Bamlanivimab/etesevimab for COVID-19: real-time meta analysis of 21 studies
Abstract
Statistically significant lower risk is seen for mortality, hospitalization, recovery, cases, and viral clearance. 16 studies from 14 independent teams (all from the same country) show significant
improvements.
Meta analysis using the most serious outcome reported shows
47% [25‑62%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Results are consistent with early treatment being more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 9 of 21
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 omicron1-5. mAb use may create new variants that spread globally6,7, and may be associated with prolonged viral loads, clinical deterioration, and immune escape7-10.
Prescription treatments have been preferentially used by patients at lower risk11. Retrospective studies may overestimate efficacy, for example patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
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.
Amani et al. present another meta analysis for bamlanivimab/etesevimab, showing significant improvements for mortality and hospitalization.
Bamlanivimab/etesevimab for COVID-19 — Highlights
Bamlanivimab/etesevimab reduces
risk with very high confidence for hospitalization and in pooled analysis, high confidence for mortality and viral clearance, low confidence for recovery and cases, and very low confidence for ICU admission and progression.
Efficacy is variant dependent.
22nd treatment shown effective with ≥3 clinical studies in
May 2021, now with p = 0.00036 from 21 studies, and recognized in 7 countries.
Outcome specific analyses and combined evidence from all
studies, incorporating treatment delay, a primary confounding factor.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 109
treatments.
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 bamlanivimab/etesevimab studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and one or more specific outcome.
mAb treatments
AdintrevimabAmubarv../r..
Bamlaniv../e..
Bebtelovimab
Casirivimab/i..
Pemivibart
Ravulizumab
Regdanvimab
Siltuximab
Sotrovimab
Tixagev../c..
Vilobelimab
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 injury13-23 and
cognitive deficits15,20, cardiovascular
complications24-26, 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,27-32, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk33, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies 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, 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.
Extensive mutations in SARS-CoV-2 have resulted in variants that evade neutralizing
antibodies from monoclonal antibody treatments34,35, resulting in efficacy
that is highly variant dependent.
Table 1 shows efficacy by variant for several monoclonal
antibodies. This table covers earlier SARS-CoV-2 variants and has not been
updated for more recent variants.
| Bamlanivimab/ etesevimab |
Casirivimab/ imdevimab |
Sotrovimab | Bebtelovimab | Tixagevimab/ cilgavimab |
|
|---|---|---|---|---|---|
| Alpha B.1.1.7 | |||||
| Beta/ Gamma BA1.351/ P.1 | |||||
| Delta B.1.617.2 | |||||
| Omicron BA.1/ BA.1.1 | |||||
| Omicron BA.2 | |||||
| Omicron BA.5 | |||||
| Omicron BA.4.6 | |||||
| Omicron BQ.1.1 |
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, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 47% [25‑62%] *** | 21 | 35,320 | 275 |
| After exclusions | 50% [28‑65%] *** | 19 | 22,629 | 259 |
| Peer-reviewed studiesPeer-reviewed | 46% [19‑64%] ** | 18 | 33,632 | 230 |
| Randomized Controlled TrialsRCTs | 39% [-24‑70%] | 6 | 3,039 | 110 |
| Mortality | 54% [13‑76%] * | 13 | 32,261 | 160 |
| ICU admissionICU | 28% [-11‑53%] | 2 | 15,055 | 29 |
| HospitalizationHosp. | 42% [30‑53%] **** | 15 | 31,733 | 183 |
| Recovery | 11% [3‑18%] ** | 2 | 1,129 | 59 |
| Viral | 38% [2‑60%] * | 3 | 1,354 | 81 |
| RCT mortality | 58% [-1321‑99%] | 2 | 1,349 | 34 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 52% [23‑70%] ** | 29% [-44‑65%] | 57% [33‑72%] *** |
| After exclusions | 57% [28‑75%] ** | 29% [-44‑65%] | 57% [33‑72%] *** |
| Peer-reviewed studiesPeer-reviewed | 54% [26‑72%] ** | 12% [-85‑58%] | |
| Randomized Controlled TrialsRCTs | 64% [-72‑92%] | -21% [-218‑54%] | 57% [33‑72%] *** |
| Mortality | 64% [12‑85%] * | 31% [-76‑73%] | |
| ICU admissionICU | 17% [-30‑47%] | 49% [-9‑76%] | |
| HospitalizationHosp. | 47% [33‑57%] **** | 32% [-7‑57%] | |
| Recovery | 11% [3‑18%] ** | -14% [-45397‑100%] | |
| Viral | 44% [-48‑79%] | 26% [10‑38%] ** | |
| RCT mortality | 95% [10‑100%] * | -100% [-483‑31%] |
Loading..
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.
Loading..
Loading..
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.
Loading..
Loading..
Figure 5. Random effects meta-analysis for mortality results.
Loading..
Figure 6. Random effects meta-analysis for ICU admission.
Loading..
Figure 7. Random effects meta-analysis for hospitalization.
Loading..
Figure 8. Random effects meta-analysis for progression.
Loading..
Figure 9. Random effects meta-analysis for recovery.
Loading..
Figure 10. Random effects meta-analysis for cases.
Loading..
Figure 11. Random effects meta-analysis for viral clearance.
Loading..
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.
Figure 13 shows a comparison of results for RCTs and non-RCT studies.
Figure 14 and 15
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
RCT results are included in Table 2 and Table 3.
Loading..
Figure 13. Results for RCTs and non-RCT studies.
Loading..
Figure 14. 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.
Loading..
Figure 15. 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 biases39, and
analysis of double-blind RCTs has identified extreme levels of bias40.
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 109 treatments we have analyzed,
65% 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.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.92‑1.08]
across 109 treatments42.
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 109 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 see46,47.
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 16 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.
Loading..
Figure 16. 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 hours50,51.
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 cases52 |
| <24 hours | -33 hours symptoms53 |
| 24-48 hours | -13 hours symptoms53 |
| Inpatients | -2.5 hours to improvement54 |
Figure 17 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 109 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Loading..
Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 109 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 variants56, for
example the Gamma variant shows significantly different characteristics57-60. 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 variants61,62.
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 synergistic63-74, 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.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however note that pooled effects are no longer
required for bamlanivimab/etesevimab as of May 2021.
Efficacy is now known based on specific 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.
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 109
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 18 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 19 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 20 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 = 0.00000042 to p = 0.00000002.
Loading..
Figure 18. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Loading..
Figure 19. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Loading..
Figure 18. 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, 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 21 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Loading..
Loading..
Figure 21. 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.
Wilcock et al. show that COVID-19 prescription treatments have been preferentially used by patients at lower risk. Retrospective studies may overestimate efficacy, and data for accurate adjustment may not be available. For example, patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
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.
Figure 22 shows a scatter plot of
results for prospective and retrospective studies.
Prospective studies show 39% [-24‑70%] improvement in meta
analysis, compared to 50% [24‑67%] for retrospective
studies, suggesting possible positive publication bias, with a non-significant trend towards retrospective studies
reporting higher efficacy.
Loading..
Figure 22. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-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 23 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 < 0.0578-85.
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 23. Example funnel plot analysis for simulated perfect trials.
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 alone63-74.
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.
1 of 21 studies
combine treatments. The results of
bamlanivimab/etesevimab
alone may differ.
1 of 6 RCTs use combined treatment.
Amani et al. present another meta analysis for bamlanivimab/etesevimab, showing significant improvements for mortality and hospitalization.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors27-32,
providing many therapeutic targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk33, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 24 shows an overview of the results for bamlanivimab/etesevimab
in the context of multiple COVID-19 treatments, and Figure 25 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Loading..
Figure 24.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 8,000+ proposed treatments show efficacy87.
Loading..
Loading..
Figure 25. Efficacy vs. cost for COVID-19 treatments.
Bamlanivimab/etesevimab is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for mortality, hospitalization, recovery, cases, and viral clearance. 16 studies from 14 independent teams (all from the same country) show significant
improvements.
Meta analysis using the most serious outcome reported shows
47% [25‑62%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Results are consistent with early treatment being more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 9 of 21
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 omicron1-5. mAb use may create new variants that spread globally6,7, and may be associated with prolonged viral loads, clinical deterioration, and immune escape7-10.
Amani et al. present another meta analysis for bamlanivimab/etesevimab, showing significant improvements for mortality and hospitalization.
Prescription treatments have been preferentially used by patients at lower risk11. Retrospective studies may overestimate efficacy, for example patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
ACTIV-3/TICO LY-CoV555 study group:
Late stage RCT of LY-CoV555 added to remdesivir, showing non-statistically significant higher mortality with the addition of LY-CoV555.
Alam:
Retrospective 246 nursing home patients showing lower mortality with early bamlanivimab treatment.
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.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene91,92, vitamin D93, etc.) — either because the physician recommending bamlanivimab/etesevimab also recommended them, or because the patient seeking out bamlanivimab/etesevimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene91,92, vitamin D93, etc.) — either because the physician recommending bamlanivimab/etesevimab also recommended them, or because the patient seeking out bamlanivimab/etesevimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Chew:
RCT 317 outpatients in the USA showing faster viral load and inflammatory biomarker decline, but no significant differences in clinical outcomes.
Cooper:
Retrospective 2,879 patients and matched controls in the USA, showing significantly lower mortality and hospitalization with monoclonal antibody treatment (bamlanivimab, bamlanivimab/etesevimab, or 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.
Corwin:
Retrospective 780 bamlanivimab patients and 5,337 patients not receiving treatment, showing lower hospitalization and ER visits with treatment.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene91,92, vitamin D93, etc.) — either because the physician recommending bamlanivimab/etesevimab also recommended them, or because the patient seeking out bamlanivimab/etesevimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene91,92, vitamin D93, etc.) — either because the physician recommending bamlanivimab/etesevimab also recommended them, or because the patient seeking out bamlanivimab/etesevimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Dale:
Retrospective 75 COVID+ patients in a skilled nursing facility in the USA, 56 treated within a median of 2 days from symptom onset with bamlanivimab, showing significantly lower mortality with treatment.
Delasobera:
Retrospective 438 patients in the USA, 253 treated with bamlanivimab, showing significantly lower hospitalization with treatment.
Dougan:
RCT showing improved viral clearance with bamlanivimab/etesevimab combined with bebtelovimab. Results refer to the placebo controlled portion of the trial.
Dougan (B):
Results from the BLAZE-1 RCT of combined bamlanivimab/etesevimab, showing significantly lower mortality and combined mortality/hospitalization with treatment. NCT04427501.
Fivelstad:
Retrospective 335 outpatients with mild to moderate COVID-19 and at least one high-risk comorbidity, showing significantly lower hospitalization with bamlanivimab treatment compared to the control group.
Ganesh:
Retrospective 2,335 bamlanivimab patients and 2,335 PSM controls in the USA, showing significantly lower hospitalization with treatment.
Gottlieb:
RCT for LY-CoV555 monotherapy and LY-CoV555/LY-CoV016 combination therapy with 592 patients showing lower hospitalization/ER visits with treatment.
For viral load at day 11, a statistically significant reduction was found with combination therapy but not monotherapy.
For viral load at day 11, a statistically significant reduction was found with combination therapy but not monotherapy.
Karr:
Retrospective 40 outpatients showing improvement in symptoms and lower risk of hospitalization/ER visits with bamlanivimab, without statistical significance.
Different counts for hospitalization are provided in the abstract and text: "Three of 40 (7.5%) patients in the treatment group required inpatient admission" and "In the treatment group, 4 of 40 (10%) patients were hospitalized after infusion."
Different counts for hospitalization are provided in the abstract and text: "Three of 40 (7.5%) patients in the treatment group required inpatient admission" and "In the treatment group, 4 of 40 (10%) patients were hospitalized after infusion."
Kip:
Retrospective 2,571 patients treated with mAbs in the USA, and 5,135 control patients, showing lower combined mortality/hospitalization for bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, sotrovimab, and bebtelovimab, with statistical significance only for casirivimab/imdevimab.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene91,92, vitamin D93, etc.) — either because the physician recommending bamlanivimab/etesevimab also recommended them, or because the patient seeking out bamlanivimab/etesevimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene91,92, vitamin D93, etc.) — either because the physician recommending bamlanivimab/etesevimab also recommended them, or because the patient seeking out bamlanivimab/etesevimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Leavitt:
Retrospective 136 outpatients showing bamlanivimab reduced emergency department visits at 28 days, but not hospitalizations, compared to a control group prior to authoritzation in patients with mild to moderate COVID-19.
Lilly:
Press release on the BLAZE-2 trial at nursing homes showing significantly lower symptomatic COVID-19 with treatment.
Priest:
Retrospective 379 bamlanivimab patients and 379 matched controls in the USA, showing no significant differences with treatment.
Rubin:
Retrospective database analysis of 1257 PCR+ outpatients with age ≥65, BMI≥35, 191 receiving bamlanivimab via lottery. Authors note that the alpha variant was most common during the study period, and that efficacy against other variants can be much lower. Authors note confounding due to prioritization in the lottery and differential reporting in the database.
Webb:
Retrospective 479 patients treated with bamlanivimab showing lower mortality, hospital admission, and emergency department visits with treatment. Authors incorrectly state that "no other COVID-19 therapies for ambulatory patients have proven effective".
Wilden:
Retrospective 395 patients in the USA receiving casirivimab/imdevimab or bamlanivimab, showing lower risk of hospitalization with treatment, statistically significant for casirivimab/imdevimab.
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 bamlanivimab, etesevimab 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 bamlanivimab/etesevimab 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 to110.
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 1113.
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
(3.13.0) 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 PythonMeta114
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 effective50,51.
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/lmeta.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.
| Alam, 5/10/2021, retrospective, USA, peer-reviewed, mean age 82.4, 9 authors, study period 15 November, 2020 - 31 January, 2021. | risk of death, 75.0% lower, OR 0.25, p = 0.03, treatment 160, control 86, RR approximated with OR. |
| risk of hospitalization, 65.0% lower, OR 0.35, p = 0.08, treatment 160, control 86, RR approximated with OR. | |
| Cooper, 10/8/2021, retrospective, USA, peer-reviewed, 9 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 7.0% higher, RR 1.07, p = 0.86, treatment 12 of 2,900 (0.4%), control 33 of 8,534 (0.4%), unadjusted, all bamlanivimab. |
| risk of death, 45.3% lower, RR 0.55, p = 1.00, treatment 1 of 473 (0.2%), control 33 of 8,534 (0.4%), NNT 571, unadjusted, bamlanivimab/etesevimab. | |
| risk of death, 17.2% higher, RR 1.17, p = 0.59, treatment 11 of 2,427 (0.5%), control 33 of 8,534 (0.4%), unadjusted, bamlanivimab. | |
| risk of ICU admission, 16.9% lower, RR 0.83, p = 0.51, treatment 24 of 2,900 (0.8%), control 85 of 8,534 (1.0%), NNT 594, unadjusted, all bamlanivimab. | |
| risk of ICU admission, 57.5% lower, RR 0.42, p = 0.33, treatment 2 of 473 (0.4%), control 85 of 8,534 (1.0%), NNT 174, unadjusted, bamlanivimab/etesevimab. | |
| risk of ICU admission, 9.0% lower, RR 0.91, p = 0.81, treatment 22 of 2,427 (0.9%), control 85 of 8,534 (1.0%), NNT 1117, unadjusted, bamlanivimab. | |
| risk of hospitalization, 24.2% lower, RR 0.76, p < 0.001, treatment 181 of 2,900 (6.2%), control 703 of 8,534 (8.2%), NNT 50, unadjusted, all bamlanivimab, primary outcome. | |
| risk of hospitalization, 5.0% lower, RR 0.95, p = 0.86, treatment 37 of 473 (7.8%), control 703 of 8,534 (8.2%), NNT 241, unadjusted, bamlanivimab/etesevimab, primary outcome. | |
| risk of hospitalization, 28.0% lower, RR 0.72, p < 0.001, treatment 144 of 2,427 (5.9%), control 703 of 8,534 (8.2%), NNT 43, unadjusted, bamlanivimab. | |
| Corwin, 6/10/2021, retrospective, USA, peer-reviewed, 8 authors, study period 23 November, 2020 - 17 January, 2021. | risk of death, 80.5% lower, RR 0.20, p = 0.08, treatment 1 of 780 (0.1%), control 35 of 5,337 (0.7%), NNT 190. |
| risk of hospitalization, 39.4% lower, RR 0.61, p < 0.001, treatment 57 of 780 (7.3%), control 490 of 5,337 (9.2%), odds ratio converted to relative risk. | |
| Dale, 2/9/2022, retrospective, USA, peer-reviewed, 14 authors, average treatment delay 2.0 days. | risk of death, 89.2% lower, RR 0.11, p = 0.010, treatment 5 of 56 (8.9%), control 9 of 19 (47.4%), NNT 2.6, adjusted per study, odds ratio converted to relative risk, multivariable. |
| risk of progression, 86.3% lower, RR 0.14, p = 0.002, treatment 6 of 56 (10.7%), control 10 of 19 (52.6%), NNT 2.4, adjusted per study, odds ratio converted to relative risk, oxygen therapy, multivariable. | |
| risk of progression, 53.8% lower, RR 0.46, p = 0.35, treatment 6 of 56 (10.7%), control 3 of 19 (15.8%), adjusted per study, odds ratio converted to relative risk, ER visit or hospitalization, multivariable. | |
| Delasobera, 1/27/2022, retrospective, USA, peer-reviewed, 12 authors. | risk of death, 119.4% higher, RR 2.19, p = 0.64, treatment 3 of 253 (1.2%), control 1 of 185 (0.5%). |
| risk of hospitalization, 52.2% lower, RR 0.48, p = 0.01, treatment 17 of 253 (6.7%), control 26 of 185 (14.1%), NNT 14. | |
| risk of progression, 19.9% lower, RR 0.80, p = 0.52, treatment 23 of 253 (9.1%), control 21 of 185 (11.4%), NNT 44, ER followup visit. | |
| Dougan, 3/12/2022, Randomized Controlled Trial, USA, preprint, 22 authors, study period 19 April, 2021 - 19 July, 2021, this trial uses multiple treatments in the treatment arm (combined with bebtelovimab) - results of individual treatments may vary, trial NCT04634409 (history) (BLAZE-4). | risk of hospitalization, 51.2% higher, RR 1.51, p = 0.68, treatment 3 of 127 (2.4%), control 2 of 128 (1.6%). |
| relative viral load reduction, 9.5% better, RR 0.91, p < 0.001, treatment mean 4.0 (±0.2) n=125, control mean 3.62 (±0.2) n=128, day 7. | |
| relative viral load reduction, 24.2% better, RR 0.76, p < 0.001, treatment mean 2.81 (±0.19) n=125, control mean 2.13 (±0.19) n=128, day 5. | |
| relative viral load reduction, 12.3% better, RR 0.88, p < 0.001, treatment mean 1.38 (±0.2) n=125, control mean 1.21 (±0.2) n=128, day 3. | |
| risk of no viral clearance, 35.5% lower, RR 0.65, p = 0.17, treatment 16 of 127 (12.6%), control 25 of 128 (19.5%), NNT 14, persistently high viral load, day 7, primary outcome. | |
| Dougan (B), 10/7/2021, Double Blind Randomized Controlled Trial, USA, peer-reviewed, 33 authors, study period 4 September, 2020 - 8 December, 2020, average treatment delay 4.0 days, trial NCT04427501 (history). | risk of death, 94.7% lower, RR 0.05, p = 0.002, treatment 0 of 518 (0.0%), control 9 of 517 (1.7%), NNT 57, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), COVID-19 deaths. |
| risk of death/hospitalization, 69.5% lower, RR 0.30, p < 0.001, treatment 11 of 518 (2.1%), control 36 of 517 (7.0%), NNT 21, primary outcome. | |
| recovery time, 11.1% lower, relative time 0.89, p = 0.007, treatment 518, control 517, sustained resolution of symptoms. | |
| risk of no viral clearance, 66.6% lower, RR 0.33, p < 0.001, treatment 50 of 508 (9.8%), control 147 of 499 (29.5%), NNT 5.1, day 7, persistently high viral load. | |
| Fivelstad, 7/31/2022, retrospective, USA, peer-reviewed, 6 authors. | risk of death, 144.2% higher, RR 2.44, p = 1.00, treatment 1 of 335 (0.3%), control 0 of 148 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). |
| risk of hospitalization, 62.9% lower, RR 0.37, p < 0.001, treatment 21 of 335 (6.3%), control 25 of 148 (16.9%), NNT 9.4. | |
| Gottlieb, 1/21/2021, Randomized Controlled Trial, USA, peer-reviewed, 27 authors, study period 17 June, 2020 - 6 October, 2020, average treatment delay 4.0 days. | risk of hospitalization/ER, 70.6% lower, RR 0.29, p = 0.046, treatment 4 of 101 (4.0%), control 7 of 52 (13.5%), NNT 11, LY-CoV555 all dosages. |
| risk of hospitalization/ER, 79.9% lower, RR 0.20, p = 0.13, treatment 1 of 37 (2.7%), control 7 of 52 (13.5%), NNT 9.3, LY-CoV555 700mg. | |
| risk of hospitalization/ER, 75.2% lower, RR 0.25, p = 0.25, treatment 1 of 30 (3.3%), control 7 of 52 (13.5%), NNT 9.9, LY-CoV555 2800mg. | |
| risk of hospitalization/ER, 56.3% lower, RR 0.44, p = 0.31, treatment 2 of 34 (5.9%), control 7 of 52 (13.5%), NNT 13, LY-CoV555 7000mg. | |
| risk of hospitalization/ER, 91.8% lower, RR 0.08, p = 0.04, treatment 0 of 31 (0.0%), control 7 of 52 (13.5%), NNT 7.4, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), LY-CoV555/LY-CoV016. | |
| Karr, 5/16/2021, retrospective, USA, peer-reviewed, 5 authors, study period 3 December, 2020 - 12 January, 2021. | risk of hospitalization, 40.0% lower, RR 0.60, p = 0.52, treatment 4 of 40 (10.0%), control 1 of 6 (16.7%), NNT 15, day 30. |
| risk of hospitalization/ER, 62.5% lower, RR 0.38, p = 0.22, treatment 5 of 40 (12.5%), control 2 of 6 (33.3%), NNT 4.8, day 30. | |
| Kip, 4/4/2023, retrospective, USA, peer-reviewed, 16 authors, study period 8 December, 2020 - 31 August, 2022. | risk of death/hospitalization, 15.0% lower, RR 0.85, p = 0.54, treatment 20 of 349 (5.7%), control 47 of 695 (6.8%), NNT 97, bamlanivimab/etesevimab, alpha and delta variants, day 28. |
| risk of death/hospitalization, 31.0% lower, RR 0.69, p = 0.17, treatment 17 of 221 (7.7%), control 49 of 442 (11.1%), NNT 29, bamlanivimab, pre-alpha and alpha variants, day 28. | |
| Leavitt, 11/19/2021, retrospective, USA, peer-reviewed, median age 69.0, 9 authors, study period 2 December, 2020 - 8 January, 2021. | risk of hospitalization, 29.9% lower, RR 0.70, p = 0.60, treatment 6 of 136 (4.4%), control 9 of 143 (6.3%), NNT 53, day 28. |
| risk of emergency care, 41.6% lower, RR 0.58, p = 0.04, treatment 20 of 136 (14.7%), control 36 of 143 (25.2%), NNT 9.6, day 28. | |
| Rubin, 11/3/2021, retrospective, USA, peer-reviewed, 7 authors, study period 9 December, 2020 - 25 February, 2021, average treatment delay 6.0 days, excluded in exclusion analyses: significant unadjusted confounding possible, conflicts of interest: research funding from the drug patent holder, consulting for the pharmaceutical industry. | risk of death, 44.2% lower, RR 0.56, p = 1.00, treatment 1 of 191 (0.5%), control 10 of 1,066 (0.9%), NNT 241. |
| risk of hospitalization, 65.3% lower, RR 0.35, p = 0.04, treatment 16 of 191 (8.4%), control 121 of 1,065 (11.4%), odds ratio converted to relative risk, IPTW weighted logistic regression. | |
| Webb, 6/23/2021, retrospective, USA, peer-reviewed, 14 authors. | risk of death, 79.7% lower, RR 0.20, p = 0.09, treatment 1 of 479 (0.2%), control 57 of 5,536 (1.0%), NNT 122. |
| risk of hospitalization, 52.7% lower, RR 0.47, p < 0.001, treatment 22 of 479 (4.6%), control 538 of 5,536 (9.7%), NNT 20. | |
| risk of hospitalization/ER, 26.8% lower, RR 0.73, p < 0.001, treatment 65 of 479 (13.6%), control 1,018 of 5,536 (18.4%), NNT 21, odds ratio converted to relative risk, primary outcome. | |
| Wilden, 3/31/2022, retrospective, USA, peer-reviewed, 9 authors, study period December 2020 - July 2021. | risk of hospitalization, 51.0% lower, OR 0.49, p = 0.06, adjusted per study, multivariable, RR approximated with OR. |
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.
| ACTIV-3/TICO LY-CoV555 study group, 12/22/2020, Randomized Controlled Trial, USA, peer-reviewed, 1 author, study period 5 August, 2020 - 13 October, 2020, average treatment delay 7.0 days, trial NCT04501978 (history) (ACTIV-3). | risk of death, 100% higher, HR 2.00, p = 0.22, treatment 9 of 163 (5.5%), control 5 of 151 (3.3%), adjusted per study, proportional hazards regression. |
| Bariola, 3/30/2021, retrospective, USA, preprint, 22 authors. | risk of death, 66.8% lower, RR 0.33, p = 0.05, treatment 4 of 234 (1.7%), control 12 of 234 (5.1%), NNT 29, odds ratio converted to relative risk. |
| risk of death/hospitalization, 64.3% lower, RR 0.36, p < 0.001, treatment 16 of 234 (6.8%), control 45 of 234 (19.2%), NNT 8.1, odds ratio converted to relative risk, primary outcome. | |
| risk of hospitalization, 60.7% lower, RR 0.39, p = 0.001, treatment 15 of 234 (6.4%), control 39 of 234 (16.7%), NNT 9.8, odds ratio converted to relative risk. | |
| Chew, 8/22/2022, Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 26 authors, study period 19 August, 2020 - 15 November, 2020, average treatment delay 6.0 days, trial NCT04427501 (history) (ACTIV-2/A5401). | risk of hospitalization, 25.5% lower, RR 0.75, p = 0.60, treatment 6 of 159 (3.8%), control 8 of 158 (5.1%), NNT 78, combined. |
| risk of hospitalization, 52.1% lower, RR 0.48, p = 0.43, treatment 2 of 48 (4.2%), control 4 of 46 (8.7%), NNT 22, 7000mg, day 28. | |
| risk of hospitalization, 0.9% higher, RR 1.01, p = 1.00, treatment 4 of 111 (3.6%), control 4 of 112 (3.6%), 700mg, day 28. | |
| relative time to symptom improvement, 13.5% higher, relative time 1.14, p = 0.97, treatment 48, control 46, 7000mg, primary outcome. | |
| relative time to symptom improvement, 17.1% higher, relative time 1.17, p = 0.08, treatment 111, control 112, 700mg, primary outcome. | |
| risk of progression, 0.6% higher, RR 1.01, p = 1.00, treatment 42 of 48 (87.5%), control 40 of 46 (87.0%), at least one symptom more severe than baseline, 7000mg. | |
| risk of progression, 2.0% lower, RR 0.98, p = 0.62, treatment 102 of 111 (91.9%), control 105 of 112 (93.8%), NNT 54, at least one symptom more severe than baseline, 700mg. | |
| viral load, 25.6% lower, relative load 0.74, p = 0.002, treatment 48, control 46, 7000mg, day 3. | |
| viral load, 35.3% lower, relative load 0.65, p = 0.07, treatment 111, control 112, 700mg, day 3. | |
| Ganesh, 10/1/2021, retrospective, USA, peer-reviewed, median age 63.0, 20 authors. | risk of death, 74.4% lower, RR 0.26, p = 0.11, treatment 2 of 1,789 (0.1%), control 8 of 1,832 (0.4%), NNT 308, day 28. |
| risk of ICU admission, 48.8% lower, RR 0.51, p = 0.10, treatment 10 of 1,789 (0.6%), control 20 of 1,832 (1.1%), NNT 188, day 28. | |
| risk of hospitalization, 37.4% lower, RR 0.63, p = 0.01, treatment 44 of 1,789 (2.5%), control 72 of 1,832 (3.9%), NNT 68, day 28, primary outcome. | |
| Priest, 1/27/2022, retrospective, propensity score matching, USA, peer-reviewed, 5 authors, study period October 2020 - March 2021, average treatment delay 6.0 days. | risk of death, no change, RR 1.00, p = 1.00, treatment 6 of 379 (1.6%), control 6 of 379 (1.6%). |
| risk of hospitalization, 3.9% higher, RR 1.04, p = 0.86, treatment 79 of 379 (20.8%), control 76 of 379 (20.1%), all-cause hospital revisit. | |
| risk of hospitalization/ER, 5.0% higher, OR 1.05, p = 0.86, treatment 379, control 379, RR approximated with OR. |
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.
| Lilly, 1/21/2021, Randomized Controlled Trial, USA, preprint, 1 author. | risk of symptomatic case, 57.0% lower, RR 0.43, p < 0.001, treatment 483, control 482, group sizes estimated because they were not supplied. |
| risk of symptomatic case, 80.0% lower, RR 0.20, p < 0.001, treatment 150, control 149, nursing home residents, group sizes estimated because they were not supplied. |
Liu et al., Striking Antibody Evasion Manifested by the Omicron Variant of SARS-CoV-2, bioRxiv, doi:10.1101/2021.12.14.472719.
Sheward et al., Variable loss of antibody potency against SARS-CoV-2 B.1.1.529 (Omicron), bioRxiv, doi:10.1101/2021.12.19.473354.
VanBlargan et al., An infectious SARS-CoV-2 B.1.1.529 Omicron virus escapes neutralization by several therapeutic monoclonal antibodies, bioRxiv, doi:10.1101/2021.12.15.472828.
Pochtovyi et al., In Vitro Efficacy of Antivirals and Monoclonal Antibodies against SARS-CoV-2 Omicron Lineages XBB.1.9.1, XBB.1.9.3, XBB.1.5, XBB.1.16, XBB.2.4, BQ.1.1.45, CH.1.1, and CL.1, Vaccines, doi:10.3390/vaccines11101533.
Haars et al., Prevalence of SARS-CoV-2 Omicron Sublineages and Spike Protein Mutations Conferring Resistance against Monoclonal Antibodies in a Swedish Cohort during 2022–2023, Microorganisms, doi:10.3390/microorganisms11102417.
Focosi et al., Analysis of SARS-CoV-2 mutations associated with resistance to therapeutic monoclonal antibodies that emerge after treatment, Drug Resistance Updates, doi:10.1016/j.drup.2023.100991.
Leducq et al., Spike protein genetic evolution in patients at high-risk of severe COVID-19 treated by monoclonal antibodies, The Journal of Infectious Diseases, doi:10.1093/infdis/jiad523.
Choudhary et al., Emergence of SARS-CoV-2 Resistance with Monoclonal Antibody Therapy, medRxiv, doi:10.1101/2021.09.03.21263105.
Günther et al., Variant-specific humoral immune response to SARS-CoV-2 escape mutants arising in clinically severe, prolonged infection, medRxiv, doi:10.1101/2024.01.06.24300890.
Casadevall et al., Single monoclonal antibodies should not be used for COVID-19 therapy: a call for antiviral stewardship, Clinical Infectious Diseases, doi:10.1093/cid/ciae408.
Wilcock et al., Clinical Risk and Outpatient Therapy Utilization for COVID-19 in the Medicare Population, JAMA Health Forum, doi:10.1001/jamahealthforum.2023.5044.
Amani et al., Efficacy and safety of bamlanivimab in patients with COVID-19: A systematic review and meta-analysis, World Journal of Virology, doi:10.5501/wjv.v13.i1.88660.
Yang et al., SARS-CoV-2 infection causes dopaminergic neuron senescence, Cell Stem Cell, doi:10.1016/j.stem.2023.12.012.
Scardua-Silva et al., Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19, Scientific Reports, doi:10.1038/s41598-024-52005-7.
Hampshire et al., Cognition and Memory after Covid-19 in a Large Community Sample, New England Journal of Medicine, doi:10.1056/NEJMoa2311330.
Duloquin et al., Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2, Journal of Clinical Medicine, doi:10.3390/jcm13051397.
Sodagar et al., Pathological Features and Neuroinflammatory Mechanisms of SARS-CoV-2 in the Brain and Potential Therapeutic Approaches, Biomolecules, doi:10.3390/biom12070971.
Sagar et al., COVID-19-associated cerebral microbleeds in the general population, Brain Communications, doi:10.1093/braincomms/fcae127.
Verma et al., Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations, bioRxiv, doi:10.1101/2024.06.02.596989.
Panagea et al., Neurocognitive Impairment in Long COVID: A Systematic Review, Archives of Clinical Neuropsychology, doi:10.1093/arclin/acae042.
Ariza et al., COVID-19: Unveiling the Neuropsychiatric Maze—From Acute to Long-Term Manifestations, Biomedicines, doi:10.3390/biomedicines12061147.
Vashisht et al., Neurological Complications of COVID-19: Unraveling the Pathophysiological Underpinnings and Therapeutic Implications, Viruses, doi:10.3390/v16081183.
Ahmad et al., Neurological Complications and Outcomes in Critically Ill Patients With COVID-19: Results From International Neurological Study Group From the COVID-19 Critical Care Consortium, The Neurohospitalist, doi:10.1177/19418744241292487.
Eberhardt et al., SARS-CoV-2 infection triggers pro-atherogenic inflammatory responses in human coronary vessels, Nature Cardiovascular Research, doi:10.1038/s44161-023-00336-5.
Van Tin et al., Spike Protein of SARS-CoV-2 Activates Cardiac Fibrogenesis through NLRP3 Inflammasomes and NF-κB Signaling, Cells, doi:10.3390/cells13161331.
Borka Balas et al., COVID-19 and Cardiac Implications—Still a Mystery in Clinical Practice, Reviews in Cardiovascular Medicine, doi:10.31083/j.rcm2405125.
Malone et al., Structures and functions of coronavirus replication–transcription complexes and their relevance for SARS-CoV-2 drug design, Nature Reviews Molecular Cell Biology, doi:10.1038/s41580-021-00432-z.
Murigneux et al., Proteomic analysis of SARS-CoV-2 particles unveils a key role of G3BP proteins in viral assembly, Nature Communications, doi:10.1038/s41467-024-44958-0.
Lv et al., Host proviral and antiviral factors for SARS-CoV-2, Virus Genes, doi:10.1007/s11262-021-01869-2.
Lui et al., Nsp1 facilitates SARS-CoV-2 replication through calcineurin-NFAT signaling, Virology, doi:10.1128/mbio.00392-24.
Niarakis et al., Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches, Frontiers in Immunology, doi:10.3389/fimmu.2023.1282859.
Katiyar et al., SARS-CoV-2 Assembly: Gaining Infectivity and Beyond, Viruses, doi:10.3390/v16111648.
Hattab et al., SARS-CoV-2 journey: from alpha variant to omicron and its sub-variants, Infection, doi:10.1007/s15010-024-02223-y.
Focosi (B), D., Monoclonal Antibody Therapies Against SARS-CoV-2: Promises and Realities, Current Topics in Microbiology and Immunology, doi:10.1007/82_2024_268.
Davis et al., The Promise and Peril of Anti-SARS-CoV-2 Monoclonal Antibodies, Clinical Infectious Diseases, doi:10.1093/cid/ciac902.
Zeraatkar et al., Consistency of covid-19 trial preprints with published reports and impact for decision making: retrospective review, BMJ Medicine, doi:10.1136/bmjmed-2022-0003091.
Davidson et al., No evidence of important difference in summary treatment effects between COVID-19 preprints and peer-reviewed publications: a meta-epidemiological study, Journal of Clinical Epidemiology, doi:10.1016/j.jclinepi.2023.08.011.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Als-Nielsen et al., Association of Funding and Conclusions in Randomized Drug Trials, JAMA, doi:10.1001/jama.290.7.921.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
Lee et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Cooper et al., Real-world Assessment of 2,879 COVID-19 Patients Treated with Monoclonal Antibody Therapy: A Propensity Score-Matched Cohort Study, Open Forum Infectious Diseases, doi:10.1093/ofid/ofab512.
Rubin et al., Bamlanivimab efficacy in older and high BMI outpatients with Covid-19 selected for treatment in a lottery-based allocation process, Open Forum Infectious Diseases, doi:10.1093/ofid/ofab546.
Treanor et al., Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016.
McLean et al., Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100.
Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.
Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.
Kumar et al., Combining baloxavir marboxil with standard-of-care neuraminidase inhibitor in patients hospitalised with severe influenza (FLAGSTONE): a randomised, parallel-group, double-blind, placebo-controlled, superiority trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2.
López-Medina et al., Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2021.3071.
Korves et al., SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk, medRxiv, doi:10.1101/2024.03.08.24303818.
Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.
Nonaka et al., SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003.
Karita et al., Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, medRxiv, doi:10.1101/2021.08.27.21262754.
Zavascki et al., Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, Research Square, doi:10.21203/rs.3.rs-910467/v1.
Willett et al., The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, medRxiv, doi:10.1101/2022.01.03.21268111.
Peacock et al., The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, bioRxiv, doi:10.1101/2021.12.31.474653.
Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.
Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.
Jeffreys et al., Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542.
Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.
Alsaidi et al., Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, Marine Drugs, doi:10.3390/md19080418.
Andreani et al., In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, Microbial Pathogenesis, doi:10.1016/j.micpath.2020.104228.
De Forni et al., Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, PLoS ONE, doi:10.1371/journal.pone.0276751.
Wan et al., Synergistic inhibition effects of andrographolide and baicalin on coronavirus mechanisms by downregulation of ACE2 protein level, Scientific Reports, doi:10.1038/s41598-024-54722-5.
Said et al., The effect of Nigella sativa and vitamin D3 supplementation on the clinical outcome in COVID-19 patients: A randomized controlled clinical trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1011522.
Fiaschi et al., In Vitro Combinatorial Activity of Direct Acting Antivirals and Monoclonal Antibodies against the Ancestral B.1 and BQ.1.1 SARS-CoV-2 Viral Variants, Viruses, doi:10.3390/v16020168.
Xing et al., Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals, Briefings in Bioinformatics, doi:10.1093/bib/bbab249.
Thairu et al., A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328.
Williams, T., Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, Do Your Own Research, doyourownresearch.substack.com/p/not-all-ivermectin-is-created-equal.
Xu et al., A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358.
Singh et al., The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkae045.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, www.wiley.com/en-ae/Publication+Bias+in+Meta+Analysis:+Prevention,+Assessment+and+Adjustments-p-9780470870143.
Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.
Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.
Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.
Moreno et al., Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2.
Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.
Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.
Harbord et al., A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, Statistics in Medicine, doi:10.1002/sim.2380.
Focosi (C), D., Monoclonal Antibody Therapies Against SARS-CoV-2: Promises and Realities, Current Topics in Microbiology and Immunology, doi:10.1007/82_2024_268.
ACTIV-3/TICO LY-CoV555 study group, A Neutralizing Monoclonal Antibody for Hospitalized Patients with Covid-19, NEJM, doi:10.1056/NEJMoa2033130.
Alam et al., Clinical Impact of the Early Use of Monoclonal Antibody LY-CoV555 (Bamlanivimab) on Mortality and Hospitalization Among Elderly Nursing Home Patients: A Multicenter Retrospective Study, Cureus, doi:10.7759/cureus.14933.
Bariola et al., Impact of monoclonal antibody treatment on hospitalization and mortality among non-hospitalized adults with SARS-CoV-2 infection, medRxiv, doi:10.1101/2021.03.25.21254322.
Chew et al., Antiviral and clinical activity of bamlanivimab in a randomized trial of non-hospitalized adults with COVID-19, Nature Communications, doi:10.1038/s41467-022-32551-2.
Corwin et al., The Efficacy of Bamlanivimab in Reducing Emergency Department Visits and Hospitalizations in a Real-world Setting, Open Forum Infectious Diseases, doi:10.1093/ofid/ofab305.
Dale et al., Clinical Outcomes of Monoclonal Antibody Therapy During a COVID-19 Outbreak in a Skilled Nursing Facility - Arizona, 2021, Journal of the American Geriatrics Society, doi:10.1111/jgs.17705.
Delasobera et al., Impact of Rapidly Deployed COVID-19 Monoclonal Antibody Infusion Clinics on Rate of Hospitalization, Infectious Diseases in Clinical Practice, doi:10.1097/IPC.0000000000001109.
Dougan et al., Bebtelovimab, alone or together with bamlanivimab and etesevimab, as a broadly neutralizing monoclonal antibody treatment for mild to moderate, ambulatory COVID-19, medRxiv, doi:10.1101/2022.03.10.22272100.
Dougan (B) et al., Bamlanivimab plus Etesevimab in Mild or Moderate Covid-19, New England Journal of Medicine, doi:10.1056/NEJMoa2102685.
Fivelstad et al., Bamlanivimab as monotherapy for high-risk COVID-19 patients with mild to moderate symptoms, International Journal of Scientific Research, 11:7, www.researchgate.net/publication/362828024_BAMLANIVIMAB_AS_MONOTHERAPY_FOR_HIGH-RISK_COVID-19_PATIENTS_WITH_MILD_TO_MODERATE_SYMPTOMS.
Ganesh et al., Intravenous bamlanivimab use associates with reduced hospitalization in high-risk patients with mild to moderate COVID-19, Journal of Clinical Investigation, doi:10.1172/JCI151697.
Gottlieb et al., Effect of Bamlanivimab as Monotherapy or in Combination With Etesevimab on Viral Load in Patients With Mild to Moderate COVID-19, JAMA, doi:10.1001/jama.2021.0202.
Karr et al., Bamlanivimab Use in a Military Treatment Facility, Military Medicine, doi:10.1093/milmed/usab188.
Kip et al., Evolving Real-World Effectiveness of Monoclonal Antibodies for Treatment of COVID-19, Annals of Internal Medicine, doi:10.7326/M22-1286.
Leavitt et al., Real World Utilization of Bamlanivimab at a Rural Community Hospital, Cureus, doi:10.7759/cureus.19747.
Lilly, Lilly's neutralizing antibody bamlanivimab (LY-CoV555) prevented COVID-19 at nursing homes in the BLAZE-2 trial, reducing risk by up to 80 percent for residents, Press Release, investor.lilly.com/news-releases/news-release-details/lillys-neutralizing-antibody-bamlanivimab-ly-cov555-prevented.
Priest et al., Bamlanivimab for the Prevention of Hospitalizations and Emergency Department Visits in SARS-CoV-2–Positive Patients in a Regional Health Care System, Infectious Diseases in Clinical Practice, doi:10.1097/IPC.0000000000001130.
Webb et al., Real-World Effectiveness and Tolerability of Monoclonal Antibody Therapy for Ambulatory Patients with Early COVID-19, Open Forum Infectious Diseases, doi:10.1093/ofid/ofab331.
Wilden et al., Real World Outcomes of Cancer Patients With SARS-CoV-2 Infection Receiving Monoclonal Antibodies, Journal of the National Comprehensive Cancer Network, doi:10.6004/jnccn.2021.7309.
Zhang et al., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes, JAMA, 80:19, 1690, doi:10.1001/jama.280.19.1690.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
Please send us corrections, updates, or comments.
c19early involves the extraction of 100,000+ datapoints from
thousands of papers. Community updates
help ensure high accuracy.
Treatments and other interventions are complementary.
All practical, effective, and safe
means should be used based on risk/benefit analysis.
No treatment or intervention is 100% available and effective for all current
and future variants.
We do not provide medical advice. Before taking any medication,
consult a qualified physician who can provide personalized advice and details
of risks and benefits based on your medical history and situation. FLCCC and WCH
provide treatment protocols.