Melatonin reduces COVID-19 risk: real-time meta analysis of 18 studies
Abstract
Significantly lower risk is seen for mortality, ventilation, and recovery. 9 studies from 9 independent teams in 5 countries show significant
benefit.
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 4 years late).
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
The quality of non-prescription supplements varies widely1,2.
All data and sources to reproduce this analysis are in the appendix.
Melatonin for COVID-19 — Highlights
Melatonin reduces
risk 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.
11th treatment shown effective in
December 2020, now with p = 0.0000002 from 18 studies.
Real-time updates
and corrections with a consistent protocol for 112 treatments. Outcome specific analysis and combined evidence from all
studies including treatment delay, a primary confounding factor.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in 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 22.6 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 injury9-20 and
cognitive deficits12,17, cardiovascular
complications21-24, 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 factorsA,25-31, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk32, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
Melatonin may restore altered redox homeostasis in COVID-1933, modulates type III interferon responses and reduces inflammatory cytokine production in TLR3 receptor agonist stimulated viral inflammation while preserving tissue integrity34, and negatively regulates genes critical for viral entry in lung tissue, including reduced expression of FURIN and components of the CD147 complex, while potentially disrupting TMPRSS2/ACE2-mediated entry mechanisms35.
Melatonin reduces oxidative stress, inhibits NET formation, and protects tissues through anti-inflammatory and antioxidant actions36.
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.
| CD147 | SARS-CoV-2 may enter host cells via the cluster of differentiation 147 (CD147) transmembrane protein. Melatonin inhibits the CD147 signalling pathway37-39. |
| 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 properties40-44. |
| Inhibiting brain infection | Melatonin has been shown to inhibit SARS-CoV-2 brain infection in a K18-hACE2 mouse model via allosteric binding to ACE245. |
| 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 interferons46. |
| Viral phase separation | Melatonin 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 factories47. |
| Mucormycosis | Melatonin deficiency may increase the risk of mucormycosis by providing favorable conditions for growth48. |
| Glutathione | Melatonin increases glutathione levels, and glutathione deficiency may be associated with COVID-19 severity49,50. |
| Cytokine levels | Melatonin may lower pro-inflammatory cytokine levels51. |
| Immune regulation | Melatonin has immune regulatory properties, enhancing the proliferation and maturation of natural killing cells, T and B lymphocytes, granulocytes, and monocytes51,52. |
| Sleep improvement | Melatonin improves the quality of sleep which may be beneficial for COVID-1951,53. |
| Anti‑inflammatory | Melatonin shows anti-inflammatory effects51. |
| Anti‑oxidation | Melatonin shows anti-oxidative effects which may be beneficial for COVID-1951,54-57. |
Melatonin may restore altered redox homeostasis in COVID-1933, modulates type III interferon responses and reduces inflammatory cytokine production in TLR3 receptor agonist stimulated viral inflammation while preserving tissue integrity34, and negatively regulates genes critical for viral entry in lung tissue, including reduced expression of FURIN and components of the CD147 complex, while potentially disrupting TMPRSS2/ACE2-mediated entry mechanisms35.
An In Vitro study supports the efficacy of melatonin34.
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 plots individual results by treatment stage.
Figure 4, 5, 6, 7, 8, 9, 10, 11, and 12
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.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 43% [30‑54%] **** | 18 | 14,301 | 159 |
| After exclusions | 46% [32‑58%] **** | 16 | 13,786 | 144 |
| Peer-reviewed studiesPeer-reviewed | 32% [21‑41%] **** | 17 | 13,353 | 156 |
| Randomized Controlled TrialsRCTs | 26% [4‑43%] * | 9 | 1,022 | 88 |
| Mortality | 48% [27‑63%] *** | 9 | 2,054 | 52 |
| VentilationVent. | 29% [14‑40%] *** | 3 | 324 | 26 |
| ICU admissionICU | 6% [-4‑15%] | 5 | 271 | 36 |
| HospitalizationHosp. | 19% [-9‑40%] | 3 | 366 | 26 |
| Recovery | 30% [15‑43%] *** | 6 | 474 | 54 |
| Cases | 38% [-6‑64%] | 3 | 11,986 | 51 |
| RCT mortality | 25% [-7‑48%] | 4 | 547 | 30 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 78% [25‑94%] * | 45% [27‑58%] **** | 38% [-6‑64%] |
| After exclusions | 78% [25‑94%] * | 49% [29‑64%] **** | 38% [-6‑64%] |
| Peer-reviewed studiesPeer-reviewed | 78% [25‑94%] * | 30% [17‑42%] **** | 38% [-6‑64%] |
| Randomized Controlled TrialsRCTs | 73% [-5‑93%] | 23% [0‑41%] * | 7% [-1368‑94%] |
| Mortality | 48% [27‑63%] *** | ||
| VentilationVent. | 29% [14‑40%] *** | ||
| ICU admissionICU | 6% [-4‑15%] | ||
| HospitalizationHosp. | 91% [-57‑99%] | 16% [-6‑33%] | |
| Recovery | 73% [-5‑93%] | 28% [14‑41%] *** | |
| Cases | 38% [-6‑64%] | ||
| RCT mortality | 25% [-7‑48%] |
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Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
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/day61.
Figure 13 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 13. Mixed-effects meta-regression showing efficacy as a function of dose. Very late stage ICU studies are excluded.
Figure 14 shows a comparison of results for RCTs and non-RCT studies.
Figure 15 and 16
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.
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Figure 14. Results for RCTs and non-RCT studies.
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Figure 15. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 16. Random effects meta-analysis for RCT mortality results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases62, and
analysis of double-blind RCTs has identified extreme levels of bias63.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, reporting, and the
potential for fraud. All of these biases have been observed with COVID-19
RCTs. There is no guarantee that a specific RCT provides a higher level of
evidence.
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 112 treatments we have analyzed,
66% 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.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.92‑1.08]
across 112 treatments65.
Evidence shows that observational studies
can also provide reliable results. Concato et al. found that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates.
We performed a similar analysis across the 112 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.92‑1.08]. Similar results are found for all low-cost treatments, RR
1.02 [0.92‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.92 [0.82‑1.03].
Details can be found in the
supplementary data.
Lee et al. showed that only 14% of the guidelines of the Infectious
Diseases Society of America were based on RCTs. Evaluation of studies relies
on an understanding of the study and potential biases. Limitations in an RCT
can outweigh the benefits, for example excessive dosages, excessive treatment
delays, or remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see69,70.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.1 months (68% with 8.2 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
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.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 17 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 17. Random effects meta-analysis for all studies after exclusions.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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 hours76,77. Baloxavir marboxil studies for influenza
also show that treatment delay is critical — Ikematsu et al. report
an 86% reduction in cases for post-exposure prophylaxis, Hayden et al.
show a 33 hour reduction in the time to alleviation of symptoms for treatment
within 24 hours and a reduction of 13 hours for treatment within 24-48 hours,
and Kumar et al. report only 2.5 hours improvement for inpatient
treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases78 |
| <24 hours | -33 hours symptoms79 |
| 24-48 hours | -13 hours symptoms79 |
| Inpatients | -2.5 hours to improvement80 |
Figure 18 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 112 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 18. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 112 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, for example as in
López-Medina et al.
Efficacy may depend critically on the distribution of
SARS-CoV-2 variants encountered by patients. Risk varies significantly across
variants82, for example the Gamma variant shows significantly
different characteristics83-86. Different
mechanisms of action may be more or less effective depending on variants, for
example the degree to which TMPRSS2 contributes to viral entry can differ
across variants87,88.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Non-prescription supplements may show very wide variations in quality1,2.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however pooled effects are no longer required
for melatonin as of August 2021. Efficacy is now known based on specific outcomes. Efficacy based on specific outcomes was delayed by 8.0 months compared to using pooled outcomes.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes" is not a good reason for
disregarding results.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 112
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 19 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 20 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 21 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.00000032 to p = 0.000000011.
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Figure 19. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 20. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 19. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.0 months. When restricting to RCTs only, 56% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months.
Figure 22 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 22. 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.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
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 results104-107.
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 23 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 23. 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 24. 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 25 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 < 0.05108-115.
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 25. 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 for specific
outcomes and by treatment delay, and we aim to identify key characteristics in
the forest plots and summaries. Results should be viewed in the context of
study characteristics.
Some analyses classify treatment based on early or late
administration, as done here, while others distinguish between mild, moderate,
and severe cases. Viral load does not indicate degree of symptoms — for
example patients may have a high viral load while being asymptomatic. With
regard to treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.
Details of treatment delay per patient is often not available.
For example, a study may treat 90% of patients relatively early, but the
events driving the outcome may come from 10% of patients treated very late.
Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in
the studies performed, for example dose, variants, and conflicts of interest.
Trials with conflicts of interest may use designs better suited to the
preferred outcome.
In some cases, the most serious outcome has very few events,
resulting in lower confidence results being used in pooled analysis, however
the method is simpler and more transparent. This is less critical as the
number of studies increases. Restriction to outcomes with sufficient power may
be beneficial in pooled analysis and improve accuracy when there are few
studies, however we maintain our pre-specified method to avoid any
retrospective changes.
Studies show that combinations of treatments can be highly
synergistic and may result in many times greater efficacy than individual
treatments alone91-102.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
SARS-CoV-2 infection and replication involves a complex
interplay of 50+ host and viral proteins and other
factors25-31, providing many therapeutic
targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk32, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 26 shows an overview of the results for melatonin
in the context of multiple COVID-19 treatments, and Figure 27 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 26.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.5% of 8,000+ proposed treatments show efficacy135.
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Figure 27. Efficacy vs. cost for COVID-19 treatments.
Melatonin is
an effective treatment for COVID-19.
Significantly lower risk is seen for mortality, ventilation, and recovery. 9 studies from 9 independent teams in 5 countries show significant
benefit.
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.
Small RCT 31 mild/moderate COVID-19 outpatients in Iran, 14 treated with melatonin, showing improved recovery with treatment.
RCT 67 extremely late stage intubated patients in Iran, showing lower CRP with melatonin treatment, but no significant difference in outcomes.
RCT 226 ICU patients in Iran, showing lower mortality with melatonin treatment.
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).
150 patient melatonin early treatment RCT with results not reported over 1.5 years after completion.
Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences.
RCT 44 hospitalized patients in Iran, 24 treated with melatonin, showing faster recovery with treatment. There was no mortality.
335 patient melatonin late treatment study with results not reported over 2 years after completion.
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. EudraCT
2020-001530-35.
RCT 158 severe condition patients in Iraq, 82 treated with melatonin, showing lower mortality, thrombosis, and sepsis with treatment.
40 hospitalized patients in Iran, 20 treated with melatonin, showing faster recovery and attenuated inflammatory cytokines with treatment.
Retrospective 11,672 patients tested for COVID-19 with 818 testing positive, showing significantly lower risk with melatonin use.
Retrospective 31 ICU patients, 12 treated with melatonin, showing lower mortality with treatment, without statistical significance. Melatonin 15mg daily.
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.
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.
228 patient melatonin study with results not reported over 2 years after completion.
Estimated 100 patient melatonin late treatment RCT with results not reported over 3 years after estimated completion.
Retrospective 948 intubated patients, 196 treated with melatonin, showing lower mortality with treatment.
18 patient melatonin late treatment RCT with results not reported over 4 years after completion.
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.
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).
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 to137.
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 1140.
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
(3.13.1) with
scipy (1.14.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.4), and plotly (5.24.1).
Forest plots are computed using PythonMeta141
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 (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.0 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 effective76,77.
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 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 1.5 years 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 2 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 4 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. |
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
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