Selenium for COVID-19: real-time meta-analysis of 12 studies (4 treatment studies and 8 sufficiency studies)

@CovidAnalysis, October 2026, Version 6V6 (V1 2023)
Abstract
Meta-analysis using the most serious outcome reported shows 34% [-40‑69%] lower risk, without reaching statistical significance. Results are similar for Randomized Controlled Trials and slightly worse for higher quality studies.
One study shows significant benefit.
8 sufficiency studies analyze outcomes based on serum levels, showing 58% [38‑71%] lower risk for patients with higher selenium levels.
1 RCT with 100 patients has not reported results (4 years late)1.
The European Food Safety Authority has found evidence for a causal relationship between the intake of selenium and optimal immune system function2,3. Sufficiency studies show COVID-19 associated with low selenium levels, however there is very limited and conflicting results for clinical outcomes with selenium treatment.
No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Other treatments are more effective. Selenium currently has no early treatment studies. Dietary sources may be preferred. The quality of non-prescription supplements varies widely4-6. All data and sources to reproduce this analysis are in the appendix.
Fan et al. present another meta-analysis for selenium, showing significant improvement for cases.
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Evolution of COVID-19 clinical evidence Meta-analysis results over time Selenium p=0.28 Acetaminophen p=0.00000021 2020 2021 2022 2023 2024 2025 2026 Lowerrisk Higherrisk c19early.org October 2026 50% 0% -50%
Selenium for COVID-19 — Highlights
Meta-analysis of studies to date shows no significant improvements with selenium.
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.
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October 2026

Selenium COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Hafizi (DB RCT)35%0.65 · 0.11–3.73death2/623/60CT1
Late treatment35%0.65 · 0.11–3.732/623/6035% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.64
Holt80%0.20 · 0.03–1.44cases1/167445/15,060COVIDENCE UK Nimer−26%1.26 · 0.64–2.32hosp.12/57207/2,091 Vaisi53%0.47 · 0.32–0.81hosp.3,853 (n)102 (n)
Prophylaxis36%0.64 · 0.26–1.5913/4,077652/17,25336% lower risk
Tau² = 0.43, I² = 73.7%, p = 0.34
All studies34%0.66 · 0.31–1.4015/4,139655/17,31334% lower risk
Tau² = 0.31, I² = 60.8%, p = 0.2800.511.52+
1 CT: study uses combined treatment
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B
-100% -50% 0% 50% 100% Timeline of COVID-19 selenium studies (pooled effects) 2020 2021 2022 2023 Favorsselenium Favorscontrol c19early.org October 2026
Fig. 1. A. Random-effects meta-analysis. 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. For details see the appendix. B. Timeline of results in selenium studies.
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.
Supporting research
Selenium has been identified by the European Food Safety Authority (EFSA) as having sufficient evidence for a causal relationship between intake and optimal immune system function2,3,47. Selenium may be beneficial for COVID-19 by inhibiting ferroptosis, an oxidative stress-induced cell death pathway implicated in COVID-19 pathogenesis48. Selenium enhances immune response, inhibits ROS production, and protects against ferroptosis via GPX4 induction49.
Analysis
We analyze all significant controlled studies of selenium 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, Randomized Controlled Trials (RCTs), and higher 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
Selenium may be beneficial for COVID-19 by inhibiting ferroptosis, an oxidative stress-induced cell death pathway implicated in COVID-19 pathogenesis48.
2 in vitro studies support the efficacy of selenium57,58.
An in vivo animal study supports the efficacy of selenium59.
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 for all stages combined, for Randomized Controlled Trials, after 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, and 9 show forest plots for random-effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, cases, and sufficiency studies.
Table 1. Random-effects meta-analysis for all stages combined, for Randomized Controlled Trials, after exclusions, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval.
Relative Risk Studies Patients
All studies0.66 [0.31‑1.40]420K
After exclusions0.78 [0.30‑2.06]26,103
RCTsRCTs0.65 [0.11‑3.73]1122
HospitalizationHosp.0.78 [0.30‑2.06]26,103
Cases0.59 [0.18‑1.98]210K
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.
Late treatment Prophylaxis
All studies0.65 [0.11‑3.73]0.65
[0.11‑3.73]		0.64 [0.26‑1.59]0.64
[0.26‑1.59]
After exclusions0.78 [0.30‑2.06]0.78
[0.30‑2.06]
RCTsRCTs0.65 [0.11‑3.73]0.65
[0.11‑3.73]
HospitalizationHosp.0.78 [0.30‑2.06]0.78
[0.30‑2.06]
Cases0.59 [0.18‑1.98]0.59
[0.18‑1.98]
0 0.25 0.5 0.75 1 1.25 1.5+ All studies Late treatment Prophylaxis Efficacy in COVID-19 selenium studies (pooled effects) Favors selenium Favors control c19early.org October 2026
Fig. 4. 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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October 2026

Selenium COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Hafizi (DB RCT)35%0.65 · 0.11–3.73death2/623/60CT1
Late treatment35%0.65 · 0.11–3.732/623/6035% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.64
Holt80%0.20 · 0.03–1.44cases1/167445/15,060COVIDENCE UK Nimer−26%1.26 · 0.64–2.32hosp.12/57207/2,091 Vaisi53%0.47 · 0.32–0.81hosp.3,853 (n)102 (n)
Prophylaxis36%0.64 · 0.26–1.5913/4,077652/17,25336% lower risk
Tau² = 0.43, I² = 73.7%, p = 0.34
All studies34%0.66 · 0.31–1.4015/4,139655/17,31334% lower risk
Tau² = 0.31, I² = 60.8%, p = 0.2800.511.52+
1 CT: study uses combined treatment
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← Selenium
reduces riskSelenium
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. For details see the appendix.
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October 2026

Selenium COVID-19 mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk
Hafizi (DB RCT)35%0.65 · 0.11–3.732/623/60CT1
Late treatment35%0.65 · 0.11–3.732/623/6035% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.64
All studies35%0.65 · 0.11–3.732/623/6035% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.6400.511.52+
1 CT: study uses combined treatment
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← Selenium
reduces riskSelenium
increases risk →
Fig. 6. Random-effects meta-analysis for mortality results.
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October 2026

Selenium COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Nimer−26%1.26 · 0.64–2.32hosp.12/57207/2,091 Vaisi53%0.47 · 0.32–0.81hosp.3,853 (n)102 (n)
Prophylaxis22%0.78 · 0.30–2.0612/3,910207/2,19322% lower risk
Tau² = 0.40, I² = 82.4%, p = 0.63
All studies22%0.78 · 0.30–2.0612/3,910207/2,19322% lower risk
Tau² = 0.40, I² = 82.4%, p = 0.6300.511.52+
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← Selenium
reduces riskSelenium
increases risk →
Fig. 7. Random-effects meta-analysis for hospitalization.
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October 2026

Selenium COVID-19 case results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Holt80%0.20 · 0.03–1.44cases1/167445/15,060COVIDENCE UK Vaisi15%0.85 · 0.40–0.97symp. case3,853 (n)102 (n)
Prophylaxis41%0.59 · 0.18–1.981/4,020445/15,16241% lower risk
Tau² = 0.51, I² = 50.3%, p = 0.4
All studies41%0.59 · 0.18–1.981/4,020445/15,16241% lower risk
Tau² = 0.51, I² = 50.3%, p = 0.400.511.52+
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reduces riskSelenium
increases risk →
Fig. 8. Random-effects meta-analysis for cases.
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October 2026

Selenium COVID-19 sufficiency studies

StudyImprovementRR · 95% CIOutcomeHigh LevelsLow LevelsRelative Risk
Moghaddam56%0.44 · 0.23–0.83death12/9222/74 Im88%0.12 · 0.01–2.30death0/283/21 Majeed67%0.33 · 0.10–1.03casescase-control study Du Laing92%0.08 · 0.01–0.61death1/429/31 Voelkle12%0.88 · 0.32–1.86death/ICU7/288/29 Wozniak (ICU)62%0.38 · 0.12–1.25death59 (n)59 (n)ICU patients Rozemeijer92%0.08 · 0.00–1.54ICUcase-control study Mohamed57%0.43 · 0.22–0.86death9/3812/22
All studies58%0.42 · 0.29–0.6229/28754/23658% lower risk
Tau² = 0.02, I² = 5.7%, p < 0.000100.511.52+
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increases risk →
Fig. 9. Random-effects meta-analysis for sufficiency studies. 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)
Fig. 10 shows a forest plot for random-effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 1 and Table 2. Currently there is only one RCT.
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 biases60, and analysis of double-blind RCTs has identified extreme levels of bias61. 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 selenium 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. 11. For COVID-19, observational study results do not systematically differ from RCTs, RR 0.97 [0.91‑1.03] across 227 treatments65.
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]54. 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 see69,70.
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—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.
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October 2026

Selenium COVID-19 Randomized Controlled Trials

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Hafizi (DB RCT)35%0.65 · 0.11–3.73death2/623/60CT1
Late treatment35%0.65 · 0.11–3.732/623/6035% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.64
All studies35%0.65 · 0.11–3.732/623/6035% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.6400.511.52+
1 CT: study uses combined treatment
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← Selenium
reduces riskSelenium
increases risk →
Fig. 10. 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. For details see the appendix.
Unreported RCTs
1 selenium RCT has not reported results1. The trial reports report an estimated total of 100 patients. The result is delayed over 4 years.
Exclusions
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major 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 can be easily influenced by potential bias, may ignore or underemphasize serious issues not captured in the checklists, and may overemphasize issues unlikely to alter outcomes in specific cases (for example certain specifics of randomization with a very large effect size and well-matched baseline characteristics).
The studies excluded are as below. Fig. 12 shows a forest plot for random-effects meta-analysis of all studies after exclusions.
Hafizi, data issues pending author response.
Holt, significant unadjusted confounding possible.
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October 2026

Selenium COVID-19 studies after exclusions

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Nimer−26%1.26 · 0.64–2.32hosp.12/57207/2,091 Vaisi53%0.47 · 0.32–0.81hosp.3,853 (n)102 (n)
Prophylaxis22%0.78 · 0.30–2.0612/3,910207/2,19322% lower risk
Tau² = 0.40, I² = 82.4%, p = 0.63
All studies22%0.78 · 0.30–2.0612/3,910207/2,19322% lower risk
Tau² = 0.40, I² = 82.4%, p = 0.6300.511.52+
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← Selenium
reduces riskSelenium
increases risk →
Fig. 12. Random-effects meta-analysis for all studies after exclusions. 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. For details see the appendix.
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."50.
We analyze media coverage for the 227 treatments we cover using Altmetric73, which reports the number of ~12,000 tracked news outlets that covered each study74. Studies are considered to have received significant media coverage if they were covered by at least 0.5% of the tracked news outlets. Fig. 13, 14, and 15 show the bias toward negative results for low-cost treatments, in contrast to the opposite bias for high-profit treatments. 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. 13. 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. 14. Mainstream media was biased against positive results for low-cost treatments.
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Fig. 15. In contrast to the results for low-cost treatments, mainstream media was biased towards positive results for high-cost treatments.
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.
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 hours75,76. 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 cases77
<24 hours-33 hours symptoms78
24-48 hours-13 hours symptoms78
Inpatients-2.5 hours to improvement79
Fig. 16 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 227 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Fig. 16. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.
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 variants81, for example the Gamma variant shows significantly different characteristics82-85. 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 variants86,87.
Treatment regimen
Effectiveness may depend strongly on the dosage and treatment regimen.
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. Non-prescription supplements may show very wide variations in quality4,5.
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 synergistic90-114, 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.
Pooled Effects
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. 17 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 18 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. 19 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. 17. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 18. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 17. 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. 20 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Fig. 20. 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.
Discussion
Publication bias
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results116-119. For selenium, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more 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. 21 shows a scatter plot of results for prospective and retrospective treatment studies. The median effect size for retrospective studies is 13% improvement, compared to 57% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Fig. 21. Prospective vs. retrospective studies. The diamonds show the results of random-effects meta-analysis.
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. Selenium for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 selenium trials have been run by physicians on the front lines with the primary goal 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, and ensuring accurate dosing), not all selenium trials represent the optimal conditions for efficacy.
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 alone90-114. 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.
Notes
1 of 4 studies combine treatments. The results of selenium alone may differ. 1 of 1 RCTs use combined treatment. Currently all studies are peer-reviewed. Fan et al. present another meta-analysis for selenium, showing significant improvement for cases.
Reviews
Many reviews cover selenium for COVID-19, presenting additional background on mechanisms and related results, including48,49,120-131.
Other studies
Additional preclinical or review papers suggesting potential benefits of selenium for COVID-19 include134-140. We have not reviewed these studies in detail.
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. 22 shows an overview of the results for selenium in the context of multiple COVID-19 treatments, and Fig. 23 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Fig. 22. 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 efficacy141.
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Fig. 23. Efficacy vs. cost for COVID-19 treatments.
Conclusion
Meta-analysis using the most serious outcome reported shows 34% [-40‑69%] lower risk, without reaching statistical significance. Results are similar for Randomized Controlled Trials and slightly worse for higher quality studies. One study shows significant benefit. 8 sufficiency studies analyze outcomes based on serum levels, showing 58% [38‑71%] lower risk for patients with higher selenium levels.
The European Food Safety Authority has found evidence for a causal relationship between the intake of selenium and optimal immune system function2,3. Sufficiency studies show COVID-19 associated with low selenium levels, however there is very limited and conflicting results for clinical outcomes with selenium treatment.
Fan et al. present another meta-analysis for selenium, showing significant improvement for cases.
 
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/semeta.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/.
Study Notes
Retrospective 73 hospitalized COVID-19 patients in Belgium, showing higher risk of mortality with selenium deficiency and zinc deficiency. Submit Corrections or Updates.
Estimated 100 patient selenium late treatment RCT with results not reported over 4 years after estimated completion. Submit Corrections or Updates.
Late treatmentRCT · 122 patients · Iran · Oct 2020 – Mar 2021
Selenium for COVID-19
Hafizi et al. Trials · November 2023 · IRCT20170731035423N2
Trial underpowered to detect differences
Randomized, double-blind, placebo-controlled trial of 122 moderate hospitalized COVID-19 patients in Iran, evaluating the addition of BCc1 iron chelator and Hep-S selenium nanomedicines to standard treatment. The nanomedicine group showed a significant 77% reduction in IL-6 levels by day 28 compared to an 18% increase in the placebo group, along with improvements in TNF-alpha and clinical scores for cough, fatigue, and oxygen need, without statistical significance.

There are several issues with this trial:

Outcome switching versus registration - the registry describes a four-arm trial (BCc1 alone, Hep-S alone, combination, placebo) with hospitalization period, mortality, and lung CT as primary outcomes. The paper reports only two arms, drops the two single-agent arms, changes the dosing, and presents IL-6 - not a registered primary outcome - as the primary endpoint. The one significant result is for a new endpoint while the registered primary outcomes are null or unanalyzed.

Degrees of freedom imply impossible sample sizes - in repeated-measures ANOVA the error degrees of freedom are fixed by the number of subjects, yet they imply mutually incompatible Ns from the same cohort - ferritin implies ~7 patients, AST/WBC/HCT imply ~94-96, and even within the cytokines IL-6 implies 21 while TNF and IFN-γ imply 22. The same patients at the same visits cannot yield these. The ferritin analysis as printed is uninterpretable.

Group sizes contradict each other - allocation is stated as 62 treatment and 60 placebo, but Table 3's sex counts sum to 65 and 57, and the CONSORT analysis boxes (58 and 57) don't reconcile with the stated dropouts either. The study's own denominator is unstable across the abstract, figure, and tables.

Headline result is unverifiable - the 77% IL-6 reduction versus 18% increase cannot be checked against any reported figure - no IL-6 means appear anywhere in the paper or supplement. Table 4 gives only sums of squares, F, and p, never the group means the percentages derive from.

Misreported significance - the text labels the IL-6 main effect of time as "significant" when its p-value is 0.219; only the time x group interaction (p=0.041) is actually significant.

Contradictory recruitment dates - the methods give a backwards, impossible range (Oct 2 to Mar 20, 2020) while the results and registry report Oct 2020-Mar 2021.

No power calculation; very small, opaque primary analysis - there was no prespecified sample size, and the primary cytokine outcome has only 11 patients per group, selected by an unstated mechanism.

Conflict of interest - the manufacturer (whose owner is the senior author) synthesized the compound, ran the experiments, analyzed the data, and wrote the manuscript. Submit Corrections or Updates.
Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency. Submit Corrections or Updates.
Analysis of 50 hospitalized COVID-19 patients in South Korea showing 42% of patients with selenium deficiency, and lower mechanical ventilation with selenium sufficiency. Submit Corrections or Updates.
Analysis of 30 COVID-19 patients and 30 healthy controls in India, showing significantly lower selenium levels in COVID-19 patients. 43.3% of COVID-19 patients had selenium levels <70 ng/mL compared to 20% of controls. Submit Corrections or Updates.
Analysis of 33 COVID-19 patients showing selenium levels significantly lower than reference levels, and significantly lower levels in non-survivors compared with survivors. Submit Corrections or Updates.
Retrospective 60 hospitalized pediatric COVID-19 patients showing deficiencies in vitamin D, folic acid (B9), zinc, and selenium associated with higher mortality. Submit Corrections or Updates.
ProphylaxisRetrospective · 2,148 patients · Jordan · Mar – Jul 2021
Selenium for COVID-19
Nimer et al. Bosnian J. Basic Medical Sciences · Feb 2022
Outcome
Improvement
Relative Risk · 95% CI
Hospitalization
-26%1.26 · 0.64–2.32
Severe case
-9%1.09 · 0.57–1.97
00.511.52+
← Selenium
reduces riskSelenium
increases risk →
Higher hospitalization with selenium
Not statistically significant · p = 0.48
Retrospective 2,148 COVID-19 recovered patients in Jordan, showing no significant differences in the risk of severity and hospitalization with selenium prophylaxis. Submit Corrections or Updates.
Prospective pilot study of 20 critically ill COVID-19 ICU patients showing high deficiency rates of 50-100% for vitamins A, B6, and D; zinc; and selenium at admission. Deficiencies of vitamins B6 and D, and low iron status, persisted after 3 weeks. Plasma levels of vitamins A and E, zinc, and selenium increased over time as inflammation resolved, suggesting redistribution may explain some observed deficiencies. All patients received daily micronutrient administration. Additional intravenous and oral micronutrient administration for 10 patients did not significantly impact micronutrient levels or deficiency rates, however authors note that the administered doses may be too low. The form of vitamin D is not specified but may have been cholecalciferol which is expected to have a very long onset of action compared to more appropriate forms such as calcifediol or calcitriol. Submit Corrections or Updates.
ProphylaxisRetrospective · 3,955 patients · Iran
Selenium for COVID-19
Vaisi et al. Clinical Respiratory J. · May 2023
Outcome
Improvement
Relative Risk · 95% CI
Hospitalization
53%0.47 · 0.32–0.81
Symp. case
15%0.85 · 0.40–0.97
00.511.52+
← Selenium
reduces riskSelenium
increases risk →
Lower hospitalization and fewer symptomatic cases
p = 0.018 (hospitalization) and p = 0.042 (fewer symptomatic cases)
Analysis of nutrient intake and COVID-19 outcomes for 3,996 people in Iran, showing lower risk of COVID-19 hospitalization with sufficient vitamin A, vitamin C, and selenium intake, with statistical significance for vitamin A and selenium. Submit Corrections or Updates.
Prospective study of 57 consecutive hospitalized COVID-19 patients in Switzerland, showing higher risk of mortality/ICU admission with vitamin A, vitamin D, and zinc deficiency, with statistical significance only for vitamin A and zinc. Adjustments only considered age. Submit Corrections or Updates.
ICU patientsRetrospective · 118 patients · Switzerland · Mar – May 2020
Selenium for COVID-19
Wozniak et al. Nutrients · July 2023
Outcome
Improvement
Relative Risk · 95% CI
Mortality
62%0.38 · 0.12–1.25
Septic shock
47%0.53 · 0.20–1.43
00.511.52+
← Selenium
reduces riskSelenium
increases risk →
Lower mortality and progression
Not statistically significant · p = 0.1 (mortality) and p = 0.2 (progression)
Retrospective 345 COVID-19 patients in Switzerland, showing significantly different selenium levels with ICU patients < hospitalized patients < outpatients.

For ICU patients, there was higher mortality, septic shock, and mechanical ventilation days with lower selenium levels, with statistical significance only for ventilation. Submit Corrections or Updates.
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 selenium 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 selenium 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.
Fig. 24. 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 reduction142. 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 PythonMeta143 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 (B) 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 1147. 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. 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.
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 reliability155. 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 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 issues157. 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 harmC. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias153,154 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 factorsD.
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 effective75,76.
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.
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/semeta.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.
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.
Ghoweba, 12/15/2021, Double Blind Randomized Controlled Trial, USA, trial NCT04869579 (history) (SeCOVID).
Submit Corrections or Updates.
 Estimated 100 patient RCT with results unknown and over 4 years late.
Hafizi, 11/11/2023, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 17 authors, study period 2 October, 2020 - 20 March, 2021, this trial uses multiple treatments in the treatment arm (combined with BCc1) - results of individual treatments may vary, trial IRCT20170731035423N2, excluded in exclusion analyses: data issues pending author response.
Submit Corrections or Updates.
 risk of death, 35.5% lower, RR 0.65, p = 0.68, treatment 2 of 62 (3.2%), control 3 of 60 (5.0%), NNT 56.
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.
Holt, 3/30/2021, prospective, United Kingdom, peer-reviewed, 34 authors, study period 1 May, 2020 - 5 February, 2021, trial NCT04330599 (history) (COVIDENCE UK), excluded in exclusion analyses: significant unadjusted confounding possible.
Submit Corrections or Updates.
 risk of case, 79.5% lower, RR 0.20, p = 0.11, treatment 1 of 167 (0.6%), control 445 of 15,060 (3.0%), NNT 42, adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated.
Nimer, 2/28/2022, retrospective, Jordan, peer-reviewed, survey, 4 authors, study period March 2021 - July 2021.
Submit Corrections or Updates.
 risk of hospitalization, 26.3% higher, RR 1.26, p = 0.48, treatment 12 of 57 (21.1%), control 207 of 2,091 (9.9%), adjusted per study, odds ratio converted to relative risk, multivariable.
risk of severe case, 8.7% higher, RR 1.09, p = 0.80, treatment 12 of 57 (21.1%), control 248 of 2,091 (11.9%), adjusted per study, odds ratio converted to relative risk, multivariable.
Vaisi, 5/11/2023, retrospective, Iran, peer-reviewed, 5 authors.
Submit Corrections or Updates.
 risk of hospitalization, 53.1% lower, HR 0.47, p = 0.02, treatment 3,853, control 102, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards.
risk of symptomatic case, 15.3% lower, HR 0.85, p = 0.04, treatment 3,853, control 102, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards.
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.
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 environment149,150. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage151. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.
d.
Peters 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