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Vitamin A for COVID-19: real-time meta analysis of 15 studies (12 treatment studies and 3 sufficiency studies)

Covid Analysis, June 2023
https://c19early.org/vameta.html
 
0 0.5 1 1.5+ All studies 34% 12 22,260 Improvement, Studies, Patients Relative Risk Mortality 42% 6 441 Ventilation 0% 1 30 ICU admission 48% 2 70 Hospitalization 10% 5 6,373 Recovery 37% 3 280 Cases 44% 3 19,391 Viral clearance 44% 1 40 RCTs 36% 4 310 RCT mortality 59% 3 130 Peer-reviewed 25% 8 21,884 Sufficiency 73% 3 217 Prophylaxis 39% 4 21,539 Early 62% 3 420 Late 9% 5 301 Vitamin A for COVID-19 c19early.org/va Jun 2023 Favorsvitamin A Favorscontrol after exclusions
Statistically significant improvements are seen for recovery and viral clearance. 6 studies from 5 independent teams in 3 different countries show statistically significant improvements in isolation (3 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 34% [-3‑58%] improvement, without reaching statistical significance. Results are similar for Randomized Controlled Trials, similar after exclusions, and slightly worse for peer-reviewed studies. Results are consistent with early treatment being more effective than late treatment.
Sufficiency studies, analyzing outcomes based on serum levels, show 73% [51‑85%] improvement for patients with higher vitamin A levels (3 studies).
0 0.5 1 1.5+ All studies 34% 12 22,260 Improvement, Studies, Patients Relative Risk Mortality 42% 6 441 Ventilation 0% 1 30 ICU admission 48% 2 70 Hospitalization 10% 5 6,373 Recovery 37% 3 280 Cases 44% 3 19,391 Viral clearance 44% 1 40 RCTs 36% 4 310 RCT mortality 59% 3 130 Peer-reviewed 25% 8 21,884 Sufficiency 73% 3 217 Prophylaxis 39% 4 21,539 Early 62% 3 420 Late 9% 5 301 Vitamin A for COVID-19 c19early.org/va Jun 2023 Favorsvitamin A Favorscontrol after exclusions
No treatment, vaccine, or intervention is 100% effective and available. 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. Only 17% of vitamin A studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Vitamin A p=0.067 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org June 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Percentage improvement with vitamin A (more)
All studies Early treatment Late treatment Studies Patients Authors
All studies34% [-3‑58%]62% [-3‑86%]9% [-235‑75%] 12 22,260 85
Randomized Controlled TrialsRCTs36% [-56‑73%]26% [-76‑69%]59% [-153‑93%] 4 310 23
Mortality42% [-133‑86%]86% [39‑97%]
**
9% [-235‑75%] 6 441 30
Cases44% [-26‑75%]-- 3 19,391 42
RCT mortality59% [-153‑93%]-59% [-153‑93%] 3 130 17
Highlights
Vitamin A reduces risk for COVID-19 with very high confidence for recovery, low confidence for viral clearance and in pooled analysis, and very low confidence for ICU admission, hospitalization, progression, and cases.
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 51 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Al-Sumiadai 86% 0.14 [0.03-0.61] death 2/70 14/70 Improvement, RR [CI] Treatment Control Al-Sumiadai 67% 0.33 [0.07-1.57] progression 2/50 6/50 Rohani (DB RCT) 26% 0.74 [0.31-1.76] hosp. 8/89 11/91 Tau​2 = 0.39, I​2 = 48.3%, p = 0.058 Early treatment 62% 0.38 [0.14-1.03] 12/209 31/211 62% improvement Sarohan -282% 3.83 [1.58-9.24] death 9/10 4/17 Improvement, RR [CI] Treatment Control Beigm.. (SB RCT) 89% 0.11 [0.01-1.98] death 0/30 4/30 ICU patients CT​1 Elkazzaz (RCT) 86% 0.14 [0.01-2.60] death 0/20 3/20 Somi (RCT) -50% 1.50 [0.29-7.73] death 3/15 2/15 Doocy 26% 0.74 [0.11-4.80] death 1/8 23/136 Tau​2 = 1.24, I​2 = 61.0%, p = 0.89 Late treatment 9% 0.91 [0.25-3.35] 13/83 36/218 9% improvement Al-Sumiadai 64% 0.36 [0.23-0.54] cases 20/97 65/112 Improvement, RR [CI] Treatment Control COVIDENCE UK Holt 56% 0.44 [0.06-2.96] cases 1/91 445/15,136 Nimer 21% 0.79 [0.45-1.35] hosp. 15/144 204/2,004 Vaisi 17% 0.83 [0.48-1.00] hosp. 1,140 (n) 2,815 (n) Tau​2 = 0.17, I​2 = 78.2%, p = 0.054 Prophylaxis 39% 0.61 [0.37-1.01] 36/1,472 714/20,067 39% improvement All studies 34% 0.66 [0.42-1.03] 61/1,764 781/20,496 34% improvement 12 vitamin A COVID-19 studies c19early.org/va Jun 2023 Tau​2 = 0.29, I​2 = 70.0%, p = 0.067 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors vitamin A Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Al-Sumiadai 86% death Relative Risk [CI] Al-Sumiadai 67% progression Rohani (DB RCT) 26% hospitalization Tau​2 = 0.39, I​2 = 48.3%, p = 0.058 Early treatment 62% 62% improvement Sarohan -282% death Beigm.. (SB RCT) 89% death ICU patients CT​1 Elkazzaz (RCT) 86% death Somi (RCT) -50% death Doocy 26% death Tau​2 = 1.24, I​2 = 61.0%, p = 0.89 Late treatment 9% 9% improvement Al-Sumiadai 64% case COVIDENCE UK Holt 56% case Nimer 21% hospitalization Vaisi 17% hospitalization Tau​2 = 0.17, I​2 = 78.2%, p = 0.054 Prophylaxis 39% 39% improvement All studies 34% 34% improvement 12 vitamin A COVID-19 studies c19early.org/va Jun 2023 Tau​2 = 0.29, I​2 = 70.0%, p = 0.067 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors vitamin A Favors control
B
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C
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D
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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 of effect extraction see the appendix. B. 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. C. Results within the context of multiple COVID-19 treatments. 0.9% of 3,989 proposed treatments show efficacy [c19early.org]. D. Timeline of results in vitamin A studies.
We analyze all significant studies concerning the use of vitamin A 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, Randomized Controlled Trials (RCTs), and after exclusions.
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.
3 In Silico studies support the efficacy of vitamin A [Chakraborty, Li, Pandya].
3 In Vitro studies support the efficacy of vitamin A [DiGuilio, Morita, Tong].
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, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, sufficiency studies, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  **** p<0.0001.
Improvement Studies Patients Authors
All studies34% [-3‑58%]12 22,260 85
After exclusions39% [8‑60%]
*
8 6,722 38
Peer-reviewed studiesPeer-reviewed25% [0‑44%]
*
8 21,884 71
Randomized Controlled TrialsRCTs36% [-56‑73%]4 310 23
Mortality42% [-133‑86%]6 441 30
ICU admissionICU48% [-40‑80%]2 70 11
HospitalizationHosp.10% [-5‑23%]5 6,373 28
Recovery37% [27‑46%]
****
3 280 16
Cases44% [-26‑75%]3 19,391 42
RCT mortality59% [-153‑93%]3 130 17
RCT hospitalizationRCT hosp.-3% [-27‑16%]3 270 19
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<0.05  ** p<0.01  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies62% [-3‑86%]9% [-235‑75%]39% [-1‑63%]
After exclusions38% [-31‑71%]59% [-153‑93%]38% [-7‑63%]
Peer-reviewed studiesPeer-reviewed64% [-80‑93%]27% [-158‑79%]18% [3‑30%]
*
Randomized Controlled TrialsRCTs26% [-76‑69%]59% [-153‑93%]-
Mortality86% [39‑97%]
**
9% [-235‑75%]-
ICU admissionICU-48% [-40‑80%]-
HospitalizationHosp.26% [-76‑69%]3% [-53‑39%]17% [2‑30%]
*
Recovery32% [-133‑80%]37% [27‑46%]
****
-
Cases--44% [-26‑75%]
RCT mortality-59% [-153‑93%]-
RCT hospitalizationRCT hosp.26% [-76‑69%]3% [-53‑39%]-
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Figure 3. 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 of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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Figure 12. Random effects meta-analysis for sufficiency studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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Figure 13. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] 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. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 14 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 56% improvement, compared to 41% for other studies. Figure 15, 16, and 17 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results. RCT results are included in Table 1 and Table 2.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases [Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. 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, 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.
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 51 treatments we have analyzed, 64% 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).
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows 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 Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
Currently, 37 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 37 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. 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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Figure 14. Results for RCTs and non-RCT studies.
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Figure 15. Random effects meta-analysis for all Randomized Controlled Trials. 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 of effect extraction see the appendix.
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Figure 16. Random effects meta-analysis for RCT mortality results.
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Figure 17. Random effects meta-analysis for RCT hospitalization results.
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 may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 18 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Al-Sumiadai], minimal details of groups provided.
[Doocy], unadjusted results with no group details.
[Holt], significant unadjusted confounding possible.
[Sarohan], unadjusted results with no group details, comments suggest significant group differences and confounding.
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Figure 18. Random effects meta-analysis for all studies after exclusions. 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 of effect extraction see the appendix.
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] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] 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] 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 19 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 19. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 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 [López-Medina]).
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.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. 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 viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] 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 quality [Crawford, Crighton].
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 20. 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, 37 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 94% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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Figure 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.
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, 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 results [Boulware, Meeus, Meneguesso]. For vitamin A, 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.
Figure 21 shows a scatter plot of results for prospective and retrospective treatment studies. 50% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 50% of prospective studies, showing no difference. The median effect size for retrospective studies is 19% improvement, compared to 60% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 21. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 22 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 22. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Vitamin A for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 vitamin A 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 vitamin A trials represent the optimal conditions for efficacy.
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, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. 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.
1 of 12 studies combine treatments. The results of vitamin A alone may differ. 1 of 4 RCTs use combined treatment.
Statistically significant improvements are seen for recovery and viral clearance. 6 studies from 5 independent teams in 3 different countries show statistically significant improvements in isolation (3 for the most serious outcome). Meta analysis using the most serious outcome reported shows 34% [-3‑58%] improvement, without reaching statistical significance. Results are similar for Randomized Controlled Trials, similar after exclusions, and slightly worse for peer-reviewed studies. Results are consistent with early treatment being more effective than late treatment. Sufficiency studies, analyzing outcomes based on serum levels, show 73% [51‑85%] improvement for patients with higher vitamin A levels (3 studies).
[Al-Sumiadai (C)] Treatment and prophylaxis studies of vitamin A in Iraq.

The treatment study contained 100 patients, 50 treated with 200,000IU vitamin A for two days, showing lower progression to severe disease, and shorter duration of symptoms.

The prophylaxis study contained 209 contacts of COVID-19 patients, 97 treated with vitamin A, showing significantly lower cases with treatment, and shorter duration of symptoms.
0 0.5 1 1.5 2+ Progression 67% Improvement Relative Risk Recovery time 38% no CI c19early.org/va Al-Sumiadai et al. Vitamin A for COVID-19 EARLY Is early treatment with vitamin A beneficial for COVID-19? Prospective study of 100 patients in Iraq Lower progression with vitamin A (not stat. sig., p=0.27) Al-Sumiadai, Systematic Reviews in Pharmacy, 12:1 Favors vitamin A Favors control
[Al-Sumiadai (B)] Treatment and prophylaxis studies of vitamin A in Iraq.

The treatment study contained 100 patients, 50 treated with 200,000IU vitamin A for two days, showing lower progression to severe disease, and shorter duration of symptoms.

The prophylaxis study contained 209 contacts of COVID-19 patients, 97 treated with vitamin A, showing significantly lower cases with treatment, and shorter duration of symptoms.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk c19early.org/va Al-Sumiadai et al. Vitamin A for COVID-19 EARLY Is early treatment with vitamin A beneficial for COVID-19? Retrospective 140 patients in Iraq Lower mortality with vitamin A (p=0.0024) Al-Sumiadai et al., EurAsian J. Biosciences, 14:7347-7350 Favors vitamin A Favors control
[Al-Sumiadai] Retrospective 70 severe condition patients treated with vitamin A (200,000IU for two days), salbutamol, and budesonide, and 70 patients not treated with vitamin A, showing significantly lower mortality with the addition of vitamin A.
0 0.5 1 1.5 2+ Mortality 89% Improvement Relative Risk Hospitalization >7 days 41% SOFA score @day 7 45% c19early.org/va Beigmohammadi et al. IRCT20200319046819N1 Vitamin A RCT ICU Is very late treatment with vitamin A+vitamins B, C, D, E beneficial for COVID-19? RCT 60 patients in Iran (April - July 2020) Improved recovery with vitamin A+vitamins B, C, D, E (p=0.001) Beigmohammadi et al., Trials, doi:10.1186/s13063-021-05795-4 Favors vitamin A Favors control
[Beigmohammadi] Small RCT 60 ICU patients in Iran, 30 treated with vitamins A, B, C, D, and E, showing significant improvement in SOFA score and several inflammatory markers at day 7 with treatment.

5,000 IU vitamin A daily, 600,000 IU vitamin D once, 300 IU of vitamin E twice a day, 500 mg vitamin C four times a day, and one ampule daily of B vitamins [thiamine nitrate 3.1 mg, sodium riboflavin phosphate 4.9 mg (corresponding to vitamin B2 3.6 mg), nicotinamide 40 mg, pyridoxine hydrochloride 4.9 mg (corresponding to vitamin B6 4.0 mg), sodium pantothenate 16.5 mg (corresponding to pantothenic acid 15 mg), sodium ascorbate 113 mg (corresponding to vitamin C 100 mg), biotin 60 μg, folic acid 400 μg, and cyanocobalamin 5 μg]. [irct.ir].
0 0.5 1 1.5 2+ Mortality 26% unadjusted Improvement Relative Risk c19early.org/va Doocy et al. NCT04568499 Vitamin A LATE TREATMENT Is late treatment with vitamin A beneficial for COVID-19? Prospective study of 144 patients in multiple countries (Dec 2020 - Jun 2021) Study underpowered to detect differences Doocy et al., PLOS Global Public Health, doi:10.1371/journal.pgph.0000924 Favors vitamin A Favors control
[Doocy] Prospective study of 144 hospitalized COVID-19 patients in the DRC and South Sudan, showing no significant difference with vitamin A treatment in unadjusted results with only 8 patients receiving vitamin A.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk ICU admission 67% Recovery time 35% Time to viral- 44% c19early.org/va Elkazzaz et al. Vitamin A for COVID-19 RCT LATE TREATMENT Is late treatment with vitamin A beneficial for COVID-19? RCT 40 patients in Egypt (June - August 2020) Faster recovery (p<0.0001) and viral clearance (p<0.0001) Elkazzaz et al., medRxiv, doi:10.1101/2022.03.05.22271959 Favors vitamin A Favors control
[Elkazzaz] RCT with 20 13-cis-retinoic acid patients and 20 control patients, showing faster recovery and viral clearance with treatment. Aerosolized 13-cis-retinoic acid with increasing dose from 0.2 mg/kg/day to 4 mg/kg/day for 14 days, plus oral 13-cis-retinoic acid 20 mg/day. 13-cis retinoic acid, also known as isotretinoin, is a synthetic vitamin A derivative that has been shown to have antiandrogenic effects .
0 0.5 1 1.5 2+ Case 56% Improvement Relative Risk c19early.org/va Holt et al. NCT04330599 COVIDENCE UK Vitamin A Prophylaxis Does vitamin A reduce COVID-19 infections? Prospective study of 15,227 patients in the United Kingdom (May 2020 - Feb 2021) Fewer cases with vitamin A (not stat. sig., p=0.41) Holt et al., Thorax, doi:10.1136/thoraxjnl-2021-217487 Favors vitamin A Favors control
[Holt] 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. NCT04330599. COVIDENCE UK.
0 0.5 1 1.5 2+ Hospitalization 21% Improvement Relative Risk Severe case 21% c19early.org/va Nimer et al. Vitamin A for COVID-19 Prophylaxis Is prophylaxis with vitamin A beneficial for COVID-19? Retrospective 2,148 patients in Jordan (March - July 2021) Lower hospitalization (p=0.4) and severe cases (p=0.36), not stat. sig. Nimer et al., Bosnian J. Basic Medical Sciences, doi:10.17305/bjbms.2021.7009 Favors vitamin A Favors control
[Nimer] Retrospective 2,148 COVID-19 recovered patients in Jordan, showing no significant differences in the risk of severity and hospitalization with vitamin A prophylaxis.
0 0.5 1 1.5 2+ Hospitalization 26% Improvement Relative Risk Recovery, dyspnea 32% Recovery, fever 80% Recovery, body ache 87% Recovery, headache 49% Recovery, weakness and.. 63% Recovery, chest pain 20% Recovery, cough 40% c19early.org/va Rohani et al. IRCT46974 Vitamin A RCT EARLY TREATMENT Is early treatment with vitamin A beneficial for COVID-19? Double-blind RCT 180 patients in Iran (May - September 2020) Trial underpowered to detect differences in serious outcomes Rohani et al., Eastern Mediterranean Health J., doi:10.26719/emhj.22.064 Favors vitamin A Favors control
[Rohani] RCT 91 vitamin A and 91 control patients in Iran, showing improved recovery with treatment. All patients received HCQ. 25,000IU/day oral vitamin A for 10 days.
0 0.5 1 1.5 2+ Mortality -282% Improvement Relative Risk c19early.org/va Sarohan et al. Vitamin A for COVID-19 LATE TREATMENT Is late treatment with vitamin A beneficial for COVID-19? Retrospective 27 patients in Turkey Higher mortality with vitamin A, but no group details Sarohan et al., medRxiv, doi:10.1101/2021.01.30.21250844 Favors vitamin A Favors control
[Sarohan] Retrospective 27 severe COVID-19 patients and 23 non-COVID-19 patients, showing significantly lower vitamin A levels in COVID-19 patients (0.37mg/L vs. 0.52 mg/L, p<0.001). 10 of 27 COVID-19 patients received vitamin A, with higher mortality. Group details are not provided but authors note that 8 of 10 had comorbidities.
0 0.5 1 1.5 2+ Mortality -50% Improvement Relative Risk Ventilation 0% ICU admission 25% Time to clinical response -76% Hospitalization time -8% c19early.org/va Somi et al. IRCT20170117032004N3 Vitamin A RCT LATE Is late treatment with vitamin A beneficial for COVID-19? RCT 30 patients in Iran (April - July 2020) Trial underpowered for serious outcomes Somi et al., Nutrition and Health, doi:10.1177/02601060221129144 Favors vitamin A Favors control
[Somi] RCT 30 hospitalized patients in Iran, showing no significant difference with vitamin A treatment. All patients received HCQ. 50,000 IU/day intramuscular vitamin A for up to 2 weeks.
0 0.5 1 1.5 2+ Mortality 70% Improvement Relative Risk Progression 45% c19early.org/va Tepasse et al. Vitamin A for COVID-19 Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Prospective study of 40 patients in Germany Lower mortality (p=0.042) and progression (p=0.048) Tepasse et al., Nutrients, doi:10.3390/nu13072173 Favors vitamin A Favors control
[Tepasse] Prospective analysis of 40 hospitalized patients and 47 age-matched convalescent patients, showing significantly lower vitamin A levels in critical patients, and significantly lower vitamin A levels in hospitalized patients vs. controls. Low vitamin A levels were significantly associated with ARDS and mortality in hospitalized patients.
0 0.5 1 1.5 2+ Ventilation 71% Improvement Relative Risk ICU admission 61% c19early.org/va Tomasa-Irriguible et al. Vitamin A Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Retrospective 120 patients in Spain (March - May 2020) Lower ventilation (p=0.001) and ICU admission (p=0.004) Tomasa-Irriguible et al., Metabolites, doi:10.3390/metabo11090565 Favors vitamin A Favors control
[Tomasa-Irriguible] Retrospective 120 hospitalized patients in Spain showing vitamin A deficiency associated with higher ICU admission.
0 0.5 1 1.5 2+ Hospitalization 17% Improvement Relative Risk Symptomatic case 11% c19early.org/va Vaisi et al. Vitamin A for COVID-19 Prophylaxis Is prophylaxis with vitamin A beneficial for COVID-19? Retrospective 3,955 patients in Iran Lower hospitalization (p=0.043) and fewer symptomatic cases (p=0.033) Vaisi et al., The Clinical Respiratory J., doi:10.1111/crj.13632 Favors vitamin A Favors control
[Vaisi] 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.
0 0.5 1 1.5 2+ Death/ICU 76% Improvement Relative Risk c19early.org/va Voelkle et al. Vitamin A for COVID-19 Sufficiency Are vitamin A levels associated with COVID-19 outcomes? Prospective study of 57 patients in Switzerland (Mar - Apr 2020) Lower death/ICU with higher vitamin A levels (p=0.004) Voelkle et al., Nutrients, doi:10.3390/nu14091862 Favors vitamin A Favors control
[Voelkle] 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.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were vitamin A, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of vitamin A 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. 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 are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). 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 outcome is considered more important than PCR testing 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 no room for an effective treatment to do better). 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 computed the relative risk when possible, or converted 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 including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome 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 (3.11.3) with scipy (1.10.1), pythonmeta (1.26), numpy (1.24.3), statsmodels (0.14.0), and plotly (5.14.1).
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. 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.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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].
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/vameta.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.
[Al-Sumiadai (B)], 1/31/2021, prospective, Iraq, preprint, 3 authors. risk of progression, 66.7% lower, RR 0.33, p = 0.27, treatment 2 of 50 (4.0%), control 6 of 50 (12.0%), NNT 13, progression to severe disease.
[Al-Sumiadai], 12/31/2020, retrospective, Iraq, peer-reviewed, 3 authors, excluded in exclusion analyses: minimal details of groups provided. risk of death, 85.7% lower, RR 0.14, p = 0.002, treatment 2 of 70 (2.9%), control 14 of 70 (20.0%), NNT 5.8.
[Rohani], 8/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, mean age 39.4, 6 authors, study period 1 May, 2020 - 1 September, 2020, trial IRCT46974. risk of hospitalization, 25.6% lower, RR 0.74, p = 0.63, treatment 8 of 89 (9.0%), control 11 of 91 (12.1%), NNT 32.
risk of no recovery, 31.8% lower, RR 0.68, p = 0.53, treatment 4 of 89 (4.5%), control 6 of 91 (6.6%), NNT 48, dyspnea.
risk of no recovery, 79.6% lower, RR 0.20, p = 0.03, treatment 2 of 89 (2.2%), control 10 of 91 (11.0%), NNT 11, fever.
risk of no recovery, 87.2% lower, RR 0.13, p = 0.01, treatment 1 of 89 (1.1%), control 8 of 91 (8.8%), NNT 13, body ache.
risk of no recovery, 48.9% lower, RR 0.51, p = 0.32, treatment 3 of 89 (3.4%), control 6 of 91 (6.6%), NNT 31, headache.
risk of no recovery, 62.8% lower, RR 0.37, p = 0.05, treatment 4 of 89 (4.5%), control 11 of 91 (12.1%), NNT 13, weakness and fatigue.
risk of no recovery, 20.5% lower, RR 0.80, p = 0.63, treatment 7 of 89 (7.9%), control 9 of 91 (9.9%), NNT 49, chest pain.
risk of no recovery, 40.4% lower, RR 0.60, p = 0.24, treatment 7 of 89 (7.9%), control 12 of 91 (13.2%), NNT 19, cough.
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.
[Beigmohammadi], 11/14/2021, Single Blind Randomized Controlled Trial, Iran, peer-reviewed, 6 authors, study period April 2020 - July 2020, this trial uses multiple treatments in the treatment arm (combined with vitamins B, C, D, E) - results of individual treatments may vary, trial IRCT20200319046819N1. risk of death, 88.9% lower, RR 0.11, p = 0.11, treatment 0 of 30 (0.0%), control 4 of 30 (13.3%), NNT 7.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization >7 days, 41.0% lower, RR 0.59, p = 0.25, treatment 4 of 30 (13.3%), control 16 of 30 (53.3%), NNT 2.5, adjusted per study, odds ratio converted to relative risk.
relative SOFA score @day 7, 45.5% better, RR 0.55, p < 0.001, treatment 30, control 30.
[Doocy], 10/19/2022, prospective, multiple countries, peer-reviewed, 6 authors, study period December 2020 - June 2021, trial NCT04568499 (history), excluded in exclusion analyses: unadjusted results with no group details. risk of death, 26.1% lower, RR 0.74, p = 1.00, treatment 1 of 8 (12.5%), control 23 of 136 (16.9%), NNT 23, unadjusted.
[Elkazzaz], 3/8/2022, Randomized Controlled Trial, Egypt, preprint, 4 authors, study period June 2020 - August 2020. risk of death, 85.7% lower, RR 0.14, p = 0.23, treatment 0 of 20 (0.0%), control 3 of 20 (15.0%), NNT 6.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 66.7% lower, RR 0.33, p = 0.24, treatment 2 of 20 (10.0%), control 6 of 20 (30.0%), NNT 5.0.
recovery time, 35.4% lower, relative time 0.65, p < 0.001, treatment mean 16.3 (±4.5) n=20, control mean 25.23 (±4.72) n=20.
time to viral-, 44.0% lower, relative time 0.56, p < 0.001, treatment mean 13.36 (±1.49) n=20, control mean 23.85 (±4.0) n=20.
[Sarohan], 2/1/2021, retrospective, Turkey, preprint, 4 authors, excluded in exclusion analyses: unadjusted results with no group details, comments suggest significant group differences and confounding. risk of death, 282.5% higher, RR 3.83, p = 0.001, treatment 9 of 10 (90.0%), control 4 of 17 (23.5%).
[Somi], 10/7/2022, Randomized Controlled Trial, Iran, peer-reviewed, mean age 60.2, 7 authors, study period April 2020 - July 2020, trial IRCT20170117032004N3. risk of death, 50.0% higher, RR 1.50, p = 1.00, treatment 3 of 15 (20.0%), control 2 of 15 (13.3%).
risk of mechanical ventilation, no change, RR 1.00, p = 1.00, treatment 3 of 15 (20.0%), control 3 of 15 (20.0%).
risk of ICU admission, 25.0% lower, RR 0.75, p = 1.00, treatment 3 of 15 (20.0%), control 4 of 15 (26.7%), NNT 15.
time to clinical response, 76.0% higher, HR 1.76, p = 0.21, treatment 15, control 15, Kaplan–Meier.
hospitalization time, 8.1% higher, relative time 1.08, p = 0.49, treatment mean 7.33 (±2.31) n=15, control mean 6.78 (±1.84) n=15.
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.
[Al-Sumiadai (C)], 1/31/2021, prospective, Iraq, preprint, 3 authors. risk of case, 64.5% lower, RR 0.36, p < 0.001, treatment 20 of 97 (20.6%), control 65 of 112 (58.0%), NNT 2.7.
[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. risk of case, 56.3% lower, RR 0.44, p = 0.41, treatment 1 of 91 (1.1%), control 445 of 15,136 (2.9%), NNT 54, 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. risk of hospitalization, 21.2% lower, RR 0.79, p = 0.40, treatment 15 of 144 (10.4%), control 204 of 2,004 (10.2%), adjusted per study, odds ratio converted to relative risk, multivariable.
risk of severe case, 20.8% lower, RR 0.79, p = 0.36, treatment 17 of 144 (11.8%), control 243 of 2,004 (12.1%), adjusted per study, odds ratio converted to relative risk, multivariable.
[Vaisi], 5/11/2023, retrospective, Iran, peer-reviewed, 5 authors. risk of hospitalization, 16.7% lower, HR 0.83, p = 0.04, treatment 1,140, control 2,815, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards.
risk of symptomatic case, 10.6% lower, HR 0.89, p = 0.03, treatment 1,140, control 2,815, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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