Chlorpheniramine for COVID-19: real-time meta analysis of 3 studies
Abstract
Statistically significant lower risk is seen for recovery. 2 studies (both from the same team) show significant
improvements.
Meta analysis using the most serious outcome reported shows
56% [46‑64%] lower risk. Results are similar for Randomized Controlled Trials and better for peer-reviewed studies.
Currently there is limited data, with only 806 patients and only 2 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
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.
All data to reproduce this paper and
sources are in the appendix.
Chlorpheniramine for COVID-19 — Highlights
Chlorpheniramine reduces
risk with very high confidence for pooled analysis, low confidence for recovery, and very low confidence for hospitalization.
40th treatment shown effective with ≥3 clinical studies in
December 2022, now with p < 0.00000000001 from 3 studies.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 92
treatments, outcome specific analyses and combined evidence from all studies.
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 chlorpheniramine studies. The marked date indicates the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for 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 injury1-9 and
cognitive deficits3,8, cardiovascular
complications10-12, 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,13-17, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 7,000 compounds may
reduce COVID-19 risk18, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
chlorpheniramine
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, individual outcomes, peer-reviewed studies, and Randomized Controlled Trials (RCTs).
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.
Currently all chlorpheniramine studies use early treatment.
Figure 2. Treatment stages.
An In Vitro study supports the efficacy of chlorpheniramine19.
Preclinical research is an important part of the development of
treatments, however results may be very different in clinical trials.
Preclinical results are not used in this paper.
Table 1 summarizes the results for all studies, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes.
Figure 3, 4, 5, and 6
show forest plots for random effects meta-analysis of
all studies with pooled effects, hospitalization, recovery, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 56% [46‑64%] **** | 3 | 806 | 37 |
| Peer-reviewed studiesPeer-reviewed | 87% [-146‑99%] | 1 | 45 | 5 |
| Randomized Controlled TrialsRCTs | 63% [39‑77%] **** | 2 | 146 | 21 |
| Recovery | 56% [45‑64%] **** | 2 | 761 | 32 |
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Figure 3. 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 4. Random effects meta-analysis for hospitalization.
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Figure 5. Random effects meta-analysis for recovery.
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Figure 6. 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.
Figure 7 shows a comparison of results for RCTs and non-RCT studies.
Random effects meta analysis of RCTs shows
63% improvement,
compared to 54% for other studies.
Figure 8 shows a forest plot for random
effects meta-analysis of all Randomized Controlled Trials.
RCT results are included in Table 1.
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Figure 7. Results for RCTs and non-RCT studies.
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Figure 8. 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.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases22, and
analysis of double-blind RCTs has identified extreme levels of bias23.
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 92 treatments we have analyzed,
64% of RCTs involve very late treatment 5+ days after
onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of
early treatments. They may more accurately represent results for treatments
that require visiting a medical facility, e.g., those requiring intravenous
administration.
Evidence shows that non-RCT 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. 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 may
have a greater effect on results. Ethical issues may also prevent running RCTs
for known effective treatments. For more on issues with RCTs see28,29.
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, 30 have been confirmed in RCTs, with a mean delay of 6.9 months. When considering only low cost treatments, 25 have been confirmed with a delay of 8.2 months. For the 18 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 14 are all consistent with the overall results (benefit or harm), with 12 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is sotrovimab.
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.
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 hours30,31.
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 cases32 |
| <24 hours | -33 hours symptoms33 |
| 24-48 hours | -13 hours symptoms33 |
| Inpatients | -2.5 hours to improvement34 |
Figure 9 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 92 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 9. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 92 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 variants36, for
example the Gamma variant shows significantly different characteristics37-40. 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 variants41,42.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use
of other treatments may significantly affect outcomes, including supplements,
other medications, or other interventions such as prone positioning.
Treatments may be synergistic43-53, therefore
efficacy may depend strongly on combined treatments.
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.
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.
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.
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.
Another way to view pooled analysis is that we are using more of
the available information. Logically we should, and do, use additional
information. For example dose-response and
treatment delay-response relationships provide significant additional evidence
of efficacy that is considered when reviewing the evidence for a
treatment.
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 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 92
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 10 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 11 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 12 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 = 0.0000013 to p = 0.000000015.
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Figure 10. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 11. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 10. 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 5.1 months. When restricting to RCTs only, 54% 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 13 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 13. 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 results57-60.
For chlorpheniramine, 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 14 shows a scatter plot of
results for prospective and retrospective studies.
The median effect size for
retrospective studies is 54% improvement,
compared to 74% for prospective
studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 14. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Chlorpheniramine for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 chlorpheniramine 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 chlorpheniramine 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 alone43-53.
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 factors13-17,
providing many therapeutic targets.
Over 7,000 compounds have been predicted to reduce COVID-19
risk18, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 15 shows an overview of the results for chlorpheniramine
in the context of multiple COVID-19 treatments, and Figure 16 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 15.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 7,000+ proposed treatments show efficacy61.
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Figure 16. Efficacy vs. cost for COVID-19 treatments.
Studies to date show that chlorpheniramine is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for recovery. 2 studies (both from the same team) show significant
improvements.
Meta analysis using the most serious outcome reported shows
56% [46‑64%] lower risk. Results are similar for Randomized Controlled Trials and better for peer-reviewed studies.
Currently there is limited data, with only 806 patients and only 2 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
Sanchez-Gonzalez:
Small RCT showing significantly improved recovery with intranasal chlorpheniramine maleate. Authors also perform an In Vitro study showing efficacy with a highly differentiated three-dimensional model of normal, human-derived tracheal/bronchial epithelial cells.
Valerio-Pascua:
RCT and retrospective study of chlorpheniramine nasal spray for COVID-19. The retrospective study included 660 outpatients showing fewer days with general COVID-19 symptoms, cough, anosmia, and ageusia compared to standard of care alone. The RCT results are listed separately63.
Valerio-Pascua (C):
RCT and retrospective study of chlorpheniramine nasal spray for COVID-19. The RCT included 101 outpatients showing significantly faster recovery with treatment. The retrospective study results are listed separately65.
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 chlorpheniramine 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 chlorpheniramine for COVID-19 that report
a comparison with a control group are included in the main analysis.
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 to66.
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 169.
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
(3.12.5) with
scipy (1.14.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.23.0).
Forest plots are computed using PythonMeta70
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 effective30,31.
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/cpmmeta.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.
| Sanchez-Gonzalez, 12/31/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 44.5, 5 authors. | risk of hospitalization, 87.4% lower, RR 0.13, p = 0.08, treatment 0 of 32 (0.0%), control 2 of 13 (15.4%), NNT 6.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| Valerio-Pascua, 10/18/2022, retrospective, multiple countries, preprint, 16 authors, study period June 2021 - July 2022, trial NCT05520944 (history) (ACCROS-II). | recovery time, 54.3% lower, relative time 0.46, p < 0.001, treatment mean 4.97 (±3.32) n=330, control mean 10.88 (±6.64) n=330. |
| Valerio-Pascua (C), 10/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Honduras, preprint, 16 authors, study period June 2021 - July 2022, trial NCT05449405 (history) (ACCROS-I). | risk of no recovery, 61.4% lower, RR 0.39, p < 0.001, treatment 61, control 40, all symptoms combined. |
| risk of no recovery, 67.2% lower, RR 0.33, p = 0.15, treatment 3 of 61 (4.9%), control 6 of 40 (15.0%), NNT 9.9, day 7, anosmia. | |
| risk of no recovery, 89.1% lower, RR 0.11, p = 0.01, treatment 1 of 61 (1.6%), control 6 of 40 (15.0%), NNT 7.5, day 7, ageusia. | |
| risk of no recovery, 53.2% lower, RR 0.47, p = 0.05, treatment 10 of 61 (16.4%), control 14 of 40 (35.0%), NNT 5.4, day 7, cough. | |
| risk of no recovery, 67.2% lower, RR 0.33, p = 0.21, treatment 2 of 61 (3.3%), control 4 of 40 (10.0%), NNT 15, day 7, fatigue. | |
| risk of no recovery, 59.0% lower, RR 0.41, p = 0.13, treatment 5 of 61 (8.2%), control 8 of 40 (20.0%), NNT 8.5, day 7, nasal congestion. |
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