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Proxalutamide for COVID-19: real-time meta analysis of 4 studies

@CovidAnalysis, March 2024, Version 11V11
 
0 0.5 1 1.5+ All studies 78% 4 1,953 Improvement, Studies, Patients Relative Risk Mortality 78% 4 1,953 Ventilation 95% 2 445 Hospitalization 72% 4 1,953 Viral clearance 74% 1 0 RCTs 78% 4 1,953 RCT mortality 78% 4 1,953 Peer-reviewed 78% 2 1,046 Early 71% 3 1,175 Late 78% 1 778 Proxalutamide for COVID-19 c19early.org March 2024 Favorsproxalutamide Favorscontrol
Abstract
Statistically significant lower risk is seen for mortality, ventilation, hospitalization, recovery, and viral clearance. 4 studies from 2 independent teams in 2 countries show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 78% [70‑83%] lower risk. Results are similar for peer-reviewed studies. Currently all studies are RCTs.
Studies to date are from only 2 different groups.
No treatment or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective.
All data to reproduce this paper and sources are in the appendix. Zheng present another meta analysis for proxalutamide, showing significant improvements for mortality, mechanical ventilation, hospitalization, and clinical improvement.
Evolution of COVID-19 clinical evidence Proxalutamide p<0.0000000001 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org March 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
Proxalutamide reduces risk for COVID-19 with very high confidence for mortality and in pooled analysis, high confidence for hospitalization, and low confidence for ventilation, recovery, and viral clearance.
Proxalutamide was the 30th treatment shown effective with ≥3 clinical studies in December 2021, now with p < 0.00000000001 from 4 studies, and recognized in 3 countries.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 66 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ McCoy (DB RCT) 80% 0.20 [0.01-4.13] death 0/134 2/134 censored, see notes Improvement, RR [CI] Treatment Control Cadegiani (DB RCT) 63% 0.37 [0.02-8.85] death 0/75 1/102 Kintor (DB RCT) 67% 0.33 [0.01-8.16] death 0/365 1/365 Tau​2​ = 0.00, I​2​ = 0.0%, p = 0.18 Early treatment 71% 0.29 [0.05-1.75] 0/574 4/601 71% lower risk Cadegiani (DB RCT) 78% 0.22 [0.16-0.30] death 45/423 171/355 Improvement, RR [CI] Treatment Control Tau​2​ = 0.00, I​2​ = 0.0%, p < 0.0001 Late treatment 78% 0.22 [0.16-0.30] 45/423 171/355 78% lower risk All studies 78% 0.22 [0.17-0.30] 45/997 175/956 78% lower risk 4 proxalutamide COVID-19 studies c19early.org March 2024 Tau​2​ = 0.00, I​2​ = 0.0%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) Favors proxalutamide Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ McCoy (DB RCT) 80% death censored Improvement Relative Risk [CI] Cadegiani (DB RCT) 63% death Kintor (DB RCT) 67% death Tau​2​ = 0.00, I​2​ = 0.0%, p = 0.18 Early treatment 71% 71% lower risk Cadegiani (DB RCT) 78% death Tau​2​ = 0.00, I​2​ = 0.0%, p < 0.0001 Late treatment 78% 78% lower risk All studies 78% 78% lower risk 4 proxalutamide C19 studies c19early.org March 2024 Tau​2​ = 0.00, I​2​ = 0.0%, p < 0.0001 Effect extraction pre-specifiedRotate device for details Favors proxalutamide Favors control
B
-100% -50% 0% 50% 100% Timeline of COVID-19 proxalutamide studies (pooled effects) 2020 2021 2022 Favorsproxalutamide Favorscontrol c19early.org March 2024 December 2021: efficacy (pooled outcomes) December 2021: efficacy (specific outcome) December 2021: efficacy (RCT pooled) December 2021: efficacy (RCT specific)
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix. B. Timeline of results in proxalutamide 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.
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 issues Duloquin, Hampshire, Scardua-Silva, Yang, cardiovascular complications Eberhardt, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factors Note A, Malone, Murigneux, Lv, Lui, Niarakis, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk c19early.org, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
We analyze all significant controlled studies of proxalutamide 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, individual outcomes, peer-reviewed studies, and Randomized Controlled Trials (RCTs).
Figure 2 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.
Figure 2. Treatment stages.
2 In Vitro studies support the efficacy of proxalutamide Ma, Qiao.
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.
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3 plots individual results by treatment stage. Figure 4, 5, 6, 7, 8, 9, and 10 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, hospitalization, recovery, viral clearance, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies78% [70‑83%]
****
4 1,953 38
Peer-reviewed studiesPeer-reviewed78% [70‑84%]
****
2 1,046 30
Randomized Controlled TrialsRCTs78% [70‑83%]
****
4 1,953 38
Mortality78% [70‑83%]
****
4 1,953 38
VentilationVent.95% [60‑99%]
**
2 445 22
HospitalizationHosp.72% [19‑90%]
*
4 1,953 38
RCT mortality78% [70‑83%]
****
4 1,953 38
RCT hospitalizationRCT hosp.72% [19‑90%]
*
4 1,953 38
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment
All studies71% [-75‑95%]78% [70‑84%]
****
Peer-reviewed studiesPeer-reviewed80% [-313‑99%]78% [70‑84%]
****
Randomized Controlled TrialsRCTs71% [-75‑95%]78% [70‑84%]
****
Mortality71% [-75‑95%]78% [70‑84%]
****
VentilationVent.95% [60‑99%]
**
HospitalizationHosp.81% [46‑93%]
**
33% [18‑46%]
***
RCT mortality71% [-75‑95%]78% [70‑84%]
****
RCT hospitalizationRCT hosp.81% [46‑93%]
**
33% [18‑46%]
***
0 0.25 0.5 0.75 1 1.25 1.5+ All studies Late treatment Early treatment Efficacy in COVID-19 proxalutamide studies (pooled effects) Favors proxalutamide Favors control c19early.org March 2024
Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 4. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for recovery.
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Figure 9. Random effects meta-analysis for viral clearance.
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Figure 10. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. 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.
Currently all studies are RCTs.
Heterogeneity in COVID-19 studies arises from many factors including:
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 hours McLean, Treanor. Baloxavir 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 for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases Ikematsu
<24 hours-33 hours symptoms Hayden
24-48 hours-13 hours symptoms Hayden
Inpatients-2.5 hours to improvement Kumar
Figure 11 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 66 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 11. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 66 treatments.
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 (as in LĂłpez-Medina et al.).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants Korves, for example the Gamma variant shows significantly different characteristics Faria, Karita, Nonaka, Zavascki. 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 variants Peacock, Willett.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other kinds of treatment such as prone positioning. Treatments may be synergistic Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan, therefore efficacy may depend strongly on combined treatments.
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.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 12. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 44 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 treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.7 months. When restricting to RCTs only, 50% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.1 months.
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Figure 12. 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.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For proxalutamide, there is currently not enough data to evaluate publication bias with high confidence.
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 by 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 affiliated with special interests 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 alone Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan. 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, vaccine, 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.
Zheng present another meta analysis for proxalutamide, showing significant improvements for mortality, mechanical ventilation, hospitalization, and clinical improvement.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors Lui, Lv, Malone, Murigneux, Niarakis, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Figure 13 shows an overview of the results for proxalutamide in the context of multiple COVID-19 treatments, and Figure 14 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Figure 13. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random effects meta-analysis. 0.6% of 7,095 proposed treatments show efficacy c19early.org (B).
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Figure 14. Efficacy vs. cost for COVID-19 treatments.
Studies to date show that proxalutamide is an effective treatment for COVID-19. Statistically significant lower risk is seen for mortality, ventilation, hospitalization, recovery, and viral clearance. 4 studies from 2 independent teams in 2 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 78% [70‑83%] lower risk. Results are similar for peer-reviewed studies. Currently all studies are RCTs.
Studies to date are from only 2 different groups.
Zheng present another meta analysis for proxalutamide, showing significant improvements for mortality, mechanical ventilation, hospitalization, and clinical improvement.
0 0.5 1 1.5 2+ Mortality 63% Improvement Relative Risk Ventilation 90% Hospitalization 86% Proxalutamide  Cadegiani et al.  EARLY TREATMENT  DB RCT Is early treatment with proxalutamide beneficial for COVID-19? Double-blind RCT 177 patients in Brazil (January - February 2021) Lower hospitalization with proxalutamide (p=0.00083) c19early.org Cadegiani et al., medRxiv, July 2021 Favors proxalutamide Favors control
Cadegiani: RCT 177 women in Brazil, 75 treated with proxalutamide, showing significantly lower hospitalization with treatment.
0 0.5 1 1.5 2+ Mortality 78% Improvement Relative Risk Mortality (b) 79% Recovery rate 45% Recovery rate (b) 55% primary Hospitalization time 33% Proxalutamide  Cadegiani et al.  LATE TREATMENT  DB RCT Is late treatment with proxalutamide beneficial for COVID-19? Double-blind RCT 778 patients in Brazil (February - April 2021) Lower mortality (p<0.0001) and improved recovery (p<0.0001) c19early.org Cadegiani et al., Cureus, December 2021 Favors proxalutamide Favors control
Cadegiani (B): RCT 778 hospitalized patients in Brazil, 423 treated with proxalutamide, showing significantly lower mortality and improved recovery with treatment. NCT04728802 and NCT05126628. Authors note that cases in this trial were predominantly the P.1 Gamma variant, for which proxalutamide may be more effective compared to other variants.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk Hospitalization 50% Mortality (b) 67% Hospitalization (b) 71% Mortality (c) 67% Hospitalization (c) 92% Viral clearance 74% Proxalutamide  Kintor et al.  EARLY TREATMENT  DB RCT Is early treatment with proxalutamide beneficial for COVID-19? Double-blind RCT 730 patients in the USA (March 2021 - April 2022) Improved viral clearance with proxalutamide (p=0.0001) c19early.org Kintor, Press Release, April 2022 Favors proxalutamide Favors control
Kintor: RCT 733 outpatients, 99% in the USA, showing lower hospitalization/death, and significantly reduced viral load with proxalutamide treatment. The viral clearance result is from Ma et al..
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk Ventilation 97% Hospitalization 91% Proxalutamide  McCoy et al.  EARLY TREATMENT  DB RCT Is early treatment with proxalutamide beneficial for COVID-19? Double-blind RCT 268 patients in Brazil (June - July 2020) Lower ventilation (p<0.0001) and hospitalization (p<0.0001) c19early.org McCoy et al., Frontiers in Medicine, Dec 2020 Favors proxalutamide Favors control
McCoy: RCT 268 male patients in Brazil, 134 treated with proxalutamide, showing significantly lower hospitalization and mechanical ventilation. NCT04446429.

This paper was censored without details or author response, and the editors have ignored the authors, see twitter.com.
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. Search terms are proxalutamide 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 proxalutamide for COVID-19 that report a comparison with a control group are included in the main analysis. This is a living analysis and is updated regularly.
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 both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used 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 test status. 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. 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. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang. Reported confidence intervals and p-values were used when available, using adjusted values 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 1 Sweeting. 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 (3.12.2) with scipy (1.12.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.1), and plotly (5.19.0).
Forest plots are computed using PythonMeta Deng 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. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.0 is used to parse PDF documents.
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 effective McLean, Treanor.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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/xmeta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Cadegiani, 7/10/2021, Double Blind Randomized Controlled Trial, Brazil, preprint, 7 authors, study period 4 January, 2021 - 28 February, 2021. risk of death, 63.4% lower, RR 0.37, p = 1.00, treatment 0 of 75 (0.0%), control 1 of 102 (1.0%), NNT 102, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of mechanical ventilation, 89.7% lower, RR 0.10, p = 0.07, treatment 0 of 75 (0.0%), control 5 of 102 (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 hospitalization, 85.7% lower, RR 0.14, p < 0.001, treatment 2 of 75 (2.7%), control 19 of 102 (18.6%), NNT 6.3.
Kintor, 4/5/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, preprint, 1 author, study period 5 March, 2021 - 1 April, 2022, trial NCT04870606 (history). risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 365 (0.0%), control 1 of 365 (0.3%), NNT 365, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 1+ days of treatment, group size approximated.
risk of hospitalization, 50.0% lower, RR 0.50, p = 0.38, treatment 4 of 365 (1.1%), control 8 of 365 (2.2%), NNT 91, 1+ days of treatment, group size approximated.
risk of death, 66.6% lower, RR 0.33, p = 1.00, treatment 0 of 360 (0.0%), control 1 of 361 (0.3%), NNT 361, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), >1 day of treatment, group size approximated.
risk of hospitalization, 71.3% lower, RR 0.29, p = 0.18, treatment 2 of 360 (0.6%), control 7 of 361 (1.9%), NNT 72, >1 day of treatment, group size approximated.
risk of death, 66.6% lower, RR 0.33, p = 1.00, treatment 0 of 346 (0.0%), control 1 of 347 (0.3%), NNT 347, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), >7 days of treatment, group size approximated.
risk of hospitalization, 92.3% lower, RR 0.08, p = 0.03, treatment 0 of 346 (0.0%), control 6 of 347 (1.7%), NNT 58, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), >7 days of treatment, group size approximated.
risk of no viral clearance, 73.9% lower, RR 0.26, p < 0.001, day 7.
McCoy, 12/30/2020, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 15 authors, study period 15 June, 2020 - 28 July, 2020, trial NCT04446429 (history). risk of death, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 134 (0.0%), control 2 of 134 (1.5%), NNT 67, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of mechanical ventilation, 97.1% lower, RR 0.03, p < 0.001, treatment 0 of 134 (0.0%), control 17 of 134 (12.7%), NNT 7.9, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 91.0% lower, RR 0.09, p < 0.001, treatment 3 of 134 (2.2%), control 35 of 134 (26.1%), NNT 4.2.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Cadegiani (B), 12/25/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 15 authors, study period 1 February, 2021 - 15 April, 2021, trial NCT04728802 (history). risk of death, 78.0% lower, RR 0.22, p < 0.001, treatment 45 of 423 (10.6%), control 171 of 355 (48.2%), NNT 2.7, adjusted per study, 28 days, Cox proportional hazards.
risk of death, 79.0% lower, RR 0.21, p < 0.001, treatment 34 of 423 (8.0%), control 138 of 355 (38.9%), NNT 3.2, adjusted per study, 14 days, Cox proportional hazards.
recovery rate, RR 0.55, p < 0.001, treatment 423, control 355, adjusted per study, inverted to make RR<1 favor treatment, 28 days, Cox proportional hazards.
recovery rate, RR 0.45, p < 0.001, treatment 423, control 355, adjusted per study, inverted to make RR<1 favor treatment, 14 days, Cox proportional hazards, primary outcome.
hospitalization time, 33.3% lower, relative time 0.67, p < 0.001, treatment 423, control 355.
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