Covid Analysis, May 23, 2022, DRAFT
https://c19ly.com/meta.html
•Statistically significant improvements are seen for mortality, ICU admission, hospitalization, recovery, and cases. 11 studies from 9 independent teams (all from the same country) show statistically significant
improvements in isolation (4 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows
55% [30‑71%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
•Results are robust — in exclusion sensitivity analysis 6 of 14
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
•Efficacy is highly variant dependent. In Vitro studies suggest a lack of efficacy for omicron [Liu, Sheward, VanBlargan]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
Only 7% of bamlanivimab/etesevimab
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
Bamlanivimab/etesevimab reduces
risk for COVID-19 with very high confidence for hospitalization, recovery, cases, and in pooled analysis, high confidence for mortality and ICU admission, low confidence for viral clearance, and very low confidence for progression.
Efficacy is highly variant dependent. Unlikely to be effective for omicron.
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
bamlanivimab/etesevimab
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.
Variant Dependence
Efficacy is variant dependent, for example in vitro
studies suggest that bamlanivimab/etesevimab is not effective for the omicron
variant [Liu, Sheward, VanBlargan, Zhou].
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, 10, 11, and 12
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, ICU admission, hospitalization, progression, recovery, cases, 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 | 7 | 8 | 87.5% |
69% improvement RR 0.31 [0.16‑0.60] p = 0.00058 |
| Late treatment | 3 | 5 | 60.0% |
29% improvement RR 0.71 [0.35‑1.44] p = 0.35 |
| Prophylaxis | 1 | 1 | 100% |
57% improvement RR 0.43 [0.28‑0.67] p = 0.00021 |
| All studies | 11 | 14 | 78.6% |
55% improvement RR 0.45 [0.29‑0.70] p = 0.00041 |
Table 1. Results by treatment stage.
| Studies | Early treatment | Late treatment | Prophylaxis | Patients | Authors | |
| All studies | 14 | 69% [40‑84%] | 29% [-44‑65%] | 57% [33‑72%] | 24,423 | 197 |
| With exclusions | 12 | 72% [37‑88%] | 29% [-44‑65%] | 57% [33‑72%] | 14,159 | 181 |
| Peer-reviewed | 11 | 69% [40‑84%] | 10% [-164‑69%] | 22,673 | 151 | |
| Randomized Controlled TrialsRCTs | 5 | 79% [19‑95%] | -21% [-218‑54%] | 57% [33‑72%] | 2,784 | 85 |
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 ICU admission.
Figure 7. Random effects meta-analysis for hospitalization.
Figure 8. Random effects meta-analysis for progression.
Figure 9. Random effects meta-analysis for recovery.
Figure 10. Random effects meta-analysis for cases.
Figure 11. Random effects meta-analysis for viral clearance.
Figure 12. 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.
Exclusions
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding studies with
major issues likely to alter results, non-standard studies, and studies where
very minimal detail is currently available. Our bias evaluation is based on
analysis of each study and identifying when there is a significant chance that
limitations will substantially change the outcome of the study. We believe
this can be more valuable than checklist-based approaches such as Cochrane
GRADE, which may underemphasize serious issues not captured in the checklists,
overemphasize issues unlikely to alter outcomes in specific cases (for
example, lack of blinding for an objective mortality outcome, or certain
specifics of randomization with a very large effect size), or be easily
influenced by potential bias. However, they can also be very high
quality.
The studies excluded are as below.
Figure 13 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
[Cooper], unadjusted results with no group details.
[Rubin], significant unadjusted confounding possible.
Figure 13. Random effects meta-analysis for all studies after exclusions.
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.
Randomized Controlled Trials (RCTs)
Figure 14 shows the distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
The median effect size for
RCTs is 57% improvement,
compared to 51% for other studies.
Figure 15 and 16
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 3 summarizes the results.
Figure 14. The distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
Figure 15. 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 16. 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 | 4 | 5 | 80.0% |
45% improvement RR 0.55 [0.26‑1.18] p = 0.12 |
| RCT mortality results | 1 | 2 | 50.0% |
58% improvement RR 0.42 [0.01‑14.21] p = 0.64 |
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 17 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 17. 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 bamlanivimab/etesevimab, 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.
78% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
80% of prospective studies, showing similar results.
The median effect size for
retrospective studies is 51% improvement,
compared to 57% for prospective
studies, suggesting a potential bias towards publishing results showing lower efficacy.
Figure 18 shows a scatter plot of
results for prospective and retrospective studies.
Figure 18. Prospective vs. retrospective studies.
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 19 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 19. 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
Bamlanivimab/etesevimab is
an effective treatment for COVID-19.
Statistically significant improvements are seen for mortality, ICU admission, hospitalization, recovery, and cases. 11 studies from 9 independent teams (all from the same country) show statistically significant
improvements in isolation (4 for the most serious outcome).
Meta analysis using the most serious outcome reported shows
55% [30‑71%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 6 of 14
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
Efficacy is highly variant dependent. In Vitro studies suggest a lack of efficacy for omicron [Liu, Sheward, VanBlargan]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
Study Notes
[ACTIV-3/TICO]
Late stage RCT of LY-CoV555 added to remdesivir, showing non-statistically significant higher mortality with the addition of LY-CoV555. NCT04501978.
[Bariola]
Retrospective 234 patients receiving bamlanivimab and 234 matched controls, showing lower hospitalization and mortality with treatment. Greater benefit was seen with administration within 4 days of their positive COVID-19 test.
[Chew]
RCT 317 outpatients in the USA showing faster viral load and inflammatory biomarker decline, but no significant differences in clinical outcomes. ACTIV-2/A5401. NCT04518410. Supplementary data is not currently available.
[Cooper]
Retrospective 2,879 patients and matched controls in the USA, showing significantly lower mortality and hospitalization with bamlanivimab, bamlanivimab/etesevimab, and casirivimab/imdevimab. There was significantly lower hospitalization with casirivimab/imdevimab compared to bamlanivimab or bamlanivimab/etesevimab. PSM and multivariate analysis is only provided for all treatments combined.
