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Melatonin for COVID-19: real-time meta analysis of 18 studies

@CovidAnalysis, March 2024, Version 25V25
 
0 0.5 1 1.5+ All studies 43% 18 14,301 Improvement, Studies, Patients Relative Risk Mortality 48% 9 2,054 Ventilation 29% 3 324 ICU admission 6% 5 271 Hospitalization 19% 3 366 Recovery 30% 6 474 Cases 38% 3 11,986 RCTs 26% 9 1,022 RCT mortality 25% 4 547 Peer-reviewed 32% 17 13,353 Prophylaxis 38% 3 11,986 Early 78% 2 91 Late 45% 13 2,224 Melatonin for COVID-19 c19early.org March 2024 after exclusions Favorsmelatonin Favorscontrol
Abstract
Statistically significant lower risk is seen for mortality, ventilation, and recovery. 9 studies from 9 independent teams in 5 countries show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 43% [30‑54%] lower risk. Results are similar for higher quality studies and slightly worse for Randomized Controlled Trials and peer-reviewed studies. Early treatment is more effective than late treatment.
3 RCTs with 268 patients have not reported results (up to 3 years late).
No treatment or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective. The quality of non-prescription supplements can vary widely Crawford, Crighton.
All data to reproduce this paper and sources are in the appendix. Other meta analyses show significant improvements with melatonin for mortality Pilia, Tóth, mechanical ventilation Taha, hospitalization Taha, clinical improvement Taha, and recovery Lan, Wang.
Evolution of COVID-19 clinical evidence Melatonin p=0.0000002 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org March 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
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.
Melatonin was the 10th treatment shown effective with ≥3 clinical studies in December 2020, now known with p = 0.0000002 from 18 studies.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 66 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lissoni 91% 0.09 [0.01-1.57] 20mg hosp. 0/30 5/30 CT​1 Improvement, RR [CI] Dose (1d) Treatment Control Alizadeh (SB RCT) 73% 0.27 [0.07-1.05] 6mg no recov. 2/14 9/17 Boukef (DB RCT) unknown, >1 year late 150 (total) Tau​2 = 0.00, I​2 = 0.0%, p = 0.016 Early treatment 78% 0.22 [0.06-0.75] 2/44 14/47 78% lower risk Ramlall (ICU) 87% 0.13 [0.08-0.22] n/a death 196 (n) 752 (n) Intubated patients Improvement, RR [CI] Dose (1d) Treatment Control Darban (RCT) 33% 0.67 [0.14-3.17] 24mg progression 2/10 3/10 ICU patients CT​1 Hosseini 48% 0.52 [0.36-0.77] 9mg recov. time 20 (n) 20 (n) Farnoosh (DB RCT) 81% 0.19 [0.01-3.65] 9mg ICU 0/24 2/20 Sánchez-González 54% 0.46 [0.28-0.71] varies death 24/224 53/224 Mousavi (RCT) 67% 0.33 [0.04-3.09] 3mg death 1/48 3/48 Hasan (RCT) 93% 0.07 [0.01-0.53] 10mg death 1/82 13/76 Bologna 50% 0.50 [0.13-1.86] 2mg death 3/40 6/40 Sánchez-Rico 19% 0.81 [0.61-1.08] 2mg death Karimpour-.. (ICU) 39% 0.61 [0.21-1.76] 15mg death 5/12 13/19 ICU patients Alizadeh (DB RCT) 4% 0.96 [0.80-1.16] 21mg death 28/33 30/34 Intubated patients Fogleman (DB RCT) 17% 0.83 [0.55-1.25] recovery 32 (n) 34 (n) Ameri (RCT) 29% 0.71 [0.62-0.82] 10mg death 73/109 110/117 ICU patients MELCOVID Rodrígue.. (DB RCT) unknown, >3 years late 18 (total) MELCOV2020 Piovezan (DB RCT) unknown, >3 years late 100 (est. total) Tau​2 = 0.14, I​2 = 81.4%, p < 0.0001 Late treatment 45% 0.55 [0.42-0.73] 137/830 233/1,394 45% lower risk Jehi 58% 0.42 [0.26-0.68] n/a cases 16/529 802/11,143 Improvement, RR [CI] Dose (1d) Treatment Control Zhou (PSM) 21% 0.79 [0.65-0.94] n/a cases MeCOVID García-G.. (DB RCT) 7% 0.93 [0.06-14.7] 2mg symp. case 1/163 1/151 Tau​2 = 0.13, I​2 = 67.2%, p = 0.081 Prophylaxis 38% 0.62 [0.36-1.06] 17/692 803/11,294 38% lower risk All studies 43% 0.57 [0.46-0.70] 156/1,566 1,050/12,735 43% lower risk 18 melatonin COVID-19 studies (+3 unreported RCTs) c19early.org March 2024 Tau​2 = 0.09, I​2 = 77.5%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors melatonin Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lissoni 91% hospitalization CT​1 Improvement Relative Risk [CI] Alizadeh (SB RCT) 73% recovery Boukef (DB RCT) n/a >1 year late Tau​2 = 0.00, I​2 = 0.0%, p = 0.016 Early treatment 78% 78% lower risk Ramlall (ICU) 87% death Intubated patients Darban (RCT) 33% progression ICU patients CT​1 Hosseini 48% recovery Farnoosh (DB RCT) 81% ICU admission Sánchez-González 54% death Mousavi (RCT) 67% death Hasan (RCT) 93% death Bologna 50% death Sánchez-Rico 19% death Karimpour.. (ICU) 39% death ICU patients Alizadeh (DB RCT) 4% death Intubated patients Fogleman (DB RCT) 17% recovery Ameri (RCT) 29% death ICU patients MELCOVID Rodrígu.. (DB RCT) n/a >3 years late MELCOV2020 Piovezan (DB RCT) n/a >3 years late Tau​2 = 0.14, I​2 = 81.4%, p < 0.0001 Late treatment 45% 45% lower risk Jehi 58% case Zhou (PSM) 21% case MeCOVID García-.. (DB RCT) 7% symp. case Tau​2 = 0.13, I​2 = 67.2%, p = 0.081 Prophylaxis 38% 38% lower risk All studies 43% 43% lower risk 18 melatonin C19 studies c19early.org March 2024 Tau​2 = 0.09, I​2 = 77.5%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors melatonin 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. 0.6% of 6,686 proposed treatments show efficacy c19early.org. D. Timeline of results in melatonin studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 5.7 months, compared to using all studies. Efficacy based on specific outcomes was delayed by 8.0 months, compared to using pooled outcomes.
SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological issues Scardua-Silva, Yang, cardiovascular complications Eberhardt, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factors Note A, Malone, Murigneux, Lv, Lui, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 6,000 compounds may reduce COVID-19 risk c19early.org (B), either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
We analyze all significant controlled studies 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, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
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.
Table 1 shows potential mechanisms of action for the treatment of COVID-19 using melatonin.
Table 1. Melatonin mechanisms of action. Submit updates.
CD147SARS-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 oxygenaseCOVID-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 infectionMelatonin 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 interferonsIn 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).
Viral phase separationMelatonin may be beneficial via regulation of viral phase separation, such as modulating the liquid-liquid phase separation of the SARS-CoV-2 nucleocapsid protein to inhibit formation of viral replication factories Loh.
MucormycosisMelatonin deficiency may increase the risk of mucormycosis by providing favorable conditions for growth Sen.
GlutathioneMelatonin increases glutathione levels, and glutathione deficiency may be associated with COVID-19 severity Morvaridzadeh, Polonikov.
Cytokine levelsMelatonin may lower pro-inflammatory cytokine levels Zhang.
Immune regulationMelatonin has immune regulatory properties, enhancing the proliferation and maturation of natural killing cells, T and B lymphocytes, granulocytes, and monocytes Miller, Zhang.
Sleep improvementMelatonin improves the quality of sleep which may be beneficial for COVID-19 Lewis, Zhang.
Anti‑inflammatoryMelatonin shows anti-inflammatory effects Zhang.
Anti‑oxidationMelatonin shows anti-oxidative effects which may be beneficial for COVID-19 Gitto, Gitto (B), Reiter, Wu, Zhang.
An In Silico study supports the efficacy of melatonin Kumar Yadalam.
2 In Vivo animal studies support the efficacy of melatonin Cecon, Cecon (B).
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 for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Table 3 shows results by treatment stage. 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.
Table 2. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies43% [30‑54%]
****
18 14,301 159
After exclusions46% [32‑58%]
****
16 13,786 144
Peer-reviewed studiesPeer-reviewed32% [21‑41%]
****
17 13,353 156
Randomized Controlled TrialsRCTs26% [4‑43%]
*
9 1,022 88
Mortality48% [27‑63%]
***
9 2,054 52
VentilationVent.29% [14‑40%]
***
3 324 26
ICU admissionICU6% [-4‑15%]5 271 36
HospitalizationHosp.19% [-9‑40%]3 366 26
Recovery30% [15‑43%]
***
6 474 54
Cases38% [-6‑64%]3 11,986 51
RCT mortality25% [-7‑48%]4 547 30
Table 3. 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<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies78% [25‑94%]
*
45% [27‑58%]
****
38% [-6‑64%]
After exclusions78% [25‑94%]
*
49% [29‑64%]
****
38% [-6‑64%]
Peer-reviewed studiesPeer-reviewed78% [25‑94%]
*
30% [17‑42%]
****
38% [-6‑64%]
Randomized Controlled TrialsRCTs73% [-5‑93%]23% [0‑41%]
*
7% [-1368‑94%]
Mortality48% [27‑63%]
***
VentilationVent.29% [14‑40%]
***
ICU admissionICU6% [-4‑15%]
HospitalizationHosp.91% [-57‑99%]16% [-6‑33%]
Recovery73% [-5‑93%]28% [14‑41%]
***
Cases38% [-6‑64%]
RCT mortality25% [-7‑48%]
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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 peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Davidson et al. also showed no important difference between meta analysis results of preprints and peer-reviewed publications for COVID-19, based on 37 meta analyses including 114 trials.
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. Results suggest that the dosage used in many trials to date is lower than optimal.
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Figure 12. Mixed-effects meta-regression showing efficacy as a function of dose. Very late stage ICU studies are excluded.
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. RCT results are included in Table 2 and Table 3.
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.
RCTs are expensive and many RCTs are funded by pharmaceutical companies or interests closely aligned with pharmaceutical companies. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.
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 66 treatments we have analyzed, 63% 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).
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. RCTs for 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, and may be greater when the risk of a serious outcome is overstated. This bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or 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, 44 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 44 treatments with statistically significant efficacy/harm, 28 have been confirmed in RCTs, with a mean delay of 5.7 months. When considering only low cost treatments, 23 have been confirmed with a delay of 6.9 months. For the 16 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 13 are all consistent with the overall results (benefit or harm), with 10 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 13. Results for RCTs and non-RCT studies.
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Figure 14. 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 15. Random effects meta-analysis for RCT mortality results.
3 melatonin RCTs have not reported results Boukef, Piovezan, Rodríguez-Rubio. The trials report a total of 268 patients, with 2 trials having actual enrollment of 168, and the other estimated. The results are delayed from 1 year to over 3 years.
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), and can be easily influenced by potential bias.
The studies excluded are as below. Figure 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
Alizadeh, extremely late treatment, over 75% control mortality.
Sánchez-González, immortal time bias may significantly affect results.
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Figure 16. 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 4. 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 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 66 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 66 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in 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 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.
Currently, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.6 months. When restricting to RCTs only, 50% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.1 months.
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Figure 18. 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 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.
Figure 19 shows a scatter plot of results for prospective and retrospective studies. 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.
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Figure 19. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Studies for melatonin were primarily late treatment studies, in contrast with typical patented treatments that were tested with early treatment as recommended.
Figure 20. Patented treatments received mostly early treatment studies, while low cost treatments were typically tested for late treatment.
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 21 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 21. 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. Melatonin for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 melatonin 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 melatonin 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, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
2 of 18 studies combine treatments. The results of melatonin alone may differ. 1 of 9 RCTs use combined treatment. Other meta analyses show significant improvements with melatonin for mortality Pilia, Tóth, mechanical ventilation Taha, hospitalization Taha, clinical improvement Taha, and recovery Lan, Wang.
Many reviews cover melatonin for COVID-19, presenting additional background on mechanisms and related results, including Alomari, Behl (B), Camp, Castle, Charaa, Cross, DiNicolantonio, Hosseinzadeh, Langen, Lempesis, Loh, Ramos, Reiter (B), Reiter (C), Shneider, Tan, Zhang.
Melatonin is an effective treatment for COVID-19. Statistically significant lower risk is seen for mortality, ventilation, and recovery. 9 studies from 9 independent teams in 5 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 43% [30‑54%] lower risk. Results are similar for higher quality studies and slightly worse for Randomized Controlled Trials and peer-reviewed studies. Early treatment is more effective than late treatment.
Other meta analyses show significant improvements with melatonin for mortality Pilia, Tóth, mechanical ventilation Taha, hospitalization Taha, clinical improvement Taha, and recovery Lan, Wang.
0 0.5 1 1.5 2+ Recovery 73% Improvement Relative Risk Melatonin  Alizadeh et al.  EARLY TREATMENT  RCT Is early treatment with melatonin beneficial for COVID-19? RCT 31 patients in Iran (June - August 2020) Improved recovery with melatonin (not stat. sig., p=0.057) c19early.org Alizadeh et al., Iranian J. Allergy, A.., May 2021 Favors melatonin Favors control
Alizadeh (B): Small RCT 31 mild/moderate COVID-19 outpatients in Iran, 14 treated with melatonin, showing improved recovery with treatment.
0 0.5 1 1.5 2+ Mortality 4% Improvement Relative Risk Extubation 14% Ventilation time 27% Melatonin  Alizadeh et al.  INTUBATED PATIENTS  DB RCT Is very late treatment with melatonin beneficial for COVID-19? Double-blind RCT 67 patients in Iran (June - September 2020) Improved recovery (p=0.19) and shorter ventilation (p=0.091), not sig. c19early.org Alizadeh et al., J. Taibah University .., May 2022 Favors melatonin Favors control
Alizadeh: RCT 67 extremely late stage intubated patients in Iran, showing lower CRP with melatonin treatment, but no significant difference in outcomes.
0 0.5 1 1.5 2+ Mortality 29% primary Improvement Relative Risk Ventilation 28% primary Clinical status 25% Recovery time 25% Hospitalization time 29% Melatonin  Ameri et al.  ICU PATIENTS  RCT Is very late treatment with melatonin beneficial for COVID-19? RCT 226 patients in Iran (March - November 2021) Lower mortality (p<0.0001) and ventilation (p=0.0027) c19early.org Ameri et al., Inflammopharmacology, Nov 2022 Favors melatonin Favors control
Ameri: RCT 226 ICU patients in Iran, showing lower mortality with melatonin treatment.
0 0.5 1 1.5 2+ Mortality 50% Improvement Relative Risk ICU admission 50% Hospitalization time 9% Sub-intensive hospitalizat.. 39% NIV time 58% High flow oxygen time 8% Sleep time 18% Delirium 33% Melatonin for COVID-19  Bologna et al.  LATE TREATMENT Is late treatment with melatonin beneficial for COVID-19? Retrospective 80 patients in Italy Lower need for oxygen therapy with melatonin (p<0.000001) c19early.org Bologna et al., J. Clinical Medicine, Dec 2021 Favors melatonin Favors control
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).
Boukef: 150 patient melatonin early treatment RCT with results not reported over 1 year after completion.
0 0.5 1 1.5 2+ Progression 33% Improvement Relative Risk ICU time 6% Melatonin  Darban et al.  ICU PATIENTS  RCT Is very late treatment with melatonin + vitamin C and zinc beneficial for COVID-19? RCT 20 patients in Iran (April - June 2020) Trial underpowered to detect differences c19early.org Darban et al., J. Cellular & Molecular.., Dec 2020 Favors melatonin Favors control
Darban: Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences.
0 0.5 1 1.5 2+ ICU admission 81% Improvement Relative Risk Recovery time 49% Discharge 44% Time to discharge 43% Melatonin  Farnoosh et al.  LATE TREATMENT  DB RCT Is late treatment with melatonin beneficial for COVID-19? Double-blind RCT 44 patients in Iran (April - June 2020) Faster recovery with melatonin (p=0.004) c19early.org Farnoosh et al., Archives of Medical R.., Jun 2021 Favors melatonin Favors control
Farnoosh: RCT 44 hospitalized patients in Iran, 24 treated with melatonin, showing faster recovery with treatment. There was no mortality.
Fernandez-Tresguerres: 335 patient melatonin late treatment study with results not reported over 1.5 years after completion.
0 0.5 1 1.5 2+ Recovery 17% Improvement Relative Risk Melatonin  Fogleman et al.  LATE TREATMENT  DB RCT Is late treatment with melatonin beneficial for COVID-19? Double-blind RCT 66 patients in the USA (October 2020 - June 2021) Improved recovery with melatonin (not stat. sig., p=0.38) c19early.org Fogleman et al., The J. the American B.., Jul 2022 Favors melatonin Favors control
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).
0 0.5 1 1.5 2+ Symp. case 7% primary Improvement Relative Risk Case -108% post-hoc primary Melatonin  MeCOVID  Prophylaxis  DB RCT Is prophylaxis with melatonin beneficial for COVID-19? Double-blind RCT 314 patients in Spain (April - December 2020) More cases with melatonin (not stat. sig., p=0.26) c19early.org García-García et al., J. Clinical Medi.., Feb 2022 Favors melatonin Favors control
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. EudraCT 2020-001530-35.
0 0.5 1 1.5 2+ Mortality 93% Improvement Relative Risk Melatonin  Hasan et al.  LATE TREATMENT  RCT Is late treatment with melatonin beneficial for COVID-19? RCT 158 patients in Iraq (December 2020 - June 2021) Lower mortality with melatonin (p=0.0004) c19early.org Hasan et al., Int. J. Infectious Disea.., Oct 2021 Favors melatonin Favors control
Hasan: RCT 158 severe condition patients in Iraq, 82 treated with melatonin, showing lower mortality, thrombosis, and sepsis with treatment.
0 0.5 1 1.5 2+ Recovery time 48% Improvement Relative Risk Melatonin  Hosseini et al.  LATE TREATMENT Is late treatment with melatonin beneficial for COVID-19? Prospective study of 40 patients in Iran Faster recovery with melatonin (p=0.001) c19early.org Hosseini et al., European J. Pharmacol.., May 2021 Favors melatonin Favors control
Hosseini: 40 hospitalized patients in Iran, 20 treated with melatonin, showing faster recovery and attenuated inflammatory cytokines with treatment.
0 0.5 1 1.5 2+ Case 58% Improvement Relative Risk Case (b) 100% Melatonin for COVID-19  Jehi et al.  Prophylaxis Does melatonin reduce COVID-19 infections? Retrospective 11,672 patients in the USA Fewer cases with melatonin (p=0.000077) c19early.org Jehi et al., Chest, June 2020 Favors melatonin Favors control
Jehi: Retrospective 11,672 patients tested for COVID-19 with 818 testing positive, showing significantly lower risk with melatonin use.
0 0.5 1 1.5 2+ Mortality 39% Improvement Relative Risk Ventilation time 43% ICU time 2% Melatonin  Karimpour-razkenari et al.  ICU PATIENTS Is very late treatment with melatonin beneficial for COVID-19? Retrospective 31 patients in Iran (March - May 2020) Lower mortality (p=0.37) and shorter ventilation (p=0.13), not sig. c19early.org Karimpour-razkenari et al., Annals of .., Mar 2022 Favors melatonin Favors control
Karimpour-razkenari: Retrospective 31 ICU patients, 12 treated with melatonin, showing lower mortality with treatment, without statistical significance. Melatonin 15mg daily.
0 0.5 1 1.5 2+ Hospitalization 91% Improvement Relative Risk Melatonin  Lissoni et al.  EARLY TREATMENT Is early treatment with melatonin + combined treatments beneficial for COVID-19? Prospective study of 60 patients in Italy Lower hospitalization with melatonin + combined treatments (not stat. sig., p=0.052) c19early.org Lissoni et al., J. Infectiology, December 2020 Favors melatonin Favors control
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.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk ICU admission 40% Melatonin  Mousavi et al.  LATE TREATMENT  RCT Is late treatment with melatonin beneficial for COVID-19? RCT 96 patients in Iran (April - June 2020) Lower ICU admission with melatonin (not stat. sig., p=0.41) c19early.org Mousavi et al., J. Medical Virology, Aug 2021 Favors melatonin Favors control
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.
Oral: 228 patient melatonin study with results not reported over 2 years after completion.
Piovezan: Estimated 100 patient melatonin late treatment RCT with results not reported over 3 years after estimated completion.
0 0.5 1 1.5 2+ Mortality 87% Improvement Relative Risk Melatonin  Ramlall et al.  INTUBATED PATIENTS Is very late treatment with melatonin beneficial for COVID-19? Retrospective 948 patients in the USA Lower mortality with melatonin (p<0.000001) c19early.org Ramlall et al., medRxiv, October 2020 Favors melatonin Favors control
Ramlall: Retrospective 948 intubated patients, 196 treated with melatonin, showing lower mortality with treatment.
Rodríguez-Rubio: 18 patient melatonin late treatment RCT with results not reported over 3 years after completion.
0 0.5 1 1.5 2+ Mortality 54% Improvement Relative Risk Melatonin  Sánchez-González et al.  LATE TREATMENT Is late treatment with melatonin beneficial for COVID-19? Retrospective 448 patients in Spain Lower mortality with melatonin (p=0.0009) c19early.org Sánchez-González, July 2021 Favors melatonin Favors control
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.
0 0.5 1 1.5 2+ Mortality 19% Improvement Relative Risk Melatonin  Sánchez-Rico et al.  LATE TREATMENT Is late treatment with melatonin beneficial for COVID-19? Retrospective 58,562 patients in France (January 2020 - October 2021) Lower mortality with melatonin (not stat. sig., p=0.15) c19early.org Sánchez-Rico et al., J. Travel Medicine, Feb 2022 Favors melatonin Favors control
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).
0 0.5 1 1.5 2+ Case 21% Improvement Relative Risk Melatonin for COVID-19  Zhou et al.  Prophylaxis Does melatonin reduce COVID-19 infections? PSM retrospective 26,779 patients in the USA Fewer cases with melatonin (p=0.011) c19early.org Zhou et al., PLOS Biology, November 2020 Favors melatonin Favors control
Zhou: PSM observational study with a database of 26,779 patients in the USA, showing significantly lower risk of PCR+ with melatonin usage.
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms are melatonin and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of 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 have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcomes are considered more important than viral test status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available. After most or all patients have recovered there is little or no room for an effective treatment to do better, however faster recovery is valuable. If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang (B). Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed Altman, Altman (B), and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 Sweeting. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.12.2) with scipy (1.12.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.1), and plotly (5.19.0).
Forest plots are computed using PythonMeta Deng with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I2 statistic. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.0 is used to parse PDF documents.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective McLean, Treanor.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/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 (B), 5/29/2021, Single Blind Randomized Controlled Trial, Iran, peer-reviewed, 6 authors, study period 30 June, 2020 - 5 August, 2020. 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.
Boukef, 2/28/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Tunisia, trial NCT05670444 (history). 150 patient RCT with results unknown and over 1 year late.
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, 5/13/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 11 authors, study period June 2020 - September 2020, excluded in exclusion analyses: extremely late treatment, over 75% control mortality. 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, study period 7 April, 2020 - 8 June, 2020, this trial uses multiple treatments in the treatment arm (combined with vitamin C and zinc) - results of individual treatments may vary, trial IRCT20151228025732N52. 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, study period 25 April, 2020 - 5 June, 2020, 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.
Fernandez-Tresguerres, 3/31/2022, Spain, trial NCT05596617 (history). 335 patient study with results unknown and over 1.5 years late.
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, study period 1 December, 2020 - 1 June, 2021. 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, study period 14 April, 2020 - 15 June, 2020. 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.
Piovezan, 3/1/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, trial NCT04470297 (history) (MELCOV2020). Estimated 100 patient RCT with results unknown and over 3 years late.
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.
Rodríguez-Rubio, 8/5/2020, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, trial NCT04568863 (history) (MELCOVID). 18 patient RCT with results unknown and over 3 years late.
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, study period April 2020 - December 2020, trial NCT04353128 (history) (MeCOVID). 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.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. 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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