCOR PREP with the latest results Vallejos (B).
2/27/2021: We added analysis restricted to peer reviewed studies.
2/24/2021: We added a comparison of the evidence base and WHO approval status for the use of ivermectin with scabies and COVID-19. We updated Okumuş with the Research Square preprint.
2/23/2021: We added Beltran Gonzalez.
2/18/2021: We updated Babalola to the journal version of the paper.
2/17/2021: We added Elalfy, and we added analysis restricted to viral clearance outcomes, and mortality results restricted to RCTs.
2/16/2021: We updated Behera to the journal version of the paper.
2/15/2021: We added Behera (B).
2/14/2021: We added analysis restricted to COVID-19 case outcomes, and we added additional results in the abstract.
2/12/2021: We added Biber.
2/11/2021: We added more details on the analysis of prospective vs. retrospective studies.
2/10/2021: We added Lima-Morales.
2/5/2021: We updated Bukhari to the preprint.
2/2/2021: We added Mohan.
1/26/2021: We updated Shouman (B) with the journal version of the article.
1/25/2021: We updated IVERCOR PREP with the recently released results.
1/19/2021: We added Shahbaznejad and Samaha et al. Chaccour (B) was updated to the journal version of the paper.
1/17/2021: We added Bukhari.
1/16/2021: We moved the analysis with exclusions to the main text, and added additional commentary.
1/15/2021: We added the effect measured for each study in the forest plots.
1/12/2021: We added Okumuş.
1/11/2021: We added Chahla (B).
1/10/2021: We put all prophylaxis studies in a single group.
1/9/2021: We added Ravikirti (B). Due to the much larger size of the control group in Bernigaud, we limited the size of the control group to be the same as the treatment group for calculation of the total number of patients.
1/7/2021: We added direct links to the study details in the chronological plots.
1/6/2021: We added Babalola.
1/5/2021: We added direct links to the study details in the forest plots.
1/2/2021: We added dosage information and we added the number of patients to the forest plots.
12/31/2020: We added additional details about the studies in the appendix.
12/29/2020: We added meta-analysis excluding late treatment.
12/27/2020: We added the total number of authors and patients.
12/26/2020: We added IVERCOR PREP, Carvallo (B).
12/17/2020: We added Alam.
12/16/2020: We added Ghauri.
12/11/2020: We added Soto-Becerra.
12/7/2020: We added Chaccour (B).
12/2/2020: We added Ahmed.
11/26/2020: Initial revision.
Appendix 1. Methods and Data
Search methods
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org, which regularly receives notification of studies upon publication. Search terms are ivermectin and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of ivermectin for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. Studies with major unexplained data issues, for example major outcome data that is impossible to be correct with no response from the authors, are excluded. However for ivermectin certain high-profile studies that would normally be retracted currently remain published due to the politicization of this treatment—for transparency, and because inclusion does not affect the results, we currently include these studies.
Fig. 44. Mid-recovery results can more accurately reflect efficacy when almost all patients recover. Mateja et al. confirm that intermediate viral load results more accurately reflect hospitalization/death.
Effect extraction
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome-specific analyses. For example, if effects for mortality and cases are reported then they are both used in specific outcome analyses, while mortality is used for pooled analysis. If symptomatic results are reported at multiple times, we use the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcomes are considered more important than viral outcomes. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available. After most or all patients have recovered there is little or no room for an effective treatment to do better, however faster recovery is valuable. An IPD meta-analysis confirms that intermediate viral load reduction is more closely associated with hospitalization/death than later viral load reduction770. If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough.
Statistical methods
Forest plots are computed using PythonMeta771 with the DerSimonian and Laird random-effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I2 statistic. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang (C) et al. Reported confidence intervals and p-values are used when available, and adjusted values are used when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed Altman, Altman (B), and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1766. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.14.8) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.3), statsmodels (0.15.0), and plotly (6.9.0). Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor (4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome. Forest plots show simplified dosages for comparison, these are the total dose in the first four days for treatment, and the monthly dose for prophylaxis, for a 70kg person. For full dosage details see below. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.2 is used to parse PDF documents.
For treatments that have multiple mechanisms of action relevant to all stages—blocking initial infection, minimizing viral replication, and mitigating secondary complications—we may find a gradient of efficacy across stages, with efficacy increasing as we go from late treatment → early treatment → prophylaxis. To evaluate this hypothesis we performed meta-regression with robust variance estimation. Stages were categorized into an ordinal scale with prophylaxis at 1, early treatment at 2, and late treatment at 3. The dependent variable was the natural logarithm of the Risk Ratio (ln(RR)) for each outcome, and the independent variable was the ordinal stage. We used the DerSimonian and Laird (DL) method-of-moments estimator to estimate residual heterogeneity (τ2) around the meta-regression line, with weights assigned using the inverse-variance method (wi = 1 / (vi + τ2)). The meta-regression slope (β) represents the change in ln(RR) per unit increase in stage. A positive slope indicates that efficacy diminishes (the Risk Ratio increases) for later intervention.
When evaluating potential effect modification across groups, we use an interaction test as described by Altman (C) et al. We compared the log-transformed relative risks using a z-test, deriving the standard error of the difference from the 95% confidence intervals. A two-sided interaction p-value of < 0.05 was considered a statistically significant difference in treatment effect between the groups.
Quality evaluation
Cochrane RoB 2/ROBINS-I are often used to evaluate studies, and have the advantage of providing standardized rules that can be applied with minimal understanding of the domain and study. However, the rules do not account for many real-world issues, often overemphasize or underemphasize others, and studies show low inter-rater reliability782. Certain domains are more applicable for these tools, however the time-sensitive nature of a pandemic, with significant mortality for every day of delay in evidence assessment, and the characteristics of COVID-19 make them inappropriate for this domain. This can be demonstrated with examples where expert RoB 2/ROBINS-I ratings do not match reality for COVID-19. Popp (B) et al. use RoB 2 to classify Reis et al. as low risk of bias, however this is the opposite of reality—the trial not only has very high risk of bias, but has very high actual known bias, refusing to release data despite pledging to, reporting multiple impossible numbers, having blinding and randomization failure, and many other issues98. Axfors et al. use RoB 2 to classify Horby et al. as low risk of bias, however this is the opposite of reality—the very late treatment and excessive dosage used produces results with no relevance to recommended usage. HCQ shows poor results with late treatment and excessive dosage, and the combination shows harmD. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias780,781 are far from the most informative. Both involve very late treatment, providing no information on recommended usage, and ROBINS-I does a very poor job of accounting for the impact of confounding factorsE.
Our quality evaluation focuses on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias. The estimated potential impact of each confounding factor, and the direction of the impact is considered. For example, consider a study that shows significantly lower risk, the value of the study varies significantly if confounding points to an underestimate or an overestimate of efficacy. In one case, the real effect may be null, while the other case provides stronger evidence of efficacy (which may be greater than the study shows). Analysis focusing on the risk of bias, while simpler, may penalize studies for theoretical or technical issues that have no or minimal impact on outcomes. Analysis also depends on the outcome, for example certain issues are less relevant for objective outcomes such as mortality. Inaccurate penalization, and inaccurate high-quality evaluation in the face of known major issues affecting outcomes, increases in significance during a pandemic when immediate recognition of new evidence is critical, and when considering all global studies, as required during a pandemic. Investigators in other countries may have different customs for design, analysis, and reporting, and different English language skills, however they may not be less diligent or have greater bias. Investigators in lower-pharmaceutical-profit countries may have lower bias towards profitable interventions.
Treatment time
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective227,228.
Living analysis
This is a living analysis and is updated regularly. Submit updates or corrections with the form below. We received no funding, this research is done in our spare time. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.
Note that the size of the control group in Bernigaud is significantly larger than the treatment group. We previously limited the size to be the same as that of the treatment group for calculation of the number of patients, however this was confusing to many people that did not read the details.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/imeta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Abbas, 12/31/2021, Double Blind Randomized Controlled Trial, placebo-controlled, China, peer-reviewed, 3 authors, study period May 2021 - August 2021, dosage 300μg/kg days 1-5, excluded in exclusion analyses: very minimal patient information, three different results for the recovery outcome, selective omission of the statistically significant recovery p-value, and other inconsistencies.
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 risk of death, 4.0% higher, RR 1.04, p = 1.00, treatment 1 of 99 (1.0%), control 1 of 103 (1.0%).
deterioration of 2 or more points, 40.5% lower, RR 0.59, p = 0.54, treatment 4 of 99 (4.0%), control 7 of 103 (6.8%), NNT 36.
escalation of care, 14.9% lower, RR 0.85, p = 0.82, treatment 9 of 99 (9.1%), control 11 of 103 (10.7%), NNT 63.
fever during study, 17.9% lower, RR 0.82, p = 0.58, treatment 15 of 99 (15.2%), control 19 of 103 (18.4%), NNT 30.
risk of no recovery, 35.6% lower, RR 0.64, p = 0.04, treatment 26 of 99 (26.3%), control 42 of 103 (40.8%), NNT 6.9, primary outcome.
recovery time, 30.8% lower, relative time 0.69, p = 0.08, treatment 99, control 103, primary outcome.
Ahmed, 12/2/2020, Double Blind Randomized Controlled Trial, Bangladesh, peer-reviewed, mean age 42.0, 15 authors, average treatment delay 3.83 days, dosage 12mg days 1-5, the ivermectin + doxycycline group took only a single dose of ivermectin.
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 risk of unresolved symptoms, 85.0% lower, RR 0.15, p = 0.09, treatment 0 of 17 (0.0%), control 3 of 19 (15.8%), NNT 6.3, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 7, fever, ivermectin (5 days), primary outcome.
risk of unresolved symptoms, 62.7% lower, RR 0.37, p = 0.35, treatment 1 of 17 (5.9%), control 3 of 19 (15.8%), NNT 10, day 7, fever, ivermectin (1 day) + doxycycline.
risk of no viral clearance, 75.6% lower, HR 0.24, p = 0.03, treatment 11 of 22 (50.0%), control 20 of 23 (87.0%), NNT 2.7, adjusted per study, inverted to make HR<1 favor treatment, day 7, ivermectin (5 days).
risk of no viral clearance, 56.5% lower, HR 0.43, p = 0.22, treatment 16 of 23 (69.6%), control 20 of 23 (87.0%), NNT 5.8, adjusted per study, inverted to make HR<1 favor treatment, day 7, ivermectin (1 day) + doxycycline.
risk of no viral clearance, 63.0% lower, HR 0.37, p = 0.02, treatment 5 of 22 (22.7%), control 14 of 23 (60.9%), NNT 2.6, adjusted per study, inverted to make HR<1 favor treatment, day 14, ivermectin (5 days).
risk of no viral clearance, 41.2% lower, HR 0.59, p = 0.19, treatment 9 of 23 (39.1%), control 14 of 23 (60.9%), NNT 4.6, adjusted per study, inverted to make HR<1 favor treatment, day 14, ivermectin (1 day) + doxycycline.
time to viral-, 23.6% lower, relative time 0.76, p = 0.02, treatment 22, control 23, ivermectin (5 days).
time to viral-, 9.4% lower, relative time 0.91, p = 0.27, treatment 23, control 23, ivermectin (1 day) + doxycycline.
Aref, 6/15/2021, Randomized Controlled Trial, Egypt, peer-reviewed, 7 authors, study period February 2021 - March 2021, dosage not specified, trial NCT04716569 (history).
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 relative duration of fever, 63.2% lower, relative time 0.37, p < 0.001, treatment 57, control 57, primary outcome.
relative duration of dyspnea, 56.4% lower, relative time 0.44, p < 0.001, treatment 57, control 57.
relative duration of anosmia, 68.8% lower, relative time 0.31, p < 0.001, treatment 57, control 57.
relative duration of cough, 64.3% lower, relative time 0.36, p < 0.001, treatment 57, control 57.
risk of no viral clearance, 78.6% lower, RR 0.21, p = 0.004, treatment 3 of 57 (5.3%), control 14 of 57 (24.6%), NNT 5.2.
time to viral-, 35.7% lower, relative time 0.64, p < 0.001, treatment 57, control 57.
Babalola, 1/6/2021, Double Blind Randomized Controlled Trial, Nigeria, peer-reviewed, baseline oxygen required 8.3%, 10 authors, study period May 2020 - November 2020, dosage 12mg or 6mg q84h for two weeks, this trial compares with another treatment - results may be better when compared to placebo.
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 adjusted risk of viral+ at day 5, 63.9% lower, RR 0.36, p = 0.11, treatment 40, control 20, adjusted per study, inverted to make RR<1 favor treatment.
relative ∆SpO2 (unadjusted), 41.5% better, RR 0.59, p = 0.07, treatment 38, control 18, figure 3.
risk of no viral clearance, 58.0% lower, HR 0.42, p = 0.01, treatment 20, control 20, inverted to make HR<1 favor treatment, 12mg - Cox proportional hazard model.
risk of no viral clearance, 40.5% lower, HR 0.60, p = 0.12, treatment 20, control 20, inverted to make HR<1 favor treatment, 6mg - Cox proportional hazard model.
time to viral-, 49.2% lower, relative time 0.51, p = 0.02, treatment 20, control 20, 12mg, primary outcome.
time to viral-, 34.4% lower, relative time 0.66, p = 0.08, treatment 20, control 20, 6mg.
Biber, 2/12/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Israel, peer-reviewed, 10 authors, study period 12 May, 2020 - 31 October, 2020, average treatment delay 4.0 days, dosage 12mg days 1-3, 15mg for patients ≥70kg, trial NCT04429711 (history).
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 risk of hospitalization, 70.2% lower, RR 0.30, p = 0.34, treatment 1 of 47 (2.1%), control 3 of 42 (7.1%), NNT 20.
risk of no viral clearance, 61.6% lower, RR 0.38, p = 0.02, treatment 8 of 47 (17.0%), control 17 of 42 (40.5%), NNT 4.3, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, Ct>30, multivariable, day 8.
risk of no viral clearance, 39.0% lower, RR 0.61, p = 0.09, treatment 13 of 47 (27.7%), control 21 of 42 (50.0%), NNT 4.5, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, Ct>30, multivariable, day 6, primary outcome.
risk of no viral clearance, 73.0% lower, RR 0.27, p = 0.008, treatment 3 of 23 (13.0%), control 14 of 29 (48.3%), NNT 2.8, culture viability.
risk of no viral clearance, 70.2% lower, RR 0.30, p = 0.14, treatment 2 of 47 (4.3%), control 6 of 42 (14.3%), NNT 10.0, non-infectious samples (Ct>30 or non-viable culture), day 10.
risk of no viral clearance, 82.1% lower, RR 0.18, p = 0.01, treatment 2 of 47 (4.3%), control 10 of 42 (23.8%), NNT 5.1, non-infectious samples (Ct>30 or non-viable culture), day 8.
risk of no viral clearance, 75.6% lower, RR 0.24, p = 0.02, treatment 3 of 47 (6.4%), control 11 of 42 (26.2%), NNT 5.0, non-infectious samples (Ct>30 or non-viable culture), day 6.
risk of no viral clearance, 65.1% lower, RR 0.35, p = 0.05, treatment 4 of 28 (14.3%), control 9 of 22 (40.9%), NNT 3.8, non-infectious samples (Ct>30 or non-viable culture), day 4.
risk of no viral clearance, 51.9% lower, RR 0.48, p = 0.08, treatment 7 of 47 (14.9%), control 13 of 42 (31.0%), NNT 6.2, Ct>30, day 10.
risk of no viral clearance, 57.9% lower, RR 0.42, p = 0.02, treatment 8 of 47 (17.0%), control 17 of 42 (40.5%), NNT 4.3, Ct>30, day 8.
risk of no viral clearance, 44.7% lower, RR 0.55, p = 0.049, treatment 13 of 47 (27.7%), control 21 of 42 (50.0%), NNT 4.5, Ct>30, day 6.
risk of no viral clearance, 31.9% lower, RR 0.68, p = 0.16, treatment 13 of 28 (46.4%), control 15 of 22 (68.2%), NNT 4.6, Ct>30, day 4.
Borody, 10/19/2021, retrospective, Australia, preprint, 2 authors, study period 1 June, 2021 - 30 September, 2021, dosage 24mg days 1-10, this trial uses multiple treatments in the treatment arm (combined with zinc and doxycycline) - results of individual treatments may vary, excluded in exclusion analyses: preliminary report with minimal details.
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 risk of death, 92.3% lower, RR 0.08, p = 0.03, treatment 0 of 600 (0.0%), control 6 of 600 (1.0%), NNT 100, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 92.9% lower, RR 0.07, p < 0.001, treatment 5 of 600 (0.8%), control 70 of 600 (11.7%), NNT 9.2, primary outcome.
Bramante, 8/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 37 authors, average treatment delay 4.6 days, dosage 430μg/kg days 1-3, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04510194 (history) (COVID-OUT).
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 risk of death, 197.1% higher, RR 2.97, p = 1.00, treatment 1 of 408 (0.2%), control 0 of 396 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), day 28.
risk of death/hospitalization, 26.7% lower, RR 0.73, p = 0.66, treatment 4 of 406 (1.0%), control 5 of 394 (1.3%), NNT 352, odds ratio converted to relative risk.
risk of progression, 36.8% higher, RR 1.37, p = 0.33, treatment 23 of 406 (5.7%), control 16 of 394 (4.1%), odds ratio converted to relative risk, combined ER, hospitalization, death, mismatch with symptom data.
risk of progression, 3.7% higher, RR 1.04, p = 0.78, treatment 105 of 407 (25.8%), control 96 of 391 (24.6%), odds ratio converted to relative risk, combined hypoxemia, ER, hospitalization, death, primary outcome, authors note hypoxemia data unreliable.
risk of hospitalization, 60.8% lower, RR 0.39, p = 0.28, treatment 2 of 206 (1.0%), control 5 of 202 (2.5%), NNT 66, IVM vs. placebo.
risk of hospitalization, 74.6% lower, RR 0.25, p = 0.37, treatment 1 of 137 (0.7%), control 4 of 139 (2.9%), NNT 47, IVM vs. placebo, ≤5 days from onset.
risk of hospitalization, 70.1% lower, RR 0.30, p = 0.12, treatment 3 of 406 (0.7%), control 5 of 202 (2.5%), NNT 58, IVM and IVM+MF vs. placebo.
risk of hospitalization, 41.8% lower, RR 0.58, p = 0.50, treatment 3 of 406 (0.7%), control 5 of 394 (1.3%), NNT 189, IVM and IVM+MF vs. placebo and MF.
Bukhari, 1/16/2021, Randomized Controlled Trial, Pakistan, preprint, 10 authors, study period 15 March, 2020 - 15 June, 2020, dosage 12mg single dose, trial NCT04392713 (history).
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 risk of no viral clearance, 82.4% lower, RR 0.18, p < 0.001, treatment 4 of 41 (9.8%), control 25 of 45 (55.6%), NNT 2.2, day 7, primary outcome.
risk of no viral clearance, 38.7% lower, RR 0.61, p < 0.001, treatment 24 of 41 (58.5%), control 43 of 45 (95.6%), NNT 2.7, day 3.
Buonfrate, 9/6/2021, Double Blind Randomized Controlled Trial, Italy, peer-reviewed, 18 authors, study period 31 July, 2020 - 8 June, 2021, average treatment delay 4.0 days, dosage 1200μg/kg days 1-5, arm B 600µg/kg, arm C 1200µg/kg, trial NCT04438850 (history) (COVER), excluded in exclusion analyses: significant unadjusted group differences, with 3 times as many patients in the ivermectin arms having the baseline visit in a hospital setting, and arm C having large differences in baseline gender, weight, cough, pyrexia, and anosmia, excessive dose for arm C.
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 risk of hospitalization, 210.7% higher, RR 3.11, p = 0.47, treatment 1 of 28 (3.6%), control 0 of 31 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), arm B.
risk of hospitalization, 610.0% higher, RR 7.10, p = 0.11, treatment 3 of 30 (10.0%), control 0 of 31 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), arm C, very high dose, poorly tolerated with low compliance, poorly tolerated, low compliance.
relative change in viral load, RR 0.80, p = 0.59, treatment mean 2.5 (±2.2) n=28, control mean 2.0 (±4.4) n=29, day 7, arm B, primary outcome.
relative change in viral load, RR 0.69, p = 0.07, treatment mean 2.9 (±1.6) n=30, control mean 2.0 (±2.1) n=29, day 7, arm C, primary outcome.
Cadegiani, 11/4/2020, prospective, Brazil, peer-reviewed, 4 authors, average treatment delay 2.9 days, dosage 200μg/kg days 1-3, this trial uses multiple treatments in the treatment arm (combined with AZ, nitazoxanide (82), HCQ (22), spironolactone (66), dutasteride (4)) - results of individual treatments may vary, excluded in exclusion analyses: control group retrospectively obtained from untreated patients in the same population.
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 risk of death, 78.3% lower, RR 0.22, p = 0.50, treatment 0 of 110 (0.0%), control 2 of 137 (1.5%), NNT 68, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), control group 1.
risk of mechanical ventilation, 94.2% lower, RR 0.06, p = 0.005, treatment 0 of 110 (0.0%), control 9 of 137 (6.6%), NNT 15, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), control group 1.
risk of hospitalization, 98.0% lower, RR 0.02, p < 0.001, treatment 0 of 110 (0.0%), control 27 of 137 (19.7%), NNT 5.1, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), control group 1.
Carvallo (C), 9/15/2020, prospective, Argentina, peer-reviewed, mean age 55.7, 3 authors, dosage 36mg days 1, 8, dose varied depending on patient condition - mild 24mg, moderate 36mg, severe 48mg, this trial uses multiple treatments in the treatment arm (combined with dexamethasone, enoxaparin, and aspirin) - results of individual treatments may vary, excluded in exclusion analyses: minimal details of groups provided.
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 risk of death, 85.4% lower, RR 0.15, p = 0.08, treatment 1 of 32 (3.1%), control 3 of 14 (21.4%), NNT 5.5, moderate/severe patients, the only treatment death was a patient already in the ICU before treatment, primary outcome.
Chaccour (B), 12/7/2020, Double Blind Randomized Controlled Trial, Spain, peer-reviewed, 24 authors, study period 31 July, 2020 - 11 September, 2020, average treatment delay 1.0 days, dosage 400μg/kg single dose, trial NCT04390022 (history) (SAINT).
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 risk of symptoms, 96.0% lower, OR 0.04, p < 0.05, treatment 12, control 12, logistic regression, chance of presenting any symptom, RR approximated with OR.
viral load, 94.8% lower, relative load 0.05, p = 0.02, treatment 12, control 12, day 7 mid-recovery, relative viral load, geometric mean of gene E and gene N, p-value estimated with a Welch test assuming log-normal viral-load distributions and estimated log-scale standard deviations from the IQRs, Supplementary Table S1.
risk of no viral clearance, 8.0% lower, RR 0.92, p = 1.00, treatment 12, control 12, primary outcome.
Chahla, 3/30/2021, Cluster Randomized Controlled Trial, Argentina, peer-reviewed, 9 authors, study period September 2020 - January 2021, dosage 24mg days 1, 8, 15, 22, trial NCT04784481 (history).
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 risk of no discharge, 86.9% lower, RR 0.13, p = 0.004, treatment 2 of 110 (1.8%), control 20 of 144 (13.9%), NNT 8.3, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, logistic regression, multivariable, primary outcome.
Chowdhury, 7/14/2020, Randomized Controlled Trial, Bangladesh, peer-reviewed, 6 authors, study period 2 May, 2020 - 5 June, 2020, dosage 200μg/kg single dose, this trial compares with another treatment - results may be better when compared to placebo, this trial uses multiple treatments in the treatment arm (combined with doxycycline) - results of individual treatments may vary, trial NCT04434144 (history).
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 risk of hospitalization, 80.6% lower, RR 0.19, p = 0.23, treatment 0 of 60 (0.0%), control 2 of 56 (3.6%), NNT 28, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no recovery, 46.4% lower, RR 0.54, p < 0.001, treatment 27 of 60 (45.0%), control 47 of 56 (83.9%), NNT 2.6, mid-recovery day 5.
recovery time, 15.2% lower, relative time 0.85, p = 0.07, treatment 60, control 56.
risk of no viral clearance, 80.6% lower, RR 0.19, p = 0.23, treatment 0 of 60 (0.0%), control 2 of 56 (3.6%), NNT 28, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), primary outcome.
time to viral-, 4.3% lower, relative time 0.96, p = 0.23, treatment 60, control 56.
de Jesús Ascencio-Montiel, 1/24/2022, retrospective, Mexico, peer-reviewed, 10 authors, dosage 6mg days 1-2, this trial uses multiple treatments in the treatment arm (combined with AZ, acetaminophen, aspirin) - results of individual treatments may vary.
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 risk of death/hospitalization, 59.0% lower, RR 0.41, p < 0.001, treatment 7,898, control 20,150, adjusted per study, multivariable, primary outcome.
risk of death/hospitalization, 71.0% lower, RR 0.29, p < 0.001, treatment 5,557, control 12,526, adjusted per study, with phone call followup, multivariable.
risk of death, 15.0% lower, RR 0.85, p = 0.16, treatment 101 of 7,898 (1.3%), control 303 of 20,150 (1.5%), NNT 445, unadjusted, excluded in exclusion analyses: unadjusted results with alternate outcome adjusted results showing significant changes with adjustments.
risk of mechanical ventilation, 9.1% lower, RR 0.91, p = 0.51, treatment 77 of 7,898 (1.0%), control 216 of 20,150 (1.1%), NNT 1031, unadjusted, excluded in exclusion analyses: unadjusted results with alternate outcome adjusted results showing significant changes with adjustments.
risk of hospitalization, 47.6% lower, RR 0.52, p < 0.001, treatment 485 of 7,898 (6.1%), control 2,360 of 20,150 (11.7%), NNT 18, unadjusted, excluded in exclusion analyses: unadjusted results with alternate outcome adjusted results showing significant changes with adjustments.
risk of progression, 41.8% lower, RR 0.58, p < 0.001, treatment 435 of 7,898 (5.5%), control 1,906 of 20,150 (9.5%), NNT 25, unadjusted, ER, excluded in exclusion analyses: unadjusted results with alternate outcome adjusted results showing significant changes with adjustments.
de la Rocha, 5/23/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, 21 authors, study period 1 July, 2020 - 29 January, 2021, dosage 12mg days 1-3, trial NCT04407507 (history), excluded in exclusion analyses: data mismatch, no response from authors.
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 risk of progression to serious adverse events, 186.7% higher, RR 2.87, p = 1.00, treatment 1 of 30 (3.3%), control 0 of 26 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
viral load, 2.4% lower, relative load 0.98, p = 0.64, treatment mean 33.74 (±4.77) n=30, control mean 32.94 (±7.74) n=26, day 14.
viral load, 7.8% lower, relative load 0.92, p = 0.04, treatment mean 30.64 (±3.74) n=30, control mean 28.25 (±4.21) n=26, mid-recovery, day 5.
Elalfy, 2/16/2021, retrospective, Egypt, peer-reviewed, 15 authors, dosage 18mg days 1, 4, 7, 10, 13, <90kg 18mg, 90-120kg 24mg, >120kg 30mg, this trial uses multiple treatments in the treatment arm (combined with nitazoxanide, ribavirin, and zinc) - results of individual treatments may vary.
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 risk of no viral clearance, 86.9% lower, RR 0.13, p < 0.001, treatment 7 of 62 (11.3%), control 44 of 51 (86.3%), NNT 1.3, day 15, primary outcome.
risk of no viral clearance, 58.1% lower, RR 0.42, p < 0.001, treatment 26 of 62 (41.9%), control 51 of 51 (100.0%), NNT 1.7, day 7.
Espitia-Hernandez, 8/15/2020, retrospective, Mexico, peer-reviewed, mean age 45.1, 5 authors, dosage 6mg days 1-2, 8-9, this trial uses multiple treatments in the treatment arm (combined with azithromycin and cholecalciferol) - results of individual treatments may vary.
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 recovery time, 70.0% lower, relative time 0.30, p < 0.001, treatment 28, control 7.
risk of viral+ at day 10, 97.2% lower, RR 0.03, p < 0.001, treatment 0 of 28 (0.0%), control 7 of 7 (100.0%), NNT 1.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), primary outcome.
Faisal, 5/10/2021, Randomized Controlled Trial, Pakistan, peer-reviewed, 3 authors, study period 5 April, 2020 - 30 May, 2020, dosage 12mg days 1-5.
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 risk of no recovery, 68.4% lower, RR 0.32, p = 0.005, treatment 6 of 50 (12.0%), control 19 of 50 (38.0%), NNT 3.8, 6-8 days, mid-recovery, primary outcome.
risk of no recovery, 27.3% lower, RR 0.73, p = 0.11, treatment 24 of 50 (48.0%), control 33 of 50 (66.0%), NNT 5.6, 3-5 days.
risk of no recovery, 75.0% lower, RR 0.25, p = 0.09, treatment 2 of 50 (4.0%), control 8 of 50 (16.0%), NNT 8.3, 9-10 days.
Ghauri, 12/15/2020, retrospective, Pakistan, peer-reviewed, 6 authors, dosage 12mg days 1-6.
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 risk of fever, 92.2% lower, RR 0.08, p = 0.04, treatment 0 of 37 (0.0%), control 7 of 53 (13.2%), NNT 7.6, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 14.
risk of fever, 86.4% lower, RR 0.14, p < 0.001, treatment 2 of 37 (5.4%), control 21 of 53 (39.6%), NNT 2.9, day 10.
risk of fever, 55.7% lower, RR 0.44, p < 0.001, treatment 13 of 37 (35.1%), control 42 of 53 (79.2%), NNT 2.3, day 7.
risk of fever, 42.2% lower, RR 0.58, p < 0.001, treatment 21 of 37 (56.8%), control 52 of 53 (98.1%), NNT 2.4, day 5.
Kamal, 9/1/2020, Randomized Controlled Trial, trial NCT04425707 (history).
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 Estimated 100 patient RCT with results unknown and over 6 years late.
Krolewiecki, 6/18/2021, Randomized Controlled Trial, Argentina, peer-reviewed, 23 authors, study period 18 May, 2020 - 9 September, 2020, average treatment delay 3.5 days, dosage 600μg/kg days 1-5, trial NCT004381884 (history).
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 risk of mechanical ventilation, 151.9% higher, RR 2.52, p = 1.00, treatment 1 of 27 (3.7%), control 0 of 14 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
risk of progression, 3.7% higher, RR 1.04, p = 1.00, treatment 2 of 27 (7.4%), control 1 of 14 (7.1%).
viral decay rate, RR 0.34, p = 0.09, treatment 20, control 12, relative mean viral decay rate (corrigendum table 2, all treatment patients vs. all control patients), primary outcome.
Loue, 4/17/2021, retrospective quasi-randomized (patient choice), France, peer-reviewed, 2 authors, dosage 200μg/kg single dose.
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 risk of death, 70.0% lower, RR 0.30, p = 0.34, treatment 1 of 10 (10.0%), control 5 of 15 (33.3%), NNT 4.3.
risk of severe case, 55.0% lower, RR 0.45, p = 0.11, treatment 3 of 10 (30.0%), control 10 of 15 (66.7%), NNT 2.7.
López-Medina, 3/4/2021, Double Blind Randomized Controlled Trial, Colombia, peer-reviewed, median age 37.0, 19 authors, study period 15 July, 2020 - 30 November, 2020, average treatment delay 5.0 days, dosage 300μg/kg days 1-5, excluded in exclusion analyses: strong evidence of patients in the control group self-medicating, ivermectin widely used in the population at that time, and the study drug identity was concealed by using the name D11AX22.
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 risk of death, 66.8% lower, RR 0.33, p = 0.50, treatment 0 of 200 (0.0%), control 1 of 198 (0.5%), NNT 198, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of escalation of care, 60.8% lower, RR 0.39, p = 0.11, treatment 4 of 200 (2.0%), control 10 of 198 (5.1%), NNT 33, odds ratio converted to relative risk.
risk of escalation of care with post-hoc <12h exclusion, 34.3% lower, RR 0.66, p = 0.52, treatment 4 of 200 (2.0%), control 6 of 198 (3.0%), NNT 97, odds ratio converted to relative risk.
risk of deterioration by ≥2 points on an 8-point scale, 43.1% lower, RR 0.57, p = 0.37, treatment 4 of 200 (2.0%), control 7 of 198 (3.5%), NNT 65, odds ratio converted to relative risk, primary outcome.
risk of fever post randomization, 24.8% lower, RR 0.75, p = 0.38, treatment 16 of 200 (8.0%), control 21 of 198 (10.6%), NNT 38, odds ratio converted to relative risk.
risk of unresolved symptoms at day 21, 15.3% lower, RR 0.85, p = 0.53, treatment 36 of 200 (18.0%), control 42 of 198 (21.2%), NNT 31, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, Cox proportional-hazard model.
lack of resolution of symptoms, 6.5% lower, HR 0.93, p = 0.53, treatment 200, control 198, inverted to make HR<1 favor treatment, post-hoc primary outcome.
Mahmud, 10/9/2020, Double Blind Randomized Controlled Trial, Bangladesh, peer-reviewed, 15 authors, study period 1 June, 2020 - 30 August, 2020, average treatment delay 4.0 days, dosage 12mg single dose, this trial uses multiple treatments in the treatment arm (combined with doxycycline) - results of individual treatments may vary, trial NCT04523831 (history).
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 risk of death, 85.7% lower, HR 0.14, p = 0.25, treatment 0 of 183 (0.0%), control 3 of 183 (1.6%), NNT 61, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of progression, 57.0% lower, HR 0.43, p < 0.001, treatment 16 of 183 (8.7%), control 32 of 180 (17.8%), NNT 11, adjusted per study, Cox regression.
risk of no recovery, 94.0% lower, HR 0.06, p < 0.001, treatment 72 of 183 (39.3%), control 100 of 180 (55.6%), NNT 6.2, adjusted per study, day 7, Cox regression.
risk of no recovery, 38.5% lower, RR 0.61, p = 0.005, treatment 40 of 183 (21.9%), control 64 of 180 (35.6%), NNT 7.3, day 11.
risk of no recovery, 96.0% lower, HR 0.04, p < 0.001, treatment 42 of 183 (23.0%), control 67 of 180 (37.2%), NNT 7.0, adjusted per study, day 12, Cox regression.
time to recovery, 27.0% lower, HR 0.73, p = 0.003, treatment 183, control 180, Cox regression, primary outcome.
risk of no viral clearance, 39.0% lower, HR 0.61, p = 0.002, treatment 14 of 183 (7.7%), control 36 of 180 (20.0%), NNT 8.1, adjusted per study, Cox regression.
Manomaipiboon, 2/2/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Thailand, peer-reviewed, mean age 48.6, 8 authors, study period 10 October, 2021 - 15 December, 2021, dosage 12mg days 1-5, trial NCT05076253 (history).
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 risk of no recovery, 43.5% lower, RR 0.57, p = 0.26, treatment 3 of 36 (8.3%), control 6 of 36 (16.7%), NNT 12, adjusted per study, odds ratio converted to relative risk, resolution of symptoms, Table S2, day 28.
recovery time, 15.3% lower, RR 0.85, p = 0.56, treatment 36, control 36, inverted to make RR<1 favor treatment, time to resolution of symptoms.
risk of no viral clearance, 5.0% lower, RR 0.95, p = 1.00, treatment 19 of 36 (52.8%), control 20 of 36 (55.6%), NNT 36, day 14, primary outcome.
risk of no viral clearance, 3.3% lower, RR 0.97, p = 1.00, treatment 29 of 36 (80.6%), control 30 of 36 (83.3%), NNT 36, day 7.
Mayer, 9/23/2021, retrospective, Argentina, peer-reviewed, 14 authors, dosage 540μg/kg days 1-5, mean prescribed dose.
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 risk of death, 55.1% lower, RR 0.45, p < 0.001, treatment 3,266, control 17,966, adjusted per study, odds ratio converted to relative risk, Figure 3, multivariable.
risk of ICU admission, 65.9% lower, RR 0.34, p < 0.001, treatment 3,266, control 17,966, adjusted per study, odds ratio converted to relative risk, Figure 3, multivariable.
risk of death, 27.6% lower, RR 0.72, p = 0.03, treatment 3,266, control 17,966, odds ratio converted to relative risk, unadjusted.
risk of ICU admission, 26.0% lower, RR 0.74, p = 0.13, treatment 3,266, control 17,966, odds ratio converted to relative risk, unadjusted.
Merino, 5/3/2021, retrospective quasi-randomized (patients receiving kit), population-based cohort, Mexico, preprint, 7 authors, dosage 6mg bid days 1-2, censored, see notes.
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 risk of hospitalization, 74.4% lower, RR 0.26, p < 0.001, model 7, same time period, patients receiving kit.
risk of hospitalization, 68.4% lower, RR 0.32, p < 0.001, model 1, different time periods, administrative rule.
Mikamo, 9/26/2022, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 19 authors, study period 12 November, 2021 - 7 August, 2022, average treatment delay 3.2 days, dosage 300μg/kg days 1-3, trial NCT05056883 (history) (IVERMILCO), excluded in exclusion analyses: very low risk group with almost no progression leaves little room for improvement, unbalanced baseline dyspnea and high symptom scores, design and post-hoc changes favor null result.
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 COVID-19 pneumonia, 205.0% higher, RR 3.05, p = 0.49, treatment 1 of 502 (0.2%), control 0 of 527 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), Table S8.
use of therapeutic agents, oxygen, transfer, hospitalization, death, 214.9% higher, RR 3.15, p = 0.36, treatment 3 of 502 (0.6%), control 1 of 527 (0.2%).
risk of no improvement, 4.0% higher, HR 1.04, p = 0.62, treatment 502, control 527, 168hr, improving trend, primary outcome.
risk of no recovery, 4.0% lower, HR 0.96, p = 0.72, treatment 502, control 527, 168hr, clinical resolution, Figure S3.
risk of no recovery, 4.0% lower, HR 0.96, p = 0.64, treatment 502, control 527, 240hr, clinical resolution, Figure S3.
Mirahmadizadeh, 6/23/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 12 authors, study period 9 April, 2021 - 20 May, 2021, dosage 24mg single dose, 12mg and 24mg arms.
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 risk of mechanical ventilation, 66.9% lower, RR 0.33, p = 0.37, treatment 1 of 131 (0.8%), control 3 of 130 (2.3%), NNT 65, 24mg.
risk of mechanical ventilation, 33.3% lower, RR 0.67, p = 1.00, treatment 2 of 130 (1.5%), control 3 of 130 (2.3%), NNT 130, 12mg.
risk of hospitalization, 45.9% lower, RR 0.54, p = 0.22, treatment 6 of 131 (4.6%), control 11 of 130 (8.5%), NNT 26, 24mg, primary outcome.
risk of hospitalization, 27.3% lower, RR 0.73, p = 0.63, treatment 8 of 130 (6.2%), control 11 of 130 (8.5%), NNT 43, 12mg, primary outcome.
risk of no recovery, 38.9% lower, RR 0.61, p = 0.27, treatment 8 of 131 (6.1%), control 13 of 130 (10.0%), NNT 26, day 28, 24mg.
risk of no recovery, 30.8% lower, RR 0.69, p = 0.50, treatment 9 of 130 (6.9%), control 13 of 130 (10.0%), NNT 32, day 28, 12mg.
Mohan, 2/2/2021, Double Blind Randomized Controlled Trial, India, peer-reviewed, 27 authors, study period 28 July, 2020 - 29 September, 2020, average treatment delay 5.0 days, dosage 400μg/kg single dose, 200μg/kg also tested, trial CTRI/2020/06/026001 (RIVET-COV).
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 risk of no discharge at day 14, 62.5% lower, RR 0.38, p = 0.27, treatment 2 of 40 (5.0%), control 6 of 45 (13.3%), NNT 12, 24mg.
risk of clinical worsening, 32.5% lower, RR 0.68, p = 0.72, treatment 3 of 40 (7.5%), control 5 of 45 (11.1%), NNT 28, 24mg.
risk of no viral clearance, 23.8% lower, RR 0.76, p = 0.18, treatment 21 of 40 (52.5%), control 31 of 45 (68.9%), NNT 6.1, day 5, 24mg, primary outcome.
risk of no viral clearance, 10.3% lower, RR 0.90, p = 0.65, treatment 20 of 36 (55.6%), control 26 of 42 (61.9%), NNT 16, day 7, 24mg.
Mouawia, 5/1/2026, Randomized Controlled Trial, Lebanon, peer-reviewed, mean age 40.1, 8 authors, study period November 2021 - December 2021, dosage 12mg single dose, <65kg - 9mg, 65-84kg - 12 mg, ≥85kg - 15 mg, trial ChiCTR2000033627.
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 risk of hospitalization, 55.6% lower, RR 0.44, p = 0.24, treatment 4 of 63 (6.3%), control 9 of 63 (14.3%), NNT 13, day 4.
risk of no recovery, 30.9% lower, RR 0.69, p = 0.007, treatment 63, control 63, all symptoms combined.
risk of no recovery, 81.2% lower, RR 0.19, p = 0.002, treatment 3 of 63 (4.8%), control 16 of 63 (25.4%), NNT 4.8, day 4, fever.
risk of no recovery, 33.3% higher, RR 1.33, p = 0.52, treatment 16 of 63 (25.4%), control 12 of 63 (19.0%), day 4, cough.
risk of no recovery, 60.0% lower, RR 0.40, p = 0.44, treatment 2 of 63 (3.2%), control 5 of 63 (7.9%), NNT 21, day 4, sore throat.
risk of no recovery, 26.3% lower, RR 0.74, p = 0.42, treatment 14 of 63 (22.2%), control 19 of 63 (30.2%), NNT 13, day 4, dyspnea.
risk of no recovery, 33.3% lower, RR 0.67, p = 0.74, treatment 4 of 63 (6.3%), control 6 of 63 (9.5%), NNT 32, day 4, pneumonia.
risk of no recovery, 40.0% lower, RR 0.60, p = 0.02, treatment 21 of 63 (33.3%), control 35 of 63 (55.6%), NNT 4.5, day 4, headache.
risk of no recovery, 46.2% higher, RR 1.46, p = 0.31, treatment 19 of 63 (30.2%), control 13 of 63 (20.6%), day 4, anosmia.
risk of no recovery, 22.2% lower, RR 0.78, p = 0.36, treatment 21 of 63 (33.3%), control 27 of 63 (42.9%), NNT 10, day 4, myalgia.
risk of no recovery, 48.0% lower, RR 0.52, p = 0.03, treatment 13 of 63 (20.6%), control 25 of 63 (39.7%), NNT 5.2, day 4, loss of taste.
risk of no recovery, 46.7% lower, RR 0.53, p = 0.02, treatment 16 of 63 (25.4%), control 30 of 63 (47.6%), NNT 4.5, day 4, fatigue.
risk of no recovery, 42.9% lower, RR 0.57, p = 0.53, treatment 4 of 63 (6.3%), control 7 of 63 (11.1%), NNT 21, day 4, dizziness.
viral load, 61.9% lower, relative load 0.38, p < 0.001, treatment 8.38 [7.56-9.2] n=63, control 3.19 [2.36-4.02] n=63, relative ΔCt, day 4.
Mourya, 4/1/2021, retrospective, India, peer-reviewed, 5 authors, dosage 12mg days 1-7.
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 risk of no viral clearance, 89.4% lower, RR 0.11, p < 0.001, treatment 5 of 50 (10.0%), control 47 of 50 (94.0%), NNT 1.2, primary outcome.
Reis, 8/6/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 27 authors, study period 23 March, 2021 - 6 August, 2021, dosage 400μg/kg days 1-3, impossible data, see notes, trial NCT04727424 (history) (TOGETHER), excluded in exclusion analyses: multiple anomalies as per detailed analysis.
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 risk of death, 12.0% lower, RR 0.88, p = 0.68, treatment 21 of 679 (3.1%), control 24 of 679 (3.5%), NNT 226.
risk of mechanical ventilation, 23.0% lower, RR 0.77, p = 0.38, treatment 19 of 679 (2.8%), control 25 of 679 (3.7%), NNT 113.
risk of hospitalization, 17.0% lower, RR 0.83, p = 0.19, treatment 79 of 679 (11.6%), control 95 of 679 (14.0%), NNT 42.
extended ER observation or hospitalization, 10.0% lower, RR 0.90, p = 0.42, treatment 100 of 679 (14.7%), control 111 of 679 (16.3%), NNT 62, primary outcome.
risk of no recovery, 4.8% lower, HR 0.95, p = 0.59, treatment 679, control 679, inverted to make HR<1 favor treatment.
viral clearance, no change, RR 1.00, p = 1.00, treatment 106 of 142 (74.6%), control 123 of 165 (74.5%), day 7.
viral clearance, 31.6% higher, RR 1.32, p = 0.46, treatment 148, control 170, inverted to make RR<1 favor treatment, day 3.
Rezai, 6/16/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, mean age 35.5, 29 authors, study period 19 February, 2021 - 30 August, 2021, dosage 400μg/kg days 1-3, trial IRCT20111224008507N4, excluded in exclusion analyses: multiple critical issues, see study page.
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 risk of death, 4.9% higher, RR 1.05, p = 1.00, treatment 1 of 268 (0.4%), control 1 of 281 (0.4%).
risk of ICU admission, 9.0% higher, RR 1.09, p = 0.95, treatment 268, control 281.
risk of hospitalization, 36.0% higher, RR 1.36, p = 0.41, treatment 268, control 281.
risk of no recovery, 2.0% higher, RR 1.02, p = 0.49, treatment 268, control 281, inverted to make RR<1 favor treatment, post-hoc primary outcome.
risk of no recovery, 13.2% lower, RR 0.87, p = 0.09, treatment mean 3.87 (±0.18) n=268, control mean 4.46 (±0.18) n=281, cough.
risk of no recovery, 16.7% lower, RR 0.83, p = 0.81, treatment mean 2.5 (±0.51) n=268, control mean 3.0 (±0.92) n=281, tachypnea.
risk of no viral clearance, 23.5% higher, RR 1.23, p = 0.16, treatment 268, control 281, inverted to make RR<1 favor treatment.
Schilling, 7/19/2022, Randomized Controlled Trial, Thailand, peer-reviewed, median age 27.0, 38 authors, study period 30 September, 2021 - 18 April, 2022, average treatment delay 2.0 days, dosage 600μg/kg days 1-7, trial NCT05041907 (history) (PLATCOV), excluded in exclusion analyses: post-hoc change to exclude patients treated before high viral load, population very low risk, recovering quickly without treatment, high baseline immunity, 2.2x greater baseline antibody negative for the treatment arm.
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 risk of hospitalization, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 45 (0.0%), control 1 of 45 (2.2%), NNT 45, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of progression, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 45 (0.0%), control 3 of 45 (6.7%), NNT 15, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), hospitalization or progression to COVID-19 rhabdomyolysis.
relative clearance rate, 9.1% worse, RR 1.09, p = 0.36, treatment 45, control 45, primary outcome.
Siripongboonsitti, 3/29/2024, Randomized Controlled Trial, Thailand, peer-reviewed, 5 authors, study period 7 December, 2022 - 3 February, 2023, dosage 600μg/kg days 1-5, this trial uses multiple treatments in the treatment arm (combined with niclosamide) - results of individual treatments may vary, trial TCTR20230403007 (FINCOV), excluded in exclusion analyses: data consistency issues, very low risk patients/variants with almost no progression, all patients received known effective antiviral, baseline differences.
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 risk of progression, no change, RR 1.00, p = 1.00, treatment 1 of 30 (3.3%), control 1 of 30 (3.3%), chest xray progression, day 6.
risk of progression, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 30 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), chest xray progression, day 3.
risk of no recovery, 39.4% lower, RR 0.61, p = 0.19, treatment 30, control 30, combined symptoms.
risk of no recovery, 70.0% lower, RR 0.30, p = 0.048, treatment 30, control 30, mid-recovery, day 3, cough.
risk of no recovery, 14.3% lower, RR 0.86, p = 0.38, treatment 30, control 30, mid-recovery, day 3, sore throat.
risk of no recovery, 66.7% lower, RR 0.33, p = 0.41, treatment 30, control 30, inverted to make RR<1 favor treatment, mid-recovery, day 3, runny nose.
risk of no recovery, 50.0% lower, RR 0.50, p = 0.32, treatment 30, control 30, inverted to make RR<1 favor treatment, day 6, cough.
relative Ct improvement, 6.1% better, RR 0.94, p = 0.75, treatment median 9.15 IQR 9.7 n=30, control median 8.59 IQR 8.24 n=30, E gene, mid-recovery, day 5, primary outcome.
Szente Fonseca, 10/31/2020, retrospective, Brazil, peer-reviewed, mean age 50.6, 10 authors, average treatment delay 4.6 days, dosage 12mg days 1-2, excluded in exclusion analyses: result is likely affected by collinearity across treatments in the model.
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 risk of hospitalization, 13.9% higher, RR 1.14, p = 0.53, treatment 340, control 377, adjusted per study, odds ratio converted to relative risk, control prevalence approximated with overall prevalence, primary outcome.
Vallejos, 7/2/2021, Double Blind Randomized Controlled Trial, Argentina, peer-reviewed, 29 authors, study period 19 August, 2020 - 22 February, 2021, average treatment delay 4.0 days, dosage 12mg days 1-2, <80kg 12mg, 80-110kg 18mg, >110kg 24mg.
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 risk of death, 33.5% higher, RR 1.33, p = 0.70, treatment 4 of 250 (1.6%), control 3 of 251 (1.2%), odds ratio converted to relative risk.
risk of mechanical ventilation, 33.5% higher, RR 1.33, p = 0.70, treatment 4 of 250 (1.6%), control 3 of 251 (1.2%), odds ratio converted to relative risk.
risk of hospitalization, 33.0% lower, RR 0.67, p = 0.23, treatment 14 of 250 (5.6%), control 21 of 251 (8.4%), NNT 36, odds ratio converted to relative risk, primary outcome.
risk of no viral clearance, 5.0% higher, RR 1.05, p = 0.55, treatment 137 of 250 (54.8%), control 131 of 251 (52.2%), inverted to make RR<1 favor treatment, odds ratio converted to relative risk, day 3.
risk of no viral clearance, 26.8% higher, RR 1.27, p = 0.29, treatment 38 of 250 (15.2%), control 30 of 251 (12.0%), inverted to make RR<1 favor treatment, odds ratio converted to relative risk, day 12.
Wijewickrema, 7/22/2024, Double Blind Randomized Controlled Trial, Sri Lanka, peer-reviewed, 11 authors, study period 29 July, 2021 - 17 March, 2022, dosage 24mg days 1-5, trial SLCTR/2021/020.
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 risk of death, 196.1% higher, RR 2.96, p = 1.00, treatment 1 of 127 (0.8%), control 0 of 122 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
progression to moderate disease, 52.0% lower, RR 0.48, p = 0.62, treatment 1 of 127 (0.8%), control 2 of 122 (1.6%), NNT 117, progression to moderate disease.
viral load, 51.2% lower, relative load 0.49, p = 0.03, treatment 80, control 81, relative viral load (copies/mL), day 10, primary outcome.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Abd-Elsalam, 6/2/2021, Randomized Controlled Trial, Egypt, peer-reviewed, 16 authors, study period March 2020 - October 2020, dosage 12mg days 1-3.
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 risk of death, 25.0% lower, RR 0.75, p = 0.70, treatment 3 of 82 (3.7%), control 4 of 82 (4.9%), NNT 82, odds ratio converted to relative risk, logistic regression, primary outcome.
risk of mechanical ventilation, no change, RR 1.00, p = 1.00, treatment 3 of 82 (3.7%), control 3 of 82 (3.7%).
hospitalization time, 19.6% lower, relative time 0.80, p = 0.09, treatment 82, control 82.
Ahsan, 4/29/2021, retrospective, Pakistan, peer-reviewed, 10 authors, dosage 150μg/kg days 1-2, 150-200µg/kg, this trial uses multiple treatments in the treatment arm (combined with doxycycline) - results of individual treatments may vary, excluded in exclusion analyses: unadjusted results with no group details.
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 risk of death, 50.0% lower, RR 0.50, p = 0.03, treatment 17 of 110 (15.5%), control 17 of 55 (30.9%), NNT 6.5.
Aref (B), 9/19/2022, Randomized Controlled Trial, placebo-controlled, Egypt, peer-reviewed, 9 authors, study period 1 March, 2021 - 30 April, 2021, dosage 2 puffs of 70 μg/mL nasal ivermectin, trial NCT04951362 (history).
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 recovery time, 74.0% lower, relative time 0.26, p < 0.001, treatment 49, control 47, anosmia.
Baguma, 12/28/2021, retrospective, Uganda, preprint, 16 authors, study period March 2020 - October 2021, dosage not specified.
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 risk of death, 96.8% lower, RR 0.03, p = 0.31, treatment 7, control 474, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable, control prevalance approximated with overall prevalence.
Beltran Gonzalez, 2/23/2021, Double Blind Randomized Controlled Trial, Mexico, peer-reviewed, mean age 53.8, 13 authors, average treatment delay 7.0 days, dosage 12mg single dose, 18mg for patients >80kg, excluded in exclusion analyses: major inconsistencies reported and the data is no longer available, although the authors state that it is available, and have shared it with an anti-treatment group.
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 risk of death, 14.4% lower, RR 0.86, p = 1.00, treatment 5 of 36 (13.9%), control 6 of 37 (16.2%), NNT 43.
risk of respiratory deterioration or death, 8.6% lower, RR 0.91, p = 1.00, treatment 8 of 36 (22.2%), control 9 of 37 (24.3%), NNT 48.
risk of no hospital discharge, 37.0% higher, RR 1.37, p = 0.71, treatment 4 of 36 (11.1%), control 3 of 37 (8.1%).
hospitalization time, 20.0% higher, relative time 1.20, p = 0.43, treatment 36, control 37, primary outcome.
Budhiraja, 11/18/2020, retrospective, India, preprint, 12 authors, dosage not specified.
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 risk of death, 99.1% lower, RR 0.009, p = 0.04, treatment 0 of 34 (0.0%), control 103 of 942 (10.9%), NNT 9.1, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), unadjusted, primary outcome.
Camprubí, 11/11/2020, retrospective, Spain, peer-reviewed, 9 authors, average treatment delay 12.0 days, dosage 200μg/kg single dose.
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 risk of mechanical ventilation, 40.0% lower, RR 0.60, p = 0.67, treatment 3 of 13 (23.1%), control 5 of 13 (38.5%), NNT 6.5.
risk of ICU admission, 33.3% lower, RR 0.67, p = 1.00, treatment 2 of 13 (15.4%), control 3 of 13 (23.1%), NNT 13, ICU at day 8.
risk of no improvement at day 8, 33.3% higher, RR 1.33, p = 1.00, treatment 4 of 13 (30.8%), control 3 of 13 (23.1%).
risk of no viral clearance, 25.0% higher, RR 1.25, p = 1.00, treatment 5 of 13 (38.5%), control 4 of 13 (30.8%), tests done between days 3-5, primary outcome.
Chachar, 9/30/2020, Randomized Controlled Trial, India, peer-reviewed, 6 authors, dosage 36mg, 12mg stat, 12mg after 12 hours, 12mg after 24 hours, trial NCT04739410 (history), excluded in exclusion analyses: multiple significant issues - pending author response.
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 risk of no recovery, 10.0% lower, RR 0.90, p = 0.50, treatment 9 of 25 (36.0%), control 10 of 25 (40.0%), NNT 25, day 7, primary outcome.
Efimenko, 2/28/2022, retrospective, propensity score matching, USA, peer-reviewed, 6 authors, study period 1 January, 2020 - 11 July, 2021, dosage not specified, this trial compares with another treatment - results may be better when compared to placebo, self-censored, see notes.
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 risk of death, 69.2% lower, OR 0.31, p < 0.001, treatment 1,072, control 40,536, propensity score matching, RR approximated with OR.
Elavarasi, 8/12/2021, retrospective, India, peer-reviewed, 31 authors, study period April 2021 - June 2021, dosage not specified, excluded in exclusion analyses: unadjusted results with no group details.
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 risk of death, 19.6% lower, RR 0.80, p = 0.12, treatment 48 of 283 (17.0%), control 311 of 1,475 (21.1%), NNT 24, unadjusted.
Ferreira, 11/26/2021, retrospective, Brazil, peer-reviewed, 5 authors, study period 12 March, 2020 - 8 July, 2020, average treatment delay 7.0 days, dosage not specified, excluded in exclusion analyses: unadjusted results with no group details; substantial unadjusted confounding by indication likely.
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 risk of death, 5.0% higher, RR 1.05, p = 0.94, treatment 3 of 21 (14.3%), control 11 of 81 (13.6%).
risk of death/intubation, 54.3% higher, RR 1.54, p = 0.37, treatment 6 of 21 (28.6%), control 15 of 81 (18.5%).
risk of death/intubation/ICU, 62.4% higher, RR 1.62, p = 0.27, treatment 8 of 21 (38.1%), control 19 of 81 (23.5%).
George, 5/27/2022, Randomized Controlled Trial, India, peer-reviewed, 15 authors, study period June 2020 - February 2021, dosage 24mg single dose, trial CTRI/2020/05/025068.
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 risk of death, 30.4% lower, RR 0.70, p = 0.55, treatment 5 of 35 (14.3%), control 8 of 39 (20.5%), NNT 16, 24mg.
risk of death, 2.6% higher, RR 1.03, p = 1.00, treatment 8 of 38 (21.1%), control 8 of 39 (20.5%), 12mg.
recovery time, 18.7% lower, relative time 0.81, p = 0.37, treatment mean 4.82 (±4.35) n=35, control mean 5.93 (±5.93) n=39, 24mg.
recovery time, 6.2% lower, relative time 0.94, p = 0.78, treatment mean 5.56 (±5.42) n=38, control mean 5.93 (±5.93) n=39, 12mg.
risk of progression, 33.1% lower, RR 0.67, p = 0.41, treatment 6 of 35 (17.1%), control 10 of 39 (25.6%), NNT 12, 24mg.
risk of progression, 17.9% lower, RR 0.82, p = 0.79, treatment 8 of 38 (21.1%), control 10 of 39 (25.6%), NNT 22, 12mg.
Gorial, 7/8/2020, retrospective, Iraq, preprint, 9 authors, dosage 200μg/kg single dose, trial NCT04343092 (history).
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 risk of death, 71.0% lower, RR 0.29, p = 1.00, treatment 0 of 16 (0.0%), control 2 of 71 (2.8%), NNT 36, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
hospitalization time, 42.0% lower, relative time 0.58, p < 0.001, treatment 16, control 71.
risk of no recovery, 71.0% lower, RR 0.29, p = 1.00, treatment 0 of 16 (0.0%), control 2 of 71 (2.8%), NNT 36, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), primary outcome.
Hashim, 10/26/2020, Single Blind Randomized Controlled Trial, Iraq, peer-reviewed, 7 authors, study period 1 July, 2020 - 14 October, 2020, dosage 200μg/kg days 1-2, some patients received a third dose on day 8, this trial uses multiple treatments in the treatment arm (combined with doxycycline) - results of individual treatments may vary, trial NCT04591600 (history).
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 risk of death, 91.7% lower, RR 0.08, p = 0.03, treatment 0 of 59 (0.0%), control 6 of 70 (8.6%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), excluding non-randomized critical patients, primary outcome.
risk of death, 67.1% lower, RR 0.33, p = 0.16, treatment 2 of 70 (2.9%), control 6 of 70 (8.6%), NNT 18, odds ratio converted to relative risk, including critical patients that were always allocated to treatment.
risk of progression, 83.1% lower, RR 0.17, p = 0.07, treatment 1 of 59 (1.7%), control 7 of 70 (10.0%), NNT 12, excluding non-randomized critical patients.
risk of progression, 57.4% lower, RR 0.43, p = 0.20, treatment 3 of 70 (4.3%), control 7 of 70 (10.0%), NNT 18, odds ratio converted to relative risk, including critical patients that were always allocated to treatment.
recovery time, 40.7% lower, relative time 0.59, p < 0.001, treatment 70, control 70.
Hashmi, 6/13/2024, Randomized Controlled Trial, Pakistan, peer-reviewed, 83 authors, study period 11 June, 2021 - 9 September, 2022, dosage 200μg/kg days 1-5, trial NCT02735707 (history) (REMAP-CAP), excluded in exclusion analyses: baseline severity favors control, post-hoc outcome and SAP changes, see discussion.
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 risk of death, 25.6% lower, RR 0.74, p = 0.40, treatment 81, control 69, adjusted per study, combined.
risk of death, 26.5% lower, OR 0.74, p = 0.57, treatment 44, control 45, adjusted per study, inverted to make OR<1 favor treatment, concurrent, non-critical, day 90, Table S7, RR approximated with OR.
risk of death, 24.8% lower, OR 0.75, p = 0.55, treatment 37, control 24, adjusted per study, inverted to make OR<1 favor treatment, concurrent, critical, day 90, Table S7, RR approximated with OR.
risk of death, 14.9% lower, RR 0.85, p = 0.64, treatment 81, control 69, adjusted per study, combined.
risk of death, 22.5% lower, OR 0.78, p = 0.59, treatment 44, control 45, adjusted per study, inverted to make OR<1 favor treatment, non-critical, day 90, RR approximated with OR.
risk of death, 5.7% lower, OR 0.94, p = 0.91, treatment 37, control 24, adjusted per study, inverted to make OR<1 favor treatment, critical, day 90, RR approximated with OR.
intubation, ECMO, death, 23.7% lower, OR 0.76, p = 0.57, treatment 44, control 45, adjusted per study, inverted to make OR<1 favor treatment, non-critical, RR approximated with OR.
intubation, ECMO, death, 16.0% lower, OR 0.84, p = 0.72, treatment 37, control 24, adjusted per study, inverted to make OR<1 favor treatment, critical, RR approximated with OR.
organ support-free days, 3.8% lower, OR 0.96, p = 0.93, treatment 44, control 45, inverted to make OR<1 favor treatment, non-critical, RR approximated with OR.
organ support-free days, 6.4% higher, OR 1.06, p = 0.89, treatment 37, control 24, inverted to make OR<1 favor treatment, critical, RR approximated with OR.
Hayward, 2/29/2024, Randomized Controlled Trial, placebo-controlled, United Kingdom, peer-reviewed, 25 authors, study period 23 June, 2021 - 1 July, 2022, dosage 350μg/kg days 1-3, trial ISRCTN86534580 (PRINCIPLE).
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 risk of death/hospitalization, 1.0% higher, HR 1.01, p = 0.97, treatment 34 of 2,157 (1.6%), control 27 of 1,806 (1.5%), concurrent and eligible, primary outcome, details missing, mAb/AV confounding.
risk of mechanical ventilation, 151.4% higher, RR 2.51, p = 0.63, treatment 3 of 2,149 (0.1%), control 1 of 1,801 (0.1%).
risk of ICU admission, 319.0% higher, RR 4.19, p = 0.23, treatment 5 of 2,149 (0.2%), control 1 of 1,801 (0.1%).
time to sustained recovery, 16.0% lower, HR 0.84, p < 0.001, treatment 2,157, control 1,806, inverted to make HR<1 favor treatment, concurrent and eligible.
early sustained recovery, 23.1% lower, HR 0.77, p < 0.001, treatment 2,154, control 1,805, inverted to make HR<1 favor treatment, concurrent and eligible.
sustained alleviation, 17.4% lower, HR 0.83, p < 0.001, treatment 1,826, control 1,535, inverted to make HR<1 favor treatment, concurrent and eligible.
alleviation of all symptoms, 16.0% lower, HR 0.84, p < 0.001, treatment 2,154, control 1,805, inverted to make HR<1 favor treatment, concurrent and eligible.
first reported recovery, 13.0% lower, HR 0.87, p < 0.001, treatment 2,157, control 1,806, inverted to make HR<1 favor treatment, concurrent and eligible, primary outcome.
no recovery at 3/6/12 months, 28.0% lower, HR 0.72, p = 0.02, treatment 94 of 1,941 (4.8%), control 109 of 1,624 (6.7%), NNT 54.
risk of no recovery, 17.6% lower, RR 0.82, p = 0.001, treatment 417 of 1,848 (22.6%), control 420 of 1,533 (27.4%), NNT 21, day 365.
risk of long COVID, 36.3% lower, RR 0.64, p < 0.001, treatment 1,886, control 1,567, all symptoms combined.
risk of long COVID, 63.2% higher, RR 1.63, p = 1.00, treatment 2 of 1,507 (0.1%), control 1 of 1,230 (0.1%), ongoing/persistent, fever.
risk of long COVID, 72.7% lower, RR 0.27, p = 0.33, treatment 1 of 1,819 (0.1%), control 3 of 1,489 (0.2%), NNT 683, ongoing/persistent, cough.
risk of long COVID, 50.1% lower, RR 0.50, p = 0.04, treatment 15 of 1,886 (0.8%), control 25 of 1,567 (1.6%), NNT 125, ongoing/persistent, dyspnea.
risk of long COVID, 35.3% higher, RR 1.35, p = 0.74, treatment 5 of 1,808 (0.3%), control 3 of 1,468 (0.2%), ongoing/persistent, chest pain.
risk of long COVID, 25.2% lower, RR 0.75, p = 0.36, treatment 21 of 1,831 (1.1%), control 23 of 1,501 (1.5%), NNT 259, ongoing/persistent, smell.
risk of long COVID, 58.7% lower, RR 0.41, p = 0.42, treatment 2 of 1,821 (0.1%), control 4 of 1,503 (0.3%), NNT 640, ongoing/persistent, diarrhoea.
risk of long COVID, 70.2% lower, RR 0.30, p < 0.001, treatment 10 of 1,739 (0.6%), control 27 of 1,400 (1.9%), NNT 74, ongoing/persistent, headache.
risk of long COVID, 29.8% lower, RR 0.70, p = 0.25, treatment 23 of 1,739 (1.3%), control 27 of 1,433 (1.9%), NNT 178, ongoing/persistent, muscle ache.
risk of long COVID, 47.1% lower, RR 0.53, p = 0.03, treatment 19 of 1,724 (1.1%), control 30 of 1,441 (2.1%), NNT 102, ongoing/persistent, generally unwell.
risk of long COVID, 20.1% lower, RR 0.80, p = 0.19, treatment 66 of 1,876 (3.5%), control 69 of 1,567 (4.4%), NNT 113, ongoing/persistent, fatigue.
risk of long COVID, 42.9% lower, RR 0.57, p < 0.001, treatment 1,886, control 1,567, all symptoms combined.
risk of long COVID, 63.2% higher, RR 1.63, p = 1.00, treatment 2 of 1,507 (0.1%), control 1 of 1,230 (0.1%), ongoing/persistent, fever.
risk of long COVID, 72.7% lower, RR 0.27, p = 0.33, treatment 1 of 1,819 (0.1%), control 3 of 1,489 (0.2%), NNT 683, ongoing/persistent, cough.
risk of long COVID, 50.1% lower, RR 0.50, p = 0.04, treatment 15 of 1,886 (0.8%), control 25 of 1,567 (1.6%), NNT 125, ongoing/persistent, dyspnea.
risk of long COVID, 35.3% higher, RR 1.35, p = 0.74, treatment 5 of 1,808 (0.3%), control 3 of 1,468 (0.2%), ongoing/persistent, chest pain.
risk of long COVID, 25.2% lower, RR 0.75, p = 0.36, treatment 21 of 1,831 (1.1%), control 23 of 1,501 (1.5%), NNT 259, ongoing/persistent, smell.
risk of long COVID, 58.7% lower, RR 0.41, p = 0.42, treatment 2 of 1,821 (0.1%), control 4 of 1,503 (0.3%), NNT 640, ongoing/persistent, diarrhoea.
risk of long COVID, 70.2% lower, RR 0.30, p < 0.001, treatment 10 of 1,739 (0.6%), control 27 of 1,400 (1.9%), NNT 74, ongoing/persistent, headache.
risk of long COVID, 29.8% lower, RR 0.70, p = 0.25, treatment 23 of 1,739 (1.3%), control 27 of 1,433 (1.9%), NNT 178, ongoing/persistent, muscle ache.
risk of long COVID, 47.1% lower, RR 0.53, p = 0.03, treatment 19 of 1,724 (1.1%), control 30 of 1,441 (2.1%), NNT 102, ongoing/persistent, excluding non-prespecified fatigue, generally unwell.
risk of long COVID, 28.5% lower, RR 0.72, p < 0.001, treatment 1,513, control 1,238, adjusted per study, all symptoms combined.
risk of long COVID, 39.9% lower, RR 0.60, p = 0.01, treatment 46 of 1,435 (3.2%), control 51 of 1,136 (4.5%), relatedness (yes + unsure) 9.1% (treatment) 11.2% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, cough.
risk of long COVID, 41.6% lower, RR 0.58, p < 0.001, treatment 96 of 1,513 (6.3%), control 106 of 1,238 (8.6%), relatedness (yes + unsure) 14.4% (treatment) 18.5% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, shortness of breath.
risk of long COVID, 22.2% lower, RR 0.78, p = 0.27, treatment 39 of 1,426 (2.7%), control 36 of 1,117 (3.2%), relatedness (yes + unsure) 7.6% (treatment) 8.4% (control) , NNT 205, adjusted per study and for relatedness, moderate/major symptoms at 12 months, chest pain.
risk of long COVID, 38.0% lower, RR 0.62, p = 0.02, treatment 46 of 1,427 (3.2%), control 44 of 1,140 (3.9%), relatedness (yes + unsure) 7.7% (treatment) 10.3% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, palpitations.
risk of long COVID, 11.5% higher, RR 1.12, p = 0.52, treatment 77 of 1,403 (5.5%), control 57 of 1,131 (5.0%), relatedness (yes + unsure) 13.2% (treatment) 12.9% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, smell.
risk of long COVID, 36.4% lower, RR 0.64, p = 0.02, treatment 51 of 1,375 (3.7%), control 51 of 1,116 (4.6%), relatedness (yes + unsure) 1.1% (treatment) 1.4% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, taste.
risk of long COVID, 22.1% lower, RR 0.78, p = 0.37, treatment 26 of 1,290 (2.0%), control 25 of 1,057 (2.4%), relatedness (yes + unsure) 4.8% (treatment) 5.3% (control) , NNT 286, adjusted per study and for relatedness, moderate/major symptoms at 12 months, ear ache.
risk of long COVID, 9.6% higher, RR 1.10, p = 0.73, treatment 37 of 1,325 (2.8%), control 25 of 1,089 (2.3%), relatedness (yes + unsure) 6.7% (treatment) 7.4% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, sore throat.
risk of long COVID, 34.8% lower, RR 0.65, p = 0.12, treatment 24 of 1,308 (1.8%), control 27 of 1,077 (2.5%), relatedness (yes + unsure) 6.0% (treatment) 6.9% (control) , NNT 149, adjusted per study and for relatedness, moderate/major symptoms at 12 months, hoarse voice.
risk of long COVID, 16.6% higher, RR 1.17, p = 0.42, treatment 60 of 1,341 (4.5%), control 46 of 1,082 (4.3%), relatedness (yes + unsure) 8.3% (treatment) 7.4% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, tinnitus.
risk of long COVID, 34.7% lower, RR 0.65, p = 0.29, treatment 13 of 1,382 (0.9%), control 12 of 1,139 (1.1%), relatedness (yes + unsure) 2.2% (treatment) 3.0% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, vomiting.
risk of long COVID, 46.5% higher, RR 1.46, p = 0.12, treatment 44 of 1,439 (3.1%), control 25 of 1,175 (2.1%), relatedness (yes + unsure) 5.9% (treatment) 5.8% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, abdominal pain.
risk of long COVID, 1.6% lower, RR 0.98, p = 0.95, treatment 33 of 1,416 (2.3%), control 24 of 1,167 (2.1%), relatedness (yes + unsure) 4.4% (treatment) 5.1% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, diarrhoea.
risk of long COVID, 43.8% higher, RR 1.44, p = 0.23, treatment 30 of 1,417 (2.1%), control 17 of 1,160 (1.5%), relatedness (yes + unsure) 4.5% (treatment) 4.6% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, reduced appetite.
risk of long COVID, 89.8% higher, RR 1.90, p = 0.27, treatment 11 of 1,375 (0.8%), control 4 of 1,129 (0.4%), relatedness (yes + unsure) 1.8% (treatment) 2.2% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, weight loss.
risk of long COVID, 41.3% lower, RR 0.59, p < 0.001, treatment 89 of 1,287 (6.9%), control 94 of 973 (9.7%), relatedness (yes + unsure) 13.2% (treatment) 16.2% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, anxiety.
risk of long COVID, 38.6% lower, RR 0.61, p < 0.001, treatment 95 of 1,298 (7.3%), control 92 of 992 (9.3%), relatedness (yes + unsure) 13.7% (treatment) 17.4% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, depression.
risk of long COVID, 49.0% lower, RR 0.51, p < 0.001, treatment 129 of 1,376 (9.4%), control 143 of 1,105 (12.9%), relatedness (yes + unsure) 19.6% (treatment) 27.3% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, brain fog.
risk of long COVID, 32.9% lower, RR 0.67, p = 0.08, treatment 37 of 1,187 (3.1%), control 33 of 874 (3.8%), relatedness (yes + unsure) 7.1% (treatment) 9.2% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, confusion.
risk of long COVID, 41.9% lower, RR 0.58, p < 0.001, treatment 78 of 1,278 (6.1%), control 81 of 964 (8.4%), relatedness (yes + unsure) 12.5% (treatment) 15.7% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, headache.
risk of long COVID, 45.4% lower, RR 0.55, p = 0.001, treatment 53 of 1,254 (4.2%), control 54 of 955 (5.7%), relatedness (yes + unsure) 11.5% (treatment) 15.8% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, dizziness.
risk of long COVID, 73.5% lower, RR 0.27, p = 0.10, treatment 3 of 1,105 (0.3%), control 3 of 802 (0.4%), relatedness (yes + unsure) 0.4% (treatment) 1.1% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, fainting.
risk of long COVID, 38.7% lower, RR 0.61, p = 0.02, treatment 42 of 1,200 (3.5%), control 39 of 894 (4.4%), relatedness (yes + unsure) 7.9% (treatment) 10.7% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, numbness.
risk of long COVID, 42.1% lower, RR 0.58, p < 0.001, treatment 123 of 1,288 (9.5%), control 121 of 982 (12.3%), relatedness (yes + unsure) 13.9% (treatment) 18.5% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, sleeping problems.
risk of long COVID, 11.8% lower, RR 0.88, p = 0.37, treatment 99 of 1,180 (8.4%), control 88 of 967 (9.1%), relatedness (yes + unsure) 15.1% (treatment) 16.1% (control) , NNT 141, adjusted per study and for relatedness, moderate/major symptoms at 12 months, body pains.
risk of long COVID, 32.2% lower, RR 0.68, p < 0.001, treatment 136 of 1,220 (11.1%), control 146 of 1,034 (14.1%), relatedness (yes + unsure) 16.1% (treatment) 19.0% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, joint pains.
risk of long COVID, 30.2% lower, RR 0.70, p < 0.001, treatment 205 of 1,398 (14.7%), control 209 of 1,181 (17.7%), relatedness (yes + unsure) 24.1% (treatment) 28.3% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, fatigue.
risk of long COVID, 31.9% lower, RR 0.68, p = 0.006, treatment 91 of 1,208 (7.5%), control 89 of 997 (8.9%), relatedness (yes + unsure) 14.5% (treatment) 18.1% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, weakness.
risk of long COVID, 35.9% lower, RR 0.64, p < 0.001, treatment 99 of 1,211 (8.2%), control 107 of 1,002 (10.7%), relatedness (yes + unsure) 14.3% (treatment) 17.4% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, generally unwell.
risk of long COVID, 9.2% lower, RR 0.91, p = 0.81, treatment 16 of 1,066 (1.5%), control 11 of 855 (1.3%), relatedness (yes + unsure) 3.0% (treatment) 3.8% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, fever.
risk of long COVID, 42.4% lower, RR 0.58, p = 0.21, treatment 10 of 1,065 (0.9%), control 11 of 853 (1.3%), relatedness (yes + unsure) 3.0% (treatment) 3.7% (control) , adjusted per study and for relatedness, moderate/major symptoms at 12 months, rashes.
risk of long COVID, 10.0% lower, RR 0.90, p = 0.62, treatment 47 of 1,090 (4.3%), control 41 of 873 (4.7%), NNT 260, adjusted per study, moderate/major symptoms at 12 months, other.
Hazan (B), 7/7/2021, retrospective, USA, preprint, 7 authors, dosage 12mg days 1, 4, 8, this trial uses multiple treatments in the treatment arm (combined with doxycycline, zinc, vitamin D, vitamin C) - results of individual treatments may vary, see notes, trial NCT04949230 (history), excluded in exclusion analyses: study uses a synthetic control arm.
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 risk of death, 86.2% lower, RR 0.14, p = 0.04, NNT 6.9, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 93.5% lower, RR 0.07, p = 0.001, NNT 3.3, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), primary outcome.
Huvemek, 3/25/2021, Double Blind Randomized Controlled Trial, Bulgaria, preprint, 1 author, average treatment delay 7.0 days, dosage 400μg/kg days 1-3, trial EudraCT2020-002091-12.
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 risk of no improvement, 31.6% lower, RR 0.68, p = 0.28, treatment 13 of 50 (26.0%), control 19 of 50 (38.0%), NNT 8.3, patients with improvement on WHO scale, day 7.
risk of no improvement, 31.8% lower, RR 0.68, p = 0.21, treatment 15 of 50 (30.0%), control 22 of 50 (44.0%), NNT 7.1, patients with improvement on WHO scale, day 6.
risk of no improvement, 36.0% lower, RR 0.64, p = 0.10, treatment 16 of 50 (32.0%), control 25 of 50 (50.0%), NNT 5.6, patients with improvement on WHO scale, day 5.
risk of no improvement, 34.5% lower, RR 0.66, p = 0.07, treatment 19 of 50 (38.0%), control 29 of 50 (58.0%), NNT 5.0, patients with improvement on WHO scale, day 4.
Jamir, 12/13/2021, retrospective, India, peer-reviewed, 6 authors, study period June 2020 - October 2020, dosage not specified.
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 risk of death, 53.0% higher, RR 1.53, p = 0.13, treatment 32 of 76 (42.1%), control 69 of 190 (36.3%), adjusted per study, multivariable Cox regression.
Khan, 9/24/2020, retrospective, Bangladesh, peer-reviewed, median age 35.0, 8 authors, dosage 12mg single dose.
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 risk of death, 87.1% lower, RR 0.13, p = 0.02, treatment 1 of 115 (0.9%), control 9 of 133 (6.8%), NNT 17.
risk of ICU admission, 89.5% lower, RR 0.11, p = 0.007, treatment 1 of 115 (0.9%), control 11 of 133 (8.3%), NNT 14.
risk of progression, 83.5% lower, RR 0.17, p < 0.001, treatment 3 of 115 (2.6%), control 21 of 133 (15.8%), NNT 7.6.
risk of no recovery, 87.1% lower, RR 0.13, p = 0.02, treatment 1 of 115 (0.9%), control 9 of 133 (6.8%), NNT 17.
hospitalization time, 40.0% lower, relative time 0.60, p < 0.001, treatment 115, control 133.
time to viral-, 73.3% lower, relative time 0.27, p < 0.001, treatment 115, control 133.
Kishoria, 8/31/2020, Randomized Controlled Trial, India, peer-reviewed, 7 authors, dosage 12mg single dose, excluded in exclusion analyses: excessive unadjusted differences between groups.
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 risk of no hospital discharge, 7.5% higher, RR 1.08, p = 1.00, treatment 11 of 19 (57.9%), control 7 of 13 (53.8%).
risk of no viral clearance, 7.5% higher, RR 1.08, p = 1.00, treatment 11 of 19 (57.9%), control 7 of 13 (53.8%), day 3, primary outcome.
risk of no viral clearance, 220.0% higher, RR 3.20, p = 0.45, treatment 1 of 5 (20.0%), control 0 of 6 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), day 5.
Lim, 11/3/2021, Randomized Controlled Trial, Malaysia, peer-reviewed, 26 authors, study period 31 May, 2021 - 9 October, 2021, average treatment delay 5.1 days, dosage 400μg/kg days 1-5, trial NCT04920942 (history) (I-TECH).
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 risk of death, 69.0% lower, RR 0.31, p = 0.09, treatment 3 of 241 (1.2%), control 10 of 249 (4.0%), NNT 36, described as similar by authors.
risk of death, 69.8% lower, RR 0.30, p = 0.09, treatment 3 of 247 (1.2%), control 10 of 249 (4.0%), NNT 36, ITT.
risk of death, 75.2% lower, RR 0.25, p = 0.02, treatment 3 of 52 (5.8%), control 10 of 43 (23.3%), NNT 5.7, among patients progressing to severe cases (mostly before treatment ended).
risk of mechanical ventilation, 59.0% lower, RR 0.41, p = 0.17, treatment 4 of 241 (1.7%), control 10 of 249 (4.0%), NNT 42.
risk of mechanical ventilation, 59.7% lower, RR 0.40, p = 0.17, treatment 4 of 247 (1.6%), control 10 of 249 (4.0%), NNT 42, ITT.
risk of ICU admission, 22.0% lower, RR 0.78, p = 0.79, treatment 6 of 241 (2.5%), control 8 of 249 (3.2%), NNT 138.
risk of ICU admission, 24.4% lower, RR 0.76, p = 0.79, treatment 6 of 247 (2.4%), control 8 of 249 (3.2%), NNT 128, ITT.
risk of progression, 31.1% lower, RR 0.69, p = 0.29, treatment 14 of 241 (5.8%), control 21 of 249 (8.4%), NNT 38, death/IMV/NIV/high flow (WHO severe cases).
risk of progression, 25.0% higher, RR 1.25, p = 0.25, treatment 52 of 241 (21.6%), control 43 of 249 (17.3%), post-hoc primary outcome, post-hoc primary specific to this group.
hospitalization time, 5.5% higher, relative time 1.05, p = 0.38, treatment 241, control 249.
risk of no recovery, 2.5% higher, RR 1.02, p = 0.86, treatment 116 of 241 (48.1%), control 116 of 247 (47.0%), day 5.
Lima-Morales, 2/10/2021, prospective, Mexico, peer-reviewed, 10 authors, average treatment delay 7.2 days, dosage 12mg single dose, this trial uses multiple treatments in the treatment arm (combined with azithromycin, montelukast, and aspirin) - results of individual treatments may vary.
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 risk of death, 77.7% lower, RR 0.22, p < 0.001, treatment 15 of 481 (3.1%), control 52 of 287 (18.1%), NNT 6.7, adjusted per study, odds ratio converted to relative risk, multivariate.
risk of mechanical ventilation, 51.9% lower, RR 0.48, p = 0.15, treatment 8 of 434 (1.8%), control 11 of 287 (3.8%), NNT 50.
risk of hospitalization, 67.4% lower, RR 0.33, p < 0.001, treatment 44 of 481 (9.1%), control 89 of 287 (31.0%), NNT 4.6, adjusted per study, odds ratio converted to relative risk, multivariate.
risk of no recovery, 58.6% lower, RR 0.41, p < 0.001, treatment 75 of 481 (15.6%), control 118 of 287 (41.1%), NNT 3.9, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, recovery at day 14 after symptoms, multivariate.
Llenas-García, 5/10/2023, retrospective, Spain, peer-reviewed, 11 authors, study period 23 February, 2020 - 14 March, 2021, average treatment delay 6.0 days, dosage 200μg/kg single dose.
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 risk of death, 16.7% lower, RR 0.83, p = 0.82, treatment 10 of 96 (10.4%), control 12 of 96 (12.5%), NNT 48.
risk of oxygen therapy, 18.4% lower, RR 0.82, p = 0.37, treatment 31 of 96 (32.3%), control 38 of 96 (39.6%), NNT 14.
risk of progression, 23.0% lower, OR 0.77, p = 0.52, treatment 96, control 96, adjusted per study, in-hospital mortality or respiratory support, multivariable, primary outcome, RR approximated with OR.
risk of ICU admission, 4.0% higher, OR 1.04, p = 0.92, treatment 96, control 96, adjusted per study, multivariable, RR approximated with OR.
Munir, 4/21/2023, retrospective, Pakistan, peer-reviewed, 3 authors, study period March 2021 - March 2022, dosage not specified.
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 risk of death, 48.2% lower, OR 0.52, p = 0.13, treatment 92, control 908, adjusted per study, multivariable, RR approximated with OR.
Mustafa, 12/29/2021, retrospective, Pakistan, peer-reviewed, 7 authors, dosage varies, excluded in exclusion analyses: unadjusted results with no group details.
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 risk of death, 63.7% lower, RR 0.36, p = 0.09, treatment 3 of 73 (4.1%), control 42 of 371 (11.3%), NNT 14.
Naggie, 6/12/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 28 authors, study period 23 June, 2021 - 4 February, 2022, average treatment delay 6.0 days, dosage 600μg/kg days 1-6, trial NCT04885530 (history) (ACTIV-6).
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 risk of death, 202.3% higher, RR 3.02, p = 0.49, treatment 1 of 708 (0.1%), control 0 of 724 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), 600µg/kg, day 28.
risk of death, 194.7% higher, RR 2.95, p = 1.00, treatment 1 of 817 (0.1%), control 0 of 774 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), 400µg/kg, day 28.
risk of death/hospitalization, 256.9% higher, RR 3.57, p = 0.11, treatment 7 of 708 (1.0%), control 2 of 722 (0.3%), 600µg/kg, day 28.
risk of hospitalization, 5.3% higher, RR 1.05, p = 1.00, treatment 10 of 817 (1.2%), control 9 of 774 (1.2%), 400µg/kg, day 28.
risk of progression, 68.4% lower, RR 0.32, p = 0.36, treatment 1 of 817 (0.1%), control 3 of 774 (0.4%), NNT 377, 400µg/kg, aggravated C19 pneumonia, eTable 2.
risk of progression, 3.0% lower, RR 0.97, p = 0.53, treatment 39 of 708 (5.5%), control 42 of 722 (5.8%), NNT 324, adjusted per study, urgent or emergency care visits, hospitalizations, or death, 600µg/kg.
risk of progression, 20.0% higher, RR 1.20, p = 0.32, treatment 32 of 817 (3.9%), control 28 of 774 (3.6%), adjusted per study, urgent or emergency care visits, hospitalizations, or death, 400µg/kg.
clinical progression, 29.0% higher, OR 1.29, p = 0.39, treatment 708, control 724, mid-recovery, 600µg/kg, day 7, RR approximated with OR.
clinical progression, 150.0% higher, OR 2.50, p = 0.08, treatment 708, control 724, 600µg/kg, day 14, RR approximated with OR.
clinical progression, 150.0% higher, OR 2.50, p = 0.09, treatment 708, control 724, 600µg/kg, day 28, RR approximated with OR.
clinical progression, 24.0% lower, OR 0.76, p = 0.07, treatment 817, control 774, mid-recovery, 400µg/kg, day 7, RR approximated with OR.
clinical progression, 27.0% lower, OR 0.73, p = 0.05, treatment 817, control 774, 400µg/kg, day 14, RR approximated with OR.
clinical progression, 10.0% lower, OR 0.90, p = 0.62, treatment 817, control 774, 400µg/kg, day 28, RR approximated with OR.
time to recovery, 2.0% lower, HR 0.98, p = 0.71, treatment 708, control 724, inverted to make HR<1 favor treatment, 600µg/kg, post-hoc primary outcome.
risk of limited activity, 30.5% lower, RR 0.70, p = 0.14, treatment 30 of 805 (3.7%), control 41 of 765 (5.4%), NNT 61, eFigure 2, 400µg/kg, day 14.
time to recovery, 6.5% lower, HR 0.93, p = 0.22, treatment 817, control 774, inverted to make HR<1 favor treatment, 400µg/kg, post-hoc primary outcome.
time to recovery, 46.2% lower, HR 0.54, p = 0.02, treatment 39, control 51, inverted to make HR<1 favor treatment, 400µg/kg, severe, recovery.
Ochoa-Jaramillo, 10/21/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Colombia, peer-reviewed, 8 authors, study period 10 December, 2020 - 9 December, 2021, average treatment delay 8.8 days, dosage 400μg/kg single dose, trial NCT04602507 (history).
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 risk of death, 57.0% lower, HR 0.43, p = 0.35, treatment 2 of 37 (5.4%), control 4 of 38 (10.5%), NNT 20, Cox proportional hazards.
risk of mechanical ventilation, 34.0% higher, HR 1.34, p = 0.62, treatment 7 of 37 (18.9%), control 5 of 38 (13.2%), Cox proportional hazards.
risk of ICU admission, 37.0% higher, HR 1.37, p = 0.52, treatment 8 of 37 (21.6%), control 6 of 38 (15.8%), Cox proportional hazards, primary outcome.
Okumuş, 1/12/2021, Double Blind Randomized Controlled Trial, Turkey, peer-reviewed, 15 authors, study period May 2020 - September 2020, dosage 200μg/kg days 1-5, 36-50kg - 9mg, 51-65kg - 12mg, 66-79kg - 15mg, >80kg 200μg/kg, trial NCT04646109 (history).
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 risk of death, 33.3% lower, RR 0.67, p = 0.55, treatment 6 of 30 (20.0%), control 9 of 30 (30.0%), NNT 10.
risk of no improvement at day 10, 42.9% lower, RR 0.57, p = 0.18, treatment 8 of 30 (26.7%), control 14 of 30 (46.7%), NNT 5.0.
risk of no improvement at day 5, 15.8% lower, RR 0.84, p = 0.60, treatment 16 of 30 (53.3%), control 19 of 30 (63.3%), NNT 10, primary outcome.
risk of no viral clearance, 80.0% lower, RR 0.20, p = 0.02, treatment 2 of 16 (12.5%), control 5 of 8 (62.5%), NNT 2.0, day 10.
Osati, 7/16/2023, retrospective, Tanzania, preprint, median age 60.0, 22 authors, study period 26 March, 2021 - 30 July, 2022, dosage not specified.
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 risk of death, 31.5% lower, OR 0.68, p = 0.02, treatment 448, control 849, adjusted per study, inverted to make OR<1 favor treatment, multivariable, RR approximated with OR.
Ozer, 11/23/2021, prospective, USA, peer-reviewed, 12 authors, dosage 200μg/kg days 1, 3.
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 risk of death, 75.0% lower, RR 0.25, p = 0.09, treatment 2 of 60 (3.3%), control 8 of 60 (13.3%), NNT 10.0, PSM.
risk of mechanical ventilation, 12.6% lower, RR 0.87, p = 0.20, treatment 3 of 60 (5.0%), control 2 of 60 (3.3%), adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable.
ventilation time, 83.3% lower, relative time 0.17, p = 0.002, treatment 60, control 60.
risk of ICU admission, 48.7% lower, RR 0.51, p = 0.42, treatment 6 of 60 (10.0%), control 3 of 60 (5.0%), adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable.
ICU time, 70.6% lower, relative time 0.29, p < 0.001, treatment 60, control 60.
hospitalization time, 9.0% higher, relative time 1.09, p = 0.09, treatment 60, control 60, adjusted per study, propensity score matching, multivariable.
Podder, 9/3/2020, Randomized Controlled Trial, Bangladesh, peer-reviewed, 4 authors, study period 1 May, 2020 - 31 July, 2020, average treatment delay 7.0 days, dosage 200μg/kg single dose.
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 recovery time from enrollment, 16.1% lower, relative time 0.84, p = 0.34, treatment 32, control 30.
Pott-Junior, 3/9/2021, Randomized Controlled Trial, Brazil, peer-reviewed, 10 authors, study period 1 July, 2020 - 1 December, 2020, average treatment delay 8.0 days, dosage 200μg/kg single dose, dose varies in three arms 100, 200, 400μg/kg, censored, see notes, trial NCT04431466 (history).
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 risk of mechanical ventilation, 85.2% lower, RR 0.15, p = 0.25, treatment 1 of 27 (3.7%), control 1 of 4 (25.0%), NNT 4.7.
risk of ICU admission, 85.2% lower, RR 0.15, p = 0.25, treatment 1 of 27 (3.7%), control 1 of 4 (25.0%), NNT 4.7.
relative improvement in Ct value, 0.8% better, RR 0.99, p = 1.00, treatment 27, control 3.
risk of no viral clearance, 11.1% higher, RR 1.11, p = 1.00, treatment 10 of 27 (37.0%), control 1 of 3 (33.3%), primary outcome.
Qadeer, 8/31/2022, prospective, Pakistan, peer-reviewed, median age 55.4, 6 authors, study period 1 November, 2020 - 30 May, 2021, dosage 12mg days 1-5, excluded in exclusion analyses: minimal baseline details provided.
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 risk of no viral clearance, 58.3% lower, RR 0.42, p < 0.001, treatment 35 of 105 (33.3%), control 84 of 105 (80.0%), NNT 2.1, mid-recovery, day 10.
risk of no viral clearance, 20.0% lower, RR 0.80, p < 0.001, treatment 84 of 105 (80.0%), control 105 of 105 (100.0%), NNT 5.0, day 7.
risk of no viral clearance, 98.6% lower, RR 0.01, p < 0.001, treatment 0 of 105 (0.0%), control 35 of 105 (33.3%), NNT 3.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 14.
Rajter, 10/13/2020, retrospective, propensity score matching, USA, peer-reviewed, 6 authors, dosage 200μg/kg single dose.
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 risk of death, 46.0% lower, RR 0.54, p = 0.045, treatment 13 of 98 (13.3%), control 24 of 98 (24.5%), NNT 8.9, adjusted per study, odds ratio converted to relative risk, PSM.
risk of death, 66.9% lower, RR 0.33, p = 0.03, treatment 26 of 173 (15.0%), control 27 of 107 (25.2%), NNT 9.8, adjusted per study, odds ratio converted to relative risk, multivariate, primary outcome.
risk of mechanical ventilation, 63.6% lower, RR 0.36, p = 0.10, treatment 4 of 98 (4.1%), control 11 of 98 (11.2%), NNT 14, matched cohort excluding intubated at baseline.
Ravikirti, 4/6/2022, retrospective, India, preprint, 7 authors, study period 1 April, 2021 - 15 May, 2021, dosage varies, excluded in exclusion analyses: exclusion of patients in less severe condition, data/analysis concerns.
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 risk of death, 2.8% lower, RR 0.97, p = 0.82, treatment 53 of 171 (31.0%), control 254 of 794 (32.0%), NNT 100, odds ratio converted to relative risk.
Ravikirti (B), 1/9/2021, Double Blind Randomized Controlled Trial, India, peer-reviewed, 11 authors, study period 1 August, 2020 - 31 October, 2020, average treatment delay 6.1 days, dosage 12mg days 1, 2.
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 risk of death, 88.7% lower, RR 0.11, p = 0.12, treatment 0 of 55 (0.0%), control 4 of 57 (7.0%), NNT 14, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of mechanical ventilation, 79.3% lower, RR 0.21, p = 0.10, treatment 1 of 55 (1.8%), control 5 of 57 (8.8%), NNT 14.
risk of ICU admission, 13.6% lower, RR 0.86, p = 0.80, treatment 5 of 55 (9.1%), control 6 of 57 (10.5%), NNT 70.
risk of no hospital discharge, 88.7% lower, RR 0.11, p = 0.12, treatment 0 of 55 (0.0%), control 4 of 57 (7.0%), NNT 14, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no viral clearance, 11.6% higher, RR 1.12, p = 0.35, treatment 42 of 55 (76.4%), control 39 of 57 (68.4%).
Rezai (B), 6/16/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, mean age 53.8, 29 authors, study period 19 February, 2021 - 14 August, 2021, average treatment delay 7.18 days, dosage 400μg/kg days 1-3, trial IRCT20111224008507N5, excluded in exclusion analyses: multiple critical issues, see study page.
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 risk of death, 30.8% lower, RR 0.69, p = 0.36, treatment 13 of 311 (4.2%), control 18 of 298 (6.0%), NNT 54.
risk of mechanical ventilation, 50.0% lower, RR 0.50, p = 0.07, treatment 311, control 298.
risk of ICU admission, 16.0% lower, RR 0.84, p = 0.47, treatment 311, control 298.
hospitalization time, 11.5% higher, relative time 1.11, p = 0.009, treatment mean 7.98 (±4.4) n=311, control mean 7.16 (±3.2) n=298.
deterioration, 12.7% higher, RR 1.13, p = 0.74, treatment 20 of 311 (6.4%), control 17 of 298 (5.7%).
risk of no recovery, 24.2% lower, RR 0.76, p = 0.02, treatment 311, control 298, inverted to make RR<1 favor treatment, post-hoc primary outcome.
risk of no recovery, 64.0% lower, RR 0.36, p = 0.06, treatment 5 of 145 (3.4%), control 10 of 105 (9.5%), NNT 16, day 7, cough.
risk of no recovery, 76.0% lower, RR 0.24, p = 0.38, day 7, tachypnea.
Rezk, 10/30/2021, prospective, Egypt, peer-reviewed, 4 authors, dosage 36mg days 1, 3, 6.
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 risk of death, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 160 (0.0%), control 2 of 160 (1.2%), NNT 80, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of progression, 55.6% lower, RR 0.44, p = 0.06, treatment 8 of 160 (5.0%), control 18 of 160 (11.2%), NNT 16, 2 weeks, including deaths.
risk of no recovery, 33.4% lower, RR 0.67, p = 0.27, treatment 14 of 145 (9.7%), control 20 of 138 (14.5%), NNT 21, 4 weeks, more patients were lost to followup in the control group.
time to viral-, 27.3% lower, relative time 0.73, p = 0.01, treatment 160, control 160.
Sarojvisut, 12/12/2022, Randomized Controlled Trial, Thailand, peer-reviewed, 8 authors, study period 1 October, 2021 - 31 May, 2022, dosage 400μg/kg days 1-5, trial TCTR20220427005, excluded in exclusion analyses: many critical issues including missing primary outcome results, non-reproducible values, and protocol violations.
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 risk of death, 1.9% higher, RR 1.02, p = 1.00, treatment 1 of 157 (0.6%), control 1 of 160 (0.6%), all-cause in-hospital mortality.
risk of ICU admission, 103.8% higher, RR 2.04, p = 0.62, treatment 2 of 157 (1.3%), control 1 of 160 (0.6%).
risk of no improvement, 103.8% higher, RR 2.04, p = 0.62, treatment 2 of 157 (1.3%), control 1 of 160 (0.6%), failure to improve ≥2 points on WHO ordinal scale.
recovery time, 3.8% lower, relative time 0.96, p = 0.63, treatment 157, control 160.
Shahbaznejad, 1/19/2021, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 8 authors, average treatment delay 6.29 days, dosage 200μg/kg single dose, trial IRCT20111224008507N3.
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 risk of death, 197.1% higher, RR 2.97, p = 1.00, treatment 1 of 35 (2.9%), control 0 of 34 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), patient died within 24 hours of admission.
risk of mechanical ventilation, 94.3% higher, RR 1.94, p = 1.00, treatment 2 of 35 (5.7%), control 1 of 34 (2.9%).
recovery time, 31.6% lower, relative time 0.68, p = 0.048, treatment 35, control 34, duration of dsypnea.
recovery time, 19.2% lower, relative time 0.81, p = 0.02, treatment 35, control 34, duration of all symptoms, primary outcome.
hospitalization time, 15.5% lower, relative time 0.85, p = 0.02, treatment 35, control 34.
Shimizu, 12/31/2021, retrospective, Japan, peer-reviewed, 11 authors, study period December 2020 - May 2021, dosage 200μg/kg days 1, 14.
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 risk of death, 99.9% lower, HR 0.001, p < 0.001, treatment 0 of 39 (0.0%), control 8 of 49 (16.3%), NNT 6.1, adjusted per study, Cox proportional hazard regression.
ventilator free days, 47.9% lower, OR 0.52, p = 0.03, treatment 39, control 49, adjusted per study, inverted to make OR<1 favor treatment, proportional odds logistic regression, primary outcome, RR approximated with OR.
ventilation time, 38.5% lower, relative time 0.62, p < 0.001, treatment 39, control 49.
ICU free days, 42.8% lower, OR 0.57, p = 0.06, treatment 39, control 49, adjusted per study, inverted to make OR<1 favor treatment, proportional odds logistic regression, RR approximated with OR.
ICU time, 37.5% lower, relative time 0.62, p < 0.001, treatment 39, control 49.
GI complications while ventilated, 77.9% lower, RR 0.22, p = 0.03, treatment 39, control 49, adjusted per study, Cox proportional hazard regression.
Soto, 3/2/2022, retrospective, Peru, peer-reviewed, median age 58.0, 10 authors, study period April 2020 - August 2020, dosage not specified, excluded in exclusion analyses: substantial unadjusted confounding by indication likely; substantial confounding by time possible due to significant changes in SOC and treatment propensity near the start of the pandemic.
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 risk of death, 41.0% higher, HR 1.41, p = 0.001, treatment 280 of 484 (57.9%), control 374 of 934 (40.0%), adjusted per study, multivariable.
Soto-Becerra, 10/8/2020, retrospective, database analysis, Peru, preprint, median age 59.4, 4 authors, study period 1 April, 2020 - 19 July, 2020, dosage 200μg/kg single dose, excluded in exclusion analyses: substantial unadjusted confounding by indication likely; includes PCR+ patients that may be asymptomatic for COVID-19 but in hospital for other reasons.
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 risk of death, 17.1% lower, HR 0.83, p = 0.01, treatment 92 of 203 (45.3%), control 1,438 of 2,630 (54.7%), NNT 11, IVM vs. control day 43 (last day available) weighted KM from figure 3, per the pre-specified rules the last available day results have priority, day 43.
risk of death, 39.0% higher, HR 1.39, p = 0.16, treatment 47 of 203 (23.2%), control 401 of 2,630 (15.2%), adjusted per study, Table 2, IVM wHR, day 30, primary outcome.
Spoorthi, 11/14/2020, prospective, India, peer-reviewed, 2 authors, dosage not specified, this trial uses multiple treatments in the treatment arm (combined with doxycycline) - results of individual treatments may vary.
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 recovery time, 21.1% lower, relative time 0.79, p = 0.03, treatment 50, control 50.
hospitalization time, 15.5% lower, relative time 0.84, p = 0.01, treatment 50, control 50.
Thairu, 2/25/2022, retrospective, Nigeria, peer-reviewed, mean age 41.7, 6 authors, study period April 2021 - November 2021, dosage 200μg/kg days 1-5, excluded in exclusion analyses: significant confounding by time possible due to separation of groups in different time periods.
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 risk of death, 87.9% lower, RR 0.12, p = 0.12, treatment 0 of 21 (0.0%), control 4 of 26 (15.4%), NNT 6.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), propensity score matching.
risk of death, 93.0% lower, RR 0.07, p = 0.007, treatment 0 of 61 (0.0%), control 4 of 26 (15.4%), NNT 6.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), all patients.
time to discharge, 54.6% lower, relative time 0.45, p < 0.001, treatment 61, control 26, propensity score matching.
risk of no viral clearance, 94.8% lower, RR 0.05, p = 0.001, treatment 0 of 21 (0.0%), control 10 of 26 (38.5%), NNT 2.6, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), propensity score matching, day 21.
risk of no viral clearance, 95.2% lower, RR 0.05, p < 0.001, treatment 1 of 21 (4.8%), control 26 of 26 (100.0%), NNT 1.1, propensity score matching, day 14.
risk of no viral clearance, 28.6% lower, RR 0.71, p = 0.005, treatment 15 of 21 (71.4%), control 26 of 26 (100.0%), NNT 3.5, propensity score matching, day 5.
Varnaseri, 4/30/2024, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 14 authors, study period July 2020 - June 2021, dosage 14mg bid days 1-3.
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 risk of mechanical ventilation, 81.8% lower, RR 0.18, p = 0.02, treatment 2 of 55 (3.6%), control 11 of 55 (20.0%), NNT 6.1.
risk of ICU admission, 83.3% lower, RR 0.17, p < 0.001, treatment 3 of 55 (5.5%), control 18 of 55 (32.7%), NNT 3.7.
hospitalization time, 33.3% lower, relative time 0.67, p < 0.001, treatment 55, control 55.
recovery time, 28.4% lower, relative time 0.72, p < 0.001, treatment 55, control 55, all symptoms combined.
recovery time, 25.0% lower, relative time 0.75, p < 0.001, treatment 55, control 55, dyspnea.
recovery time, 25.0% lower, relative time 0.75, p < 0.001, treatment 55, control 55, fever.
recovery time, 25.0% lower, relative time 0.75, p < 0.001, treatment 55, control 55, shivering.
recovery time, 40.0% lower, relative time 0.60, p < 0.001, treatment 55, control 55, cough.
recovery time, 37.5% lower, relative time 0.62, p = 0.09, treatment 55, control 55, sore throat.
recovery time, 25.0% lower, relative time 0.75, p = 0.01, treatment 55, control 55, nausea.
recovery time, 37.5% lower, relative time 0.62, p = 0.005, treatment 55, control 55, diarrhea.
recovery time, 40.0% lower, relative time 0.60, p < 0.001, treatment 55, control 55, myalgia.
Wada, 5/22/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Japan, peer-reviewed, 26 authors, study period August 2020 - October 2021, average treatment delay 6.6 days, dosage 200μg/kg single dose, trial NCT04703205 (history).
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 risk of progression, 19.0% lower, RR 0.81, p = 0.46, treatment 19 of 106 (17.9%), control 23 of 106 (21.7%), NNT 26, adjusted per study, odds ratio converted to relative risk.
risk of progression, 27.1% lower, RR 0.73, p = 0.47, treatment 7 of 28 (25.0%), control 9 of 29 (31.0%), NNT 17, adjusted per study, odds ratio converted to relative risk, pneumonia for patients w/o pneumonia at baseline.
risk of oxygen therapy, 14.3% higher, RR 1.14, p = 0.46, treatment 22 of 106 (20.8%), control 19 of 106 (17.9%), adjusted per study, odds ratio converted to relative risk.
improvement, 23.5% worse, OR 1.23, p = 0.61, treatment 106, control 106, adjusted per study, inverted to make OR<1 favor treatment, RR approximated with OR.
risk of no recovery, 60.0% lower, RR 0.40, p = 0.17, treatment 4 of 107 (3.7%), control 10 of 107 (9.3%), NNT 18, day 15, dyspnea.
risk of no recovery, 20.0% lower, RR 0.80, p = 1.00, treatment 4 of 107 (3.7%), control 5 of 107 (4.7%), NNT 107, day 15, headache.
risk of no recovery, no change, RR 1.00, p = 1.00, treatment 7 of 107 (6.5%), control 7 of 107 (6.5%), day 15, sore throat.
risk of no recovery, 18.2% lower, RR 0.82, p = 0.81, treatment 9 of 107 (8.4%), control 11 of 107 (10.3%), NNT 53, day 15, nasal discharge.
risk of no recovery, 3.8% higher, RR 1.04, p = 1.00, treatment 27 of 107 (25.2%), control 26 of 107 (24.3%), day 15, cough.
risk of no recovery, no change, RR 1.00, p = 1.00, treatment 18 of 107 (16.8%), control 18 of 107 (16.8%), day 15, sputum.
risk of no recovery, 20.0% lower, RR 0.80, p = 1.00, treatment 4 of 107 (3.7%), control 5 of 107 (4.7%), NNT 107, day 15, diarhhea.
risk of no recovery, 66.7% higher, RR 1.67, p = 0.72, treatment 5 of 107 (4.7%), control 3 of 107 (2.8%), day 15, myalgia.
risk of no recovery, 1000.0% higher, RR 11.00, p = 0.06, treatment 5 of 107 (4.7%), control 0 of 107 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), day 15, arthralgia.
risk of no viral clearance, 4.2% higher, HR 1.04, p = 0.79, treatment 106, control 106, inverted to make HR<1 favor treatment, Kaplan-Meier, primary outcome.
Zubair, 1/18/2022, retrospective, Pakistan, peer-reviewed, 8 authors, study period October 2020 - February 2021, dosage 12mg single dose, excluded in exclusion analyses: substantial unadjusted confounding by indication likely; unadjusted results with no group details.
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 risk of death, 9.0% higher, RR 1.09, p = 1.00, treatment 5 of 90 (5.6%), control 5 of 98 (5.1%), unadjusted.
hospitalization time, 8.0% higher, relative time 1.08, p = 0.40, treatment 90, control 98, unadjusted, Table 3, mean number of days.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Alam, 12/15/2020, prospective, Bangladesh, peer-reviewed, 13 authors, dosage 12mg monthly.
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 risk of case, 90.6% lower, RR 0.09, p < 0.001, treatment 4 of 58 (6.9%), control 44 of 60 (73.3%), NNT 1.5.
Behera (B), 2/15/2021, prospective, India, peer-reviewed, 14 authors, dosage 300μg/kg days 1, 4.
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 risk of case, 83.0% lower, RR 0.17, p < 0.001, treatment 45 of 2,199 (2.0%), control 133 of 1,147 (11.6%), NNT 10, two doses, primary outcome.
Behera, 11/3/2020, retrospective, India, peer-reviewed, 13 authors, dosage 300μg/kg days 1, 4.
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 risk of case, 53.8% lower, RR 0.46, p < 0.001, treatment 41 of 117 (35.0%), control 145 of 255 (56.9%), NNT 4.6, adjusted per study, odds ratio converted to relative risk, model 2 2+ doses conditional logistic regression.
Bernigaud, 11/28/2020, retrospective, France, peer-reviewed, 12 authors, dosage 200μg/kg days 1, 8, 15, 400μg/kg days 1, 8, 15, two different dosages.
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 risk of death, 99.4% lower, RR 0.006, p = 0.08, treatment 0 of 69 (0.0%), control 150 of 3,062 (4.9%), NNT 20, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of case, 55.1% lower, RR 0.45, p = 0.01, treatment 7 of 69 (10.1%), control 692 of 3,062 (22.6%), NNT 8.0.
Carvallo, 11/17/2020, prospective, Argentina, peer-reviewed, 4 authors, dosage 12mg weekly, this trial uses multiple treatments in the treatment arm (combined with iota-carrageenan) - results of individual treatments may vary, see notes, excluded in exclusion analyses: concern about potential data issues.
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 risk of case, 99.9% lower, RR 0.001, p < 0.001, treatment 0 of 788 (0.0%), control 237 of 407 (58.2%), NNT 1.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Carvallo (B), 10/19/2020, prospective, Argentina, preprint, 1 author, dosage 1mg days 1-14, this trial uses multiple treatments in the treatment arm (combined with iota-carrageenan) - results of individual treatments may vary, see notes, trial NCT04425850 (history), excluded in exclusion analyses: concern about potential data issues.
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 risk of case, 96.3% lower, RR 0.04, p < 0.001, treatment 0 of 131 (0.0%), control 11 of 98 (11.2%), NNT 8.9, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Chahla (B), 1/11/2021, Randomized Controlled Trial, Argentina, peer-reviewed, 11 authors, study period 15 October, 2020 - 31 December, 2020, dosage 12mg weekly, this trial uses multiple treatments in the treatment arm (combined with iota-carrageenan) - results of individual treatments may vary, trial NCT04701710 (history).
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 risk of moderate/severe case, 95.2% lower, RR 0.05, p = 0.002, treatment 0 of 117 (0.0%), control 10 of 117 (8.5%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), moderate/severe COVID-19.
risk of case, 84.0% lower, RR 0.16, p = 0.004, treatment 4 of 117 (3.4%), control 25 of 117 (21.4%), NNT 5.6, adjusted per study, odds ratio converted to relative risk, all cases, primary outcome.
Desort-Henin, 1/5/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Bulgaria, preprint, 5 authors, study period March 2022 - October 2022, dosage 200μg/kg day 1, 100μg/kg days 2-28, trial NCT05305560 (history) (SAIVE).
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 risk of high viral load, 96.0% lower, RR 0.04, p < 0.001, treatment 4 of 200 (2.0%), control 99 of 199 (49.7%), NNT 2.1.
risk of case, 71.6% lower, RR 0.28, p < 0.001, treatment 30 of 200 (15.0%), control 105 of 199 (52.8%), NNT 2.6, primary outcome.
Hellwig, 11/28/2020, retrospective, ecological study, multiple countries, peer-reviewed, 2 authors, dosage 200μg/kg, dose varied, typically 150-200μg/kg, excluded in exclusion analyses: not a typical trial, analysis of African countries that used or did not use ivermectin prophylaxis for parasitic infections.
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 risk of case, 78.0% lower, RR 0.22, p < 0.02, African countries, PCTI vs. no PCT, relative cases per capita.
IVERCOR PREP, 12/20/2020, retrospective, Argentina, preprint, 1 author, dosage 12mg weekly, excluded in exclusion analyses: minimal details provided.
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 risk of case, 73.4% lower, RR 0.27, p < 0.001, treatment 13 of 389 (3.3%), control 61 of 486 (12.6%), NNT 11.
Kerr, 12/11/2021, retrospective, propensity score matching, Brazil, peer-reviewed, 9 authors, study period July 2020 - December 2020, dosage 200μg/kg days 1, 2, 16, 17, 0.2mg/kg/day for 2 days every 15 days.
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 risk of death, 70.0% lower, RR 0.30, p < 0.001, treatment 25 of 3,034 (0.8%), control 79 of 3,034 (2.6%), NNT 56, adjusted per study, multivariate linear regression, propensity score matching.
risk of hospitalization, 67.0% lower, RR 0.33, p < 0.001, treatment 44 of 3,034 (1.5%), control 99 of 3,034 (3.3%), adjusted per study, multivariate linear regression, propensity score matching.
risk of case, 44.5% lower, RR 0.56, p < 0.001, treatment 4,197 of 113,845 (3.7%), control 3,034 of 45,716 (6.6%), NNT 34.
Mondal, 5/31/2021, retrospective, India, peer-reviewed, 11 authors, dosage not specified.
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 risk of symptomatic case, 87.9% lower, RR 0.12, p = 0.006, treatment 128, control 1,342, adjusted per study, odds ratio converted to relative risk, control prevalence approximated with overall prevalence, multivariable, primary outcome.
Moraes, 4/30/2021, Randomized Controlled Trial, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04384458 (history).
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 Estimated 400 patient RCT with results unknown and over 5 years late.
Morgenstern, 4/16/2021, retrospective, propensity score matching, Dominican Republic, peer-reviewed, 16 authors, dosage 200μg/kg weekly, trial NCT04832945 (history).
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 risk of hospitalization, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 271 (0.0%), control 2 of 271 (0.7%), NNT 136, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), PSM.
risk of case, 74.0% lower, RR 0.26, p = 0.008, treatment 5 of 271 (1.8%), control 18 of 271 (6.6%), NNT 21, adjusted per study, PSM, multivariate Cox regression, primary outcome.
PREVENT-COVID, 6/24/2023, Double Blind Randomized Controlled Trial, placebo-controlled, trial NCT05060666 (history) (PREVENT-COVID).
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 Estimated 412 patient RCT with results unknown and over 4 years late.
Samajdar, 11/17/2021, retrospective, India, peer-reviewed, 9 authors, study period 1 September, 2020 - 31 December, 2020, dosage not specified, excluded in exclusion analyses: minimal details provided; unadjusted results with no group details; results may be significantly affected by survey bias.
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 risk of case, 79.8% lower, RR 0.20, p < 0.001, treatment 12 of 164 (7.3%), control 29 of 81 (35.8%), NNT 3.5, odds ratio converted to relative risk, physician survey.
risk of case, 48.6% lower, RR 0.51, p = 0.03, treatment 11 of 109 (10.1%), control 39 of 200 (19.5%), NNT 11, odds ratio converted to relative risk, combined ivermectin or HCQ in community.
Seet, 4/14/2021, Cluster Randomized Controlled Trial, Singapore, peer-reviewed, 15 authors, study period 13 May, 2020 - 31 August, 2020, dosage 12mg single dose, 200µg/kg, maximum 12mg, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04446104 (history).
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 risk of symptomatic case, 49.8% lower, RR 0.50, p < 0.001, treatment 32 of 617 (5.2%), control 64 of 619 (10.3%), NNT 19.
risk of case, 5.8% lower, RR 0.94, p = 0.61, treatment 398 of 617 (64.5%), control 433 of 619 (70.0%), NNT 18, adjusted per study, odds ratio converted to relative risk, model 6, primary outcome.
Shouman (B), 8/28/2020, Randomized Controlled Trial, Egypt, peer-reviewed, 8 authors, study period 1 June, 2020 - 28 July, 2020, dosage 18mg days 1, 3, dose varies depending on weight - 40-60kg: 15mg, 60-80kg: 18mg, >80kg: 24mg, trial NCT04422561 (history).
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 risk of symptomatic case, 91.3% lower, RR 0.09, p < 0.001, treatment 15 of 203 (7.4%), control 59 of 101 (58.4%), NNT 2.0, adjusted per study, inverted to make RR<1 favor treatment, multivariate, primary outcome.
risk of severe case, 92.9% lower, RR 0.07, p = 0.002, treatment 1 of 203 (0.5%), control 7 of 101 (6.9%), NNT 16, unadjusted.
Tanioka, 3/26/2021, retrospective, ecological study, multiple countries, preprint, 3 authors, dosage 200μg/kg, dose varied, typically 150-200μg/kg, excluded in exclusion analyses: not a typical trial, analysis of African countries that used or did not use ivermectin prophylaxis for parasitic infections.
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 risk of death, 88.2% lower, RR 0.12, p = 0.002, relative mean mortality per million.
Wagstaff (D), 9/16/2025, Double Blind Randomized Controlled Trial, placebo-controlled, Australia, peer-reviewed, median age 50.0, 9 authors, study period 22 November, 2021 - 31 May, 2024, trial ACTRN12621001535864.
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 DAFS, 54.5% lower, OR 0.45, p = 0.04, treatment 36, control 32, adjusted per study, inverted to make OR<1 favor treatment, DAFS 1-14, multivariable, RR approximated with OR.
DAFS, 33.3% lower, OR 0.67, p = 0.35, treatment 36, control 32, adjusted per study, inverted to make OR<1 favor treatment, DAFS 1-28, multivariable, RR approximated with OR.
viral+ time, 47.4% lower, relative time 0.53, p = 0.03, treatment 36, control 32, adjusted per study, inverted to make RR<1 favor treatment, relative mean time to positive PCR/RAT.
risk of case, 11.1% lower, RR 0.89, p = 0.80, treatment 11 of 36 (30.6%), control 11 of 32 (34.4%), NNT 26.
Footnotes
a.
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
b.
Monoclonal antibodies were previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib were recommended for late stage hospitalized patients.
c.
Smoking was known to cause lung cancer since at least 1939, but this was not widely recognized in the US until 1964, 25 years later. Surgeon general Leroy Burney tried publicizing the danger starting in 1957, with limited success. Surgeon general Luther Terry, appointed in 1961, prompted by President Kennedy in 1962 amid pressure from health advocates, finally got recognition in 1964. The 1964 report reviewed 7,000+ studies, but these could (and should) have been reviewed and acted upon in real-time as they were published. Historians attribute the 25 year delay to an industry campaign to manufacture doubt and controversy, through tactics like funding biased and fraudulent research from "independent" organizations, attacking scientists, and political lobbying. The success of the industry campaign is only possible because officials did not analyze the research in detail. Fraudulent industry research supported prior failures, but would have been called out as fraudulent by officials that analyzed and understood the research in real-time.
d.
When administered late in infection, HCQ may enhance viral egress by further increasing lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of SARS-CoV-2 particles into the extracellular environment776,777. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage778. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.
e.
Peters (B) et al. is subject to confounding by calendar-time (SOC evolved rapidly early in the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital specific effects), hospital type (non-treatment hospitals were tertiary university centers), confounding by indication (4/7 hospitals initiated treatment on deterioration), immortal-time bias for as-treated (exposure assigned after baseline), significant differences for other experimental treatments, potential overadjustment from collider bias (steroid use and indication bias), limited baseline severity information, differences in hospice referral propensity across hospitals, unadjusted difference in time from onset to admission, difference in PCR positivity, and other factors. Mahévas et al. is subject to confounding by hospital (treatment highly dependent on the hospital, different SOC/ICU transfer practices, not included in PS), immortal time (only partly addressed in sensitivity analysis), co-treatment differences, calendar-time (SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep age-risk gradient), residual imbalance (variables dropped from PS), a composite outcome dependent on hospital triage/capacity, and other factors.
References
576.
covid19criticalcare.com, covid19criticalcare.com/wp-content/uploads/2022/03/Statement-on-Together-Trial-WSJ-Article-Mar-18-1.pdf.
699.
doyourownresearch.substack.com (S), doyourownresearch.substack.com/p/the-story-of-a-real-activ-6-patient.