Covid Analysis, May 22, 2022, DRAFT
https://c19early.com/ilmeta.html
•Statistically significant improvement is seen for hospitalization. 2 studies from 2 independent teams in 2 different countries show statistically significant
improvements in isolation (not for the most serious outcome).
•Meta analysis using the most serious outcome reported shows
35% [-132‑82%] improvement, without reaching statistical significance. Results are worse for peer-reviewed studies. Currently all studies are RCTs.
•Currently there is limited data, with only 7 control events for the most serious outcome in trials to date.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
None of the peginterferon lambda studies show zero events in the treatment arm.
Multiple treatments are typically used
in combination, and other treatments
may be more effective.
•No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used.
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•All data to reproduce this paper and
sources are in the appendix.
Highlights
Peginterferon Lambda reduces
risk for COVID-19 with high confidence for hospitalization, low confidence for viral clearance, and very low confidence for progression.
We show traditional outcome specific analyses and combined
evidence from all studies, incorporating treatment delay, a primary
confounding factor in COVID-19 studies.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 42
treatments.
Figure 1. A. Random effects
meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
B. Scatter plot showing the
distribution of effects reported in studies. C. History of all reported
effects (chronological within treatment stages).
Introduction
We analyze all significant studies
concerning the use of
peginterferon lambda
for COVID-19.
Search methods, inclusion criteria, effect
extraction criteria (more serious outcomes have priority), all individual
study data, PRISMA answers, and statistical methods are detailed in
Appendix 1. We present random effects meta-analysis results for all
studies, for studies within each treatment stage, for individual outcomes, for
peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after
exclusions.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Results
Figure 3 shows a visual overview of the results, with details in
Table 1 and Table 2.
Figure 4, 5, 6, 7, 8, 9, and 10
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, hospitalization, progression, recovery, viral clearance, and peer reviewed studies.
Figure 3. Overview of results.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Early treatment | 1 | 3 | 33.3% |
35% improvement RR 0.65 [0.18‑2.32] p = 0.51 |
| All studies | 1 | 3 | 33.3% |
35% improvement RR 0.65 [0.18‑2.32] p = 0.51 |
Table 1. Results by treatment stage.
| Studies | Early treatment | Patients | Authors | |
| All studies | 3 | 35% [-132‑82%] | 2,116 | 63 |
| Peer-reviewed | 2 | 0% [-382‑79%] | 180 | 62 |
| Randomized Controlled TrialsRCTs | 3 | 35% [-132‑82%] | 2,116 | 63 |
Table 2. Results by treatment stage for all studies and with different exclusions.
Figure 4. Random effects meta-analysis for all studies with pooled effects.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 5. Random effects meta-analysis for mortality results.
Figure 6. Random effects meta-analysis for hospitalization.
Figure 7. Random effects meta-analysis for progression.
Figure 8. Random effects meta-analysis for recovery.
Figure 9. Random effects meta-analysis for viral clearance.
Figure 10. Random effects meta-analysis for peer reviewed studies.
[Zeraatkar] analyze 356 COVID-19 trials, finding no
significant evidence that peer-reviewed studies are more trustworthy.
They also show extremely slow review times during a pandemic. Authors
recommend using preprint evidence, with appropriate checks for potential
falsified data, which provides higher certainty much earlier.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Randomized Controlled Trials (RCTs)
Figure 11 shows a chronological history of Randomized Controlled Trials.
Figure 12 and 13
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 3 summarizes the results.
Currently all studies are RCTs, so these are the same as for all studies.
Figure 11. Chronological history of Randomized Controlled Trials.
Figure 12. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 13. Random effects meta-analysis for RCT mortality results.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Randomized Controlled Trials | 1 | 3 | 33.3% |
35% improvement RR 0.65 [0.18‑2.32] p = 0.51 |
| RCT mortality results | 1 | 1 | 100% |
72% improvement RR 0.28 [0.03‑2.49] p = 0.26 |
Table 3. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
The time between infection
or the onset of symptoms and treatment may critically affect how well a
treatment works. For example an antiviral may be very effective when used
early but may not be effective in late stage disease, and may even be harmful.
Oseltamivir, for example, is generally only considered effective for influenza
when used within 0-36 or 0-48 hours [McLean, Treanor].
Figure 14 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 42 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 14. Meta-regression
showing efficacy as a function of treatment delay in COVID-19 studies from 42 treatments. Early
treatment is critical.
Patient demographics.
Details of the
patient population including age and comorbidities may critically affect how
well a treatment works. For example, many COVID-19 studies with relatively
young low-comorbidity patients show all patients recovering quickly with or
without treatment. In such cases, there is little room for an effective
treatment to improve results (as in [López-Medina]).Effect measured.
Efficacy may differ
significantly depending on the effect measured, for example a treatment may be
very effective at reducing mortality, but less effective at minimizing cases
or hospitalization. Or a treatment may have no effect on viral clearance while
still being effective at reducing mortality.Variants.
There are many different
variants of SARS-CoV-2 and efficacy may depend critically on the distribution
of variants encountered by the patients in a study. For example, the Gamma
variant shows significantly different characteristics
[Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be
more or less effective depending on variants, for example the viral entry
process for the omicron variant has moved towards TMPRSS2-independent fusion,
suggesting that TMPRSS2 inhibitors may be less effective
[Peacock, Willett].Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Treatments.
The use of other
treatments may significantly affect outcomes, including anything from
supplements, other medications, or other kinds of treatment such as prone
positioning.The 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 the treatment may be very effective when
used earlier.
In general, by combining heterogeneous studies, as all meta
analyses do, we run the risk of obscuring an effect by including studies where
the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we
expect the estimated effect size to be lower than that for the optimal case.
We do not a priori expect that pooling all studies will create a
positive result for an effective treatment. 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.
Discussion
Publication bias.
Publishing is often biased
towards positive results. Trials with patented drugs may have a financial conflict of interest that
results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to
date (CTRI/2021/05/033864 and CTRI/2021/08/0354242).
For peginterferon lambda, there is currently not
enough data to evaluate publication bias with high confidence.
Funnel plot analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 15 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.05
[Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley].
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 15. Example funnel plot analysis for
simulated perfect trials.
Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do
here), while others distinguish between mild/moderate/severe cases. We note
that 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.Conclusion
Statistically significant improvement is seen for hospitalization. 2 studies from 2 independent teams in 2 different countries show statistically significant
improvements in isolation (not for the most serious outcome).
Meta analysis using the most serious outcome reported shows
35% [-132‑82%] improvement, without reaching statistical significance. Results are worse for peer-reviewed studies. Currently all studies are RCTs.
Currently there is limited data, with only 7 control events for the most serious outcome in trials to date.
Study Notes
[Eiger BioPharmaceuticals]
High-risk outpatient RCT with 916 peginterferon lambda patients and 1,020 control patients, showing significantly lower hospitalization/ER visits with treatment. Single subcutaneous injection. NCT04727424.
For more discussion see [twitter.com].
For more discussion see [twitter.com].
[Feld]
Small outpatient RCT with 30 peginterferon lambda and 30 control patients, showing improved viral clearance with treatment. Single subcutaneous injection of peginterferon lambda 180μg. NCT04354259.
[Jagannathan]
RCT 120 outpatients with mild/moderate COVID-19, showing no significant differences with peginterferon lambda-1a treatment. 180μg subcutaneous peginterferon lambda-1a. NCT04331899.
We performed ongoing searches of PubMed, medRxiv,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.com. Search terms were peginterferon lambda, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed
every few hours with notification of new matches.
All studies regarding the use of peginterferon lambda 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 are used. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms were not used (the next most serious
outcome is used — no studies were excluded). For example, in low-risk
populations with no mortality, a reduction in mortality with treatment is not
possible, however a reduction in hospitalization, for example, is still
valuable.
Clinical outcome is considered more important than PCR testing status. When
basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available (after most or all patients have recovered there is no room
for an effective treatment to do better).
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we computed the relative risk when
possible, or converted to a relative risk according to [Zhang].
Reported confidence intervals and p-values were used when available,
using adjusted values when provided. If multiple types of adjustments are
reported including propensity score matching (PSM), the PSM results are used.
Adjusted primary outcome results have preference over unadjusted results for a more
serious outcome when the adjustments significantly alter results.
When needed, conversion between reported p-values and confidence
intervals followed [Altman, Altman (B)], and Fisher's exact test was
used to calculate p-values for event data. If continuity correction for
zero values is required, we use the reciprocal of the opposite arm with the
sum of the correction factors equal to 1 [Sweeting].
Results are expressed with RR < 1.0 favoring treatment, and using the risk of
a negative outcome when applicable (for example, the risk of death rather than
the risk of survival). If studies only report relative continuous values such
as relative times, the ratio of the time for the treatment group versus the
time for the control group is used. Calculations are done in Python
(3.9.12) with
scipy (1.8.0), pythonmeta (1.26), numpy (1.22.2), statsmodels (0.14.0), and plotly (5.6.0).
Forest plots are computed using PythonMeta [Deng]
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor
(3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment (for example based
on oxygen status or lung involvement), and treatment started within 5 days of
the onset of symptoms. If studies contain a mix of early treatment and late
treatment patients, we consider the treatment time of patients contributing
most to the events (for example, consider a study where most patients are
treated early but late treatment patients are included, and all mortality
events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective
[McLean, Treanor].
A summary of study results is below. Please submit
updates and corrections at https://c19early.com/ilmeta.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.
| [Eiger BioPharmaceuticals], 3/17/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, South America, preprint, 1 author, trial NCT04727424. | risk of death, 72.2% lower, RR 0.28, p = 0.38, treatment 1 of 916 (0.1%), control 4 of 1,020 (0.4%), NNT 353. |
| risk of hospitalization, 43.0% lower, RR 0.57, p = 0.04, treatment 21 of 916 (2.3%), control 41 of 1,020 (4.0%), NNT 58. | |
| risk of hospitalization/ER, 51.2% lower, RR 0.49, p = 0.002, treatment 25 of 916 (2.7%), control 57 of 1,020 (5.6%), NNT 35. | |
| [Feld], 11/12/2020, Double Blind Randomized Controlled Trial, placebo-controlled, Canada, North America, peer-reviewed, 35 authors, study period 18 May, 2020 - 4 September, 2020, average treatment delay 4.3 days, trial NCT04354259. | risk of hospitalization, no change, RR 1.00, p = 1.00, treatment 1 of 30 (3.3%), control 1 of 30 (3.3%). |
| risk of ER visit, 75.0% lower, RR 0.25, p = 0.35, treatment 1 of 30 (3.3%), control 4 of 30 (13.3%), NNT 10.0. | |
| risk of no viral clearance, 66.4% lower, RR 0.34, p = 0.03, treatment 6 of 30 (20.0%), control 11 of 30 (36.7%), NNT 6.0, odds ratio converted to relative risk, adjusted for baseline viral load, day 7. | |
| [Jagannathan], 3/30/2021, Single Blind Randomized Controlled Trial, placebo-controlled, USA, North America, peer-reviewed, 27 authors, average treatment delay 5.0 days, trial NCT04331899. | risk of hospitalization, no change, RR 1.00, p = 1.00, treatment 2 of 60 (3.3%), control 2 of 60 (3.3%), day 28. |
| duration of symptoms, 6.4% higher, HR 1.06, p = 0.76, treatment 60, control 60. | |
| relative change in viral load, 14.0% worse, RR 1.14, p = 0.91, treatment 60, control 60, day 14. |
Supplementary Data
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Please send us corrections, updates, or comments. Vaccines and
treatments are both valuable and complementary. All practical, effective, and
safe means should be used. No treatment, vaccine, or intervention is 100%
available and effective for all current and future variants. Denying the
efficacy of any method increases mortality, morbidity, collateral damage, and
the risk of endemic status. We do not provide medical advice. Before taking
any medication, consult a qualified physician who can provide personalized
advice and details of risks and benefits based on your medical history and
situation. FLCCC and WCH
provide treatment protocols.