Covid Analysis, December 2022
•Statistically significant improvements are seen for mortality, ventilation, and recovery. 9 studies from 9 independent teams in 5 different countries show statistically significant improvements in isolation (7 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows 43% [30‑54%] improvement. Results are slightly worse for Randomized Controlled Trials, similar after exclusions, and slightly worse for peer-reviewed studies. Early treatment is more effective than late treatment.
•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 11% of melatonin studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
Melatonin reduces risk for COVID-19 with very high confidence for mortality, ventilation, recovery, and in pooled analysis, low confidence for cases, and very low confidence for ICU admission and hospitalization.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
We analyze all significant studies concerning the use of melatonin 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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for 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.
|CD147||SARS-CoV-2 may enter host cells via the cluster of differentiation 147 (CD147) transmembrane protein. Melatonin inhibits the CD147 signalling pathway [Behl, Su, Wang (B)].|
|Heme oxygenase||COVID-19 risk may be related to low intracellular heme oxygenase (HO-1). Melatonin increases HO-1 and HO-1 has cytoprotective and anti-inflammatory properties [Anderson, Anderson (B), Hooper, Hooper (B), Shi].|
|Inhibiting brain infection||Melatonin has been shown to inhibit SARS-CoV-2 brain infection in a K18-hACE2 mouse model via allosteric binding to ACE2. [Cecon].|
|Limiting type I and III interferons||In a K18-hACE2 mouse model, melatonin improved survival which may be associated with limiting lung production of type I and type III interferons [Cecon (B)].|
|Mucormycosis||Melatonin deficiency may increase the risk of mucormycosis by providing favorable conditions for growth [Sen].|
|Glutathione||Melatonin increases glutathione levels, and glutathione deficiency may be associated with COVID-19 severity [Morvaridzadeh, Polonikov].|
|Cytokine levels||Melatonin may lower pro-inflammatory cytokine levels [Zhang].|
|Immune regulation||Melatonin has immune regulatory properties, enhancing the proliferation and maturation of natural killing cells, T and B lymphocytes, granulocytes, and monocytes [Miller, Zhang].|
|Sleep improvement||Melatonin improves the quality of sleep which may be beneficial for COVID-19 [Lewis, Zhang].|
|Anti‑inflammatory||Melatonin shows anti-inflammatory effects [Zhang].|
|Anti‑oxidation||Melatonin shows anti-oxidative effects which may be beneficial for COVID-19 [Gitto, Gitto (B), Reiter, Wu, Zhang].|
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 2 summarizes the results by treatment stage and with different exclusions. Figure 3, 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, and peer reviewed studies.
|Studies||Early treatment||Late treatment||Prophylaxis||Patients||Authors|
|All studies||18||78% [25‑94%]||45% [27‑58%]||38% [-6‑64%]||14,301||159|
|After exclusions||17||78% [25‑94%]||43% [24‑58%]||38% [-6‑64%]||13,853||155|
|Peer-reviewed||17||78% [25‑94%]||30% [17‑42%]||38% [-6‑64%]||13,353||156|
|Randomized Controlled TrialsRCTs||9||73% [-5‑93%]||23% [0‑41%]||7% [-1368‑94%]||1,022||88|
Melatonin trials for COVID-19 use a very wide range of dosage, from 2mg/day to 500mg/day [Reiter (B)]. Figure 12 shows a mixed-effects meta-regression for efficacy as a function of dose from studies to date, excluding very late stage ICU studies.
Figure 13 shows a comparison of results for RCTs and non-RCT studies. Figure 14 and 15 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
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. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for melatonin 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. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
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].
In summary, 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 example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
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 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Sánchez-González], immortal time bias may significantly affect results.
Heterogeneity in COVID-19 studies arises from many factors including:
[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.
|Post exposure prophylaxis||86% fewer cases [Ikematsu]|
|<24 hours||-33 hours symptoms [Hayden]|
|24-48 hours||-13 hours symptoms [Hayden]|
|Inpatients||-2.5 hours to improvement [Kumar]|
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
[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].
[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].
Figure 18. 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.
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.
[Boulware, Meeus, Meneguesso]. For melatonin, 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.
71% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 36% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 50% improvement, compared to 48% for prospective studies, showing similar results. Figure 19 shows a scatter plot of results for prospective and retrospective studies.
Figure 20 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.
[Lan, Pilia, Tan, Wang], showing significant improvements for recovery and mortality.
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.
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.
Melatonin is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ventilation, and recovery. 9 studies from 9 independent teams in 5 different countries show statistically significant improvements in isolation (7 for the most serious outcome). Meta analysis using the most serious outcome reported shows 43% [30‑54%] improvement. Results are slightly worse for Randomized Controlled Trials, similar after exclusions, and slightly worse for peer-reviewed studies. Early treatment is more effective than late treatment.
[Alizadeh] Small RCT 31 mild/moderate COVID-19 outpatients in Iran, 14 treated with melatonin, showing improved recovery with treatment.
[Alizadeh (B)] RCT 67 extremely late stage intubated patients in Iran, showing lower CRP with melatonin treatment, but no significant difference in outcomes.
[Ameri] RCT 226 ICU patients in Iran, showing lower mortality with melatonin treatment.
[Bologna] Retrospective 40 hospitalized patients in Italy treated with melatonin and 40 control patients, showing improved sleep, reduced delirium, shorter hospitalization and oxygen times, and reduced ICU admission and mortality (not statistically significant).
[Darban] Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences. IRCT20151228025732N52.
[Farnoosh] RCT 44 hospitalized patients in Iran, 24 treated with melatonin, showing faster recovery with treatment. There was no mortality.
[Fogleman] Early terminated low-risk patient RCT with 32 low-dose vitamin C, 32 melatonin, and 34 placebo patients, showing faster resolution of symptoms with melatonin in spline regression analysis, and no significant difference for vitamin C. All patients recovered with no serious outcomes reported. Baseline symptoms scores were higher in the melatonin and vitamin C arms (median 27 and 24 vs. 18 for placebo).
[García-García] PrEP RCT healthcare workers in Spain, showing no significant difference in cases with melatonin prophylaxis. Most cases were asymptomatic or paucisymtomatic, there were two symptomatic cases, no moderate/severe cases, and no hospitalization.
The registered primary outcome is symptomatic cases. Authors report on all cases due to the small number of symptomatic cases. They did not include the original primary outcome results in the paper, but have provided the results via email to a contributor.
The dosage in this trial is very low, 2mg daily. Meta regression suggests higher doses are much more effective.
MeCOVID. EudraCT 2020-001530-35. NCT04353128.
[Hasan] RCT 158 severe condition patients in Iraq, 82 treated with melatonin, showing lower mortality, thrombosis, and sepsis with treatment.
[Hosseini] 40 hospitalized patients in Iran, 20 treated with melatonin, showing faster recovery and attenuated inflammatory cytokines with treatment.
[Jehi] Retrospective 11,672 patients tested for COVID-19 with 818 testing positive, showing significantly lower risk with melatonin use.
[Karimpour-razkenari] Retrospective 31 ICU patients, 12 treated with melatonin, showing lower mortality with treatment, without statistical significance. Melatonin 15mg daily.
[Lissoni] Small study with 30 patients treated with melatonin, cannabidiol, and for 14 patients angiotensin 1-7, compared with an age/sex matched control group during the same period, showing lower hospitalization with treatment.
[Mousavi] RCT 96 hospitalized patients in Iran, 48 treated with melatonin, showing improved sleep quality and SpO2 with treatment. 3mg oral melatonin daily. Authors recommend studies with a higher dose. IRCT20200411047030N1.
[Ramlall] Retrospective 948 intubated patients, 196 treated with melatonin, showing lower mortality with treatment.
[Sánchez-González] Retrospective 2,463 hospitalized patients in Spain, 265 treated with melatonin, showing lower mortality with treatment in PSM analysis, however these results are subject to immortal time bias. Authors excluded from the sample patients that died during the first 72 hours of admission without taking melatonin, and patients that started on melatonin in the last 7 days of their admittance, having completed 75% of their stay.
[Sánchez-Rico] Retrospective database analysis in France with 272 patients treated with melatonin, showing 19% lower mortality after adjustments, without statistical significance. Risk was lower for higher dosage (not statistically significant). Age was only in three age ranges and severe COVID was binary, likely leading to substantial residual confounding. Unadjusted differences were extreme with 60% >80 years old for melatonin compared to 15% for control. Mean daily dose 2.61mg. The title of the paper is incorrect, the most adjusted results show melatonin did reduce mortality (without reaching statistical significance).
[Zhou] PSM observational study with a database of 26,779 patients in the USA, showing significantly lower risk of PCR+ with melatonin usage.
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 melatonin, 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 melatonin 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 (B)]. 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 (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. 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 https://c19early.org/jmeta.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.
|[Alizadeh], 5/29/2021, Single Blind Randomized Controlled Trial, Iran, peer-reviewed, 6 authors.||risk of no recovery, 73.0% lower, RR 0.27, p = 0.06, treatment 2 of 14 (14.3%), control 9 of 17 (52.9%), NNT 2.6, day 14.|
|[Lissoni], 12/30/2020, prospective, Italy, peer-reviewed, 14 authors, this trial uses multiple treatments in the treatment arm (combined with cannabidiol and angiotensin 1-7) - results of individual treatments may vary.||risk of hospitalization, 90.9% lower, RR 0.09, p = 0.05, treatment 0 of 30 (0.0%), control 5 of 30 (16.7%), NNT 6.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).|
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.
|[Alizadeh (B)], 5/13/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 11 authors, study period June 2020 - September 2020.||risk of death, 3.8% lower, RR 0.96, p = 0.73, treatment 28 of 33 (84.8%), control 30 of 34 (88.2%), NNT 30.|
|risk of no extubation, 13.6% lower, RR 0.86, p = 0.19, treatment 26 of 33 (78.8%), control 31 of 34 (91.2%), NNT 8.1.|
|ventilation time, 27.0% lower, relative time 0.73, p = 0.09, treatment 33, control 34.|
|[Ameri], 11/19/2022, Randomized Controlled Trial, Iran, peer-reviewed, 9 authors, study period 1 March, 2021 - 30 November, 2021.||risk of death, 28.8% lower, RR 0.71, p < 0.001, treatment 73 of 109 (67.0%), control 110 of 117 (94.0%), NNT 3.7, primary outcome.|
|risk of mechanical ventilation, 27.6% lower, RR 0.72, p = 0.003, treatment 56 of 109 (51.4%), control 83 of 117 (70.9%), NNT 5.1, primary outcome.|
|clinical status, 25.0% lower, RR 0.75, p = 0.001, treatment 109, control 117, day 14.|
|recovery time, 25.0% lower, relative time 0.75, p = 0.04, treatment 109, control 117.|
|hospitalization time, 28.6% lower, relative time 0.71, p = 0.03, treatment 109, control 117.|
|[Bologna], 12/14/2021, retrospective, Italy, peer-reviewed, 3 authors.||risk of death, 50.0% lower, RR 0.50, p = 0.48, treatment 3 of 40 (7.5%), control 6 of 40 (15.0%), NNT 13.|
|risk of ICU admission, 50.0% lower, RR 0.50, p = 0.48, treatment 3 of 40 (7.5%), control 6 of 40 (15.0%), NNT 13.|
|hospitalization time, 8.7% lower, relative time 0.91, p = 0.05, treatment mean 31.3 (±6.8) n=40, control mean 34.3 (±6.9) n=40.|
|relative sub-intensive hospitalization time, 38.8% better, relative time 0.61, p < 0.001, treatment mean 12.3 (±3.0) n=40, control mean 20.1 (±6.1) n=40.|
|relative NIV time, 58.4% better, relative time 0.42, p < 0.001, treatment mean 5.2 (±3.0) n=40, control mean 12.5 (±4.2) n=40.|
|relative high flow oxygen time, 7.8% better, relative time 0.92, p = 0.35, treatment mean 7.1 (±2.5) n=40, control mean 7.7 (±3.2) n=40.|
|relative sleep time, 18.2% better, RR 0.82, p < 0.001, treatment mean 5.5 (±0.8) n=40, control mean 4.5 (±1.2) n=40.|
|delirium, 33.3% lower, RR 0.67, p < 0.001, treatment mean 2.2 (±1.1) n=40, control mean 3.3 (±1.3) n=40.|
|[Darban], 12/15/2020, Randomized Controlled Trial, Iran, peer-reviewed, 8 authors, this trial uses multiple treatments in the treatment arm (combined with vitamin C and zinc) - results of individual treatments may vary.||risk of progression, 33.3% lower, RR 0.67, p = 1.00, treatment 2 of 10 (20.0%), control 3 of 10 (30.0%), NNT 10.|
|ICU time, 6.0% lower, relative time 0.94, p = 0.30, treatment 10, control 10.|
|[Farnoosh], 6/23/2021, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 12 authors, average treatment delay 7.0 days.||risk of ICU admission, 81.5% lower, RR 0.19, p = 0.20, treatment 0 of 24 (0.0%), control 2 of 20 (10.0%), NNT 10.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).|
|recovery time, 49.0% lower, relative time 0.51, p = 0.004, treatment 24, control 20.|
|risk of no hospital discharge, 44.4% lower, RR 0.56, p = 0.65, treatment 2 of 24 (8.3%), control 3 of 20 (15.0%), NNT 15.|
|time to discharge, 42.9% lower, relative time 0.57, p = 0.02, treatment 24, control 20.|
|[Fogleman], 7/27/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 52.0, 7 authors, study period 5 October, 2020 - 21 June, 2021, average treatment delay 6.0 days, trial NCT04530539 (history).||relative recovery, 17.3% better, RR 0.83, p = 0.38, treatment mean 20.33 (±16.4) n=32, control mean 16.82 (±15.7) n=34, mid-recovery, relative symptom improvement, day 9.|
|[Hasan], 10/12/2021, Randomized Controlled Trial, Iraq, peer-reviewed, 3 authors.||risk of death, 92.9% lower, RR 0.07, p < 0.001, treatment 1 of 82 (1.2%), control 13 of 76 (17.1%), NNT 6.3.|
|[Hosseini], 5/17/2021, prospective, Iran, peer-reviewed, 9 authors.||recovery time, 47.6% lower, relative time 0.52, p = 0.001, treatment 20, control 20.|
|[Karimpour-razkenari], 3/10/2022, retrospective, Iran, peer-reviewed, 6 authors, study period 13 March, 2020 - 30 May, 2020.||risk of death, 39.0% lower, HR 0.61, p = 0.37, treatment 5 of 12 (41.7%), control 13 of 19 (68.4%), NNT 3.7, Kaplan–Meier.|
|ventilation time, 42.9% lower, relative time 0.57, p = 0.13, treatment 12, control 19.|
|ICU time, 1.9% lower, relative time 0.98, p = 0.85, treatment 12, control 19.|
|[Mousavi], 8/30/2021, Randomized Controlled Trial, Iran, peer-reviewed, 7 authors.||risk of death, 66.7% lower, RR 0.33, p = 0.62, treatment 1 of 48 (2.1%), control 3 of 48 (6.2%), NNT 24, day 10.|
|risk of ICU admission, 40.0% lower, RR 0.60, p = 0.41, treatment 6 of 48 (12.5%), control 10 of 48 (20.8%), NNT 12, day 10.|
|[Ramlall], 10/18/2020, retrospective, USA, preprint, 3 authors.||risk of death, 86.9% lower, HR 0.13, p < 0.001, treatment 196, control 752, adjusted per study, multivariable, Cox proportional hazards.|
|[Sánchez-González], 7/20/2021, retrospective, Spain, peer-reviewed, 4 authors, excluded in exclusion analyses: immortal time bias may significantly affect results.||risk of death, 54.4% lower, RR 0.46, p < 0.001, treatment 24 of 224 (10.7%), control 53 of 224 (23.7%), NNT 7.7, odds ratio converted to relative risk, PSM.|
|[Sánchez-Rico], 2/5/2022, retrospective, France, peer-reviewed, 6 authors, study period 24 January, 2020 - 31 October, 2021.||risk of death, 19.0% lower, RR 0.81, p = 0.15, treatment 82 of 272 (30.1%), control 6,487 of 58,290 (11.1%), adjusted per study, model b.|
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.
|[García-García], 2/21/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, peer-reviewed, 25 authors.||risk of symptomatic case, 7.4% lower, RR 0.93, p = 1.00, treatment 1 of 163 (0.6%), control 1 of 151 (0.7%), NNT 2051, primary outcome.|
|risk of case, 108.4% higher, RR 2.08, p = 0.26, treatment 9 of 163 (5.5%), control 4 of 151 (2.6%), post-hoc primary outcome.|
|[Jehi], 6/10/2020, retrospective, USA, peer-reviewed, 8 authors.||risk of case, 58.0% lower, RR 0.42, p < 0.001, treatment 16 of 529 (3.0%), control 802 of 11,143 (7.2%), NNT 24, development cohort.|
|risk of case, 99.7% lower, RR 0.003, p = 0.09, treatment 0 of 18 (0.0%), control 290 of 2,005 (14.5%), NNT 6.9, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), Florida validation cohort.|
|[Zhou], 11/6/2020, retrospective, propensity score matching, USA, peer-reviewed, 18 authors.||risk of case, 21.1% lower, RR 0.79, p = 0.01, treatment 222 of 1,055 (21.0%), control 8,052 of 25,724 (31.3%), NNT 9.7, odds ratio converted to relative risk, PSM.|
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