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Bamlanivimab/etesevimab for COVID-19: real-time meta analysis of 21 studies

@CovidAnalysis, July 2024, Version 38V38
 
0 0.5 1 1.5+ All studies 47% 21 35,320 Improvement, Studies, Patients Relative Risk Mortality 54% 13 32,261 ICU admission 28% 2 15,055 Hospitalization 42% 15 31,733 Progression 47% 3 607 Recovery 11% 2 1,129 Cases 57% 1 965 Viral clearance 38% 3 1,354 RCTs 39% 6 3,039 RCT mortality 58% 2 1,349 Peer-reviewed 46% 18 33,632 Prophylaxis 57% 1 965 Early 52% 15 28,877 Late 29% 5 5,478 Bamlanivimab/etesevimab for COVID-19 c19early.org July 2024 after exclusions Favorsbamlanivimab/e.. Favorscontrol
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.
0 0.5 1 1.5+ All studies 47% 21 35,320 Improvement, Studies, Patients Relative Risk Mortality 54% 13 32,261 ICU admission 28% 2 15,055 Hospitalization 42% 15 31,733 Progression 47% 3 607 Recovery 11% 2 1,129 Cases 57% 1 965 Viral clearance 38% 3 1,354 RCTs 39% 6 3,039 RCT mortality 58% 2 1,349 Peer-reviewed 46% 18 33,632 Prophylaxis 57% 1 965 Early 52% 15 28,877 Late 29% 5 5,478 Bamlanivimab/etesevimab for COVID-19 c19early.org July 2024 after exclusions Favorsbamlanivimab/e.. Favorscontrol
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 escape7-9.
Prescription treatments have been preferentially used by patients at lower risk10. 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.
Evolution of COVID-19 clinical evidence Bamlanivimab/etesevimab p=0.00036 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org July 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
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.
21st 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 79 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gottlieb (RCT) 71% 0.29 [0.09-0.96] hosp./ER 4/101 7/52 Improvement, RR [CI] Treatment Control Alam 75% 0.25 [0.10-0.85] death 160 (n) 86 (n) Karr 40% 0.60 [0.08-4.51] hosp. 4/40 1/6 Corwin 80% 0.20 [0.03-1.42] death 1/780 35/5,337 Webb 80% 0.20 [0.03-1.46] death 1/479 57/5,536 Dougan (DB RCT) 95% 0.05 [0.00-0.90] death 0/518 9/517 Cooper -7% 1.07 [0.55-2.07] death 12/2,900 33/8,534 Rubin 44% 0.56 [0.07-4.33] death 1/191 10/1,066 Leavitt 30% 0.70 [0.26-1.92] hosp. 6/136 9/143 Delasobera -119% 2.19 [0.23-20.9] death 3/253 1/185 Dale 89% 0.11 [0.02-0.55] death 5/56 9/19 BLAZE-4 Dougan (RCT) -51% 1.51 [0.26-8.90] hosp. 3/127 2/128 CT​1 Wilden 51% 0.49 [0.23-1.04] hosp. n/a n/a Fivelstad -144% 2.44 [0.10-59.6] death 1/335 0/148 Kip 15% 0.85 [0.51-1.41] death/hosp. 20/349 47/695 Tau​2 = 0.35, I​2 = 51.5%, p = 0.0023 Early treatment 52% 0.48 [0.30-0.77] 61/6,425 220/22,452 52% lower risk ACTIV-3 ACTIV-3/TIC.. (RCT) -100% 2.00 [0.69-5.83] death 9/163 5/151 Improvement, RR [CI] Treatment Control Bariola 67% 0.33 [0.10-1.01] death 4/234 12/234 Ganesh 74% 0.26 [0.05-1.20] death 2/1,789 8/1,832 Priest (PSM) 0% 1.00 [0.33-3.07] death 6/379 6/379 ACTIV-2/A5401 Chew (RCT) 25% 0.75 [0.26-2.10] hosp. 6/159 8/158 Tau​2 = 0.29, I​2 = 45.8%, p = 0.35 Late treatment 29% 0.71 [0.35-1.44] 27/2,724 39/2,754 29% lower risk Lilly (RCT) 57% 0.43 [0.28-0.67] symp. case 483 (n) 482 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.00021 Prophylaxis 57% 0.43 [0.28-0.67] 483 (n) 482 (n) 57% lower risk All studies 47% 0.53 [0.38-0.75] 88/9,632 259/25,688 47% lower risk 21 bamlanivimab/etesevimab COVID-19 studies c19early.org July 2024 Tau​2 = 0.25, I​2 = 48.5%, p = 0.00036 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors bamlanivimab/e.. Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gottlieb (RCT) 71% hosp./ER Improvement Relative Risk [CI] Alam 75% death Karr 40% hospitalization Corwin 80% death Webb 80% death Dougan (DB RCT) 95% death Cooper -7% death Rubin 44% death Leavitt 30% hospitalization Delasobera -119% death Dale 89% death BLAZE-4 Dougan (RCT) -51% hospitalization CT​1 Wilden 51% hospitalization Fivelstad -144% death Kip 15% death/hosp. Tau​2 = 0.35, I​2 = 51.5%, p = 0.0023 Early treatment 52% 52% lower risk ACTIV-3 ACTIV-3/TI.. (RCT) -100% death Bariola 67% death Ganesh 74% death Priest (PSM) 0% death ACTIV-2/A5401 Chew (RCT) 25% hospitalization Tau​2 = 0.29, I​2 = 45.8%, p = 0.35 Late treatment 29% 29% lower risk Lilly (RCT) 57% symp. case Tau​2 = 0.00, I​2 = 0.0%, p = 0.00021 Prophylaxis 57% 57% lower risk All studies 47% 47% lower risk 21 bamlanivimab/etesevimab C19 studies c19early.org July 2024 Tau​2 = 0.25, I​2 = 48.5%, p = 0.00036 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors bamlanivimab/e.. Favors control
B
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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.
Introduction
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 injury11-18 and cognitive deficits13,18, cardiovascular complications19, 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,20-24, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk25, 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.
Variant Dependence
Extensive mutations in SARS-CoV-2 have resulted in variants that evade neutralizing antibodies from monoclonal antibody treatments26, 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.
Table 1. Predicted efficacy by variant from Davis et al. (not updated for more recent variants).    : likely effective    : likely ineffective    : unknown. Submit updates.
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
Results
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.
Table 2. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies47% [25‑62%]
***
21 35,320 275
After exclusions50% [28‑65%]
***
19 22,629 259
Peer-reviewed studiesPeer-reviewed46% [19‑64%]
**
18 33,632 230
Randomized Controlled TrialsRCTs39% [-24‑70%]6 3,039 110
Mortality54% [13‑76%]
*
13 32,261 160
ICU admissionICU28% [-11‑53%]2 15,055 29
HospitalizationHosp.42% [30‑53%]
****
15 31,733 183
Recovery11% [3‑18%]
**
2 1,129 59
Viral38% [2‑60%]
*
3 1,354 81
RCT mortality58% [-1321‑99%]2 1,349 34
Table 3. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies52% [23‑70%]
**
29% [-44‑65%]57% [33‑72%]
***
After exclusions57% [28‑75%]
**
29% [-44‑65%]57% [33‑72%]
***
Peer-reviewed studiesPeer-reviewed54% [26‑72%]
**
12% [-85‑58%]
Randomized Controlled TrialsRCTs64% [-72‑92%]-21% [-218‑54%]57% [33‑72%]
***
Mortality64% [12‑85%]
*
31% [-76‑73%]
ICU admissionICU17% [-30‑47%]49% [-9‑76%]
HospitalizationHosp.47% [33‑57%]
****
32% [-7‑57%]
Recovery11% [3‑18%]
**
-14% [-45397‑100%]
Viral44% [-48‑79%]26% [10‑38%]
**
RCT mortality95% [10‑100%]
*
-100% [-483‑31%]
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Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 4. Random effects meta-analysis for all studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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Figure 12. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below. Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Davidson et al. also showed no important difference between meta analysis results of preprints and peer-reviewed publications for COVID-19, based on 37 meta analyses including 114 trials.
Randomized Controlled Trials (RCTs)
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.
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Figure 13. Results for RCTs and non-RCT studies.
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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.
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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 biases30, and analysis of double-blind RCTs has identified extreme levels of bias31. 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 79 treatments we have analyzed, 63% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.
Evidence shows that non-RCT studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see36,37.
Currently, 47 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 30 have been confirmed in RCTs, with a mean delay of 7.0 months. When considering only low cost treatments, 25 have been confirmed with a delay of 8.4 months. For the 17 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 14 are all consistent with the overall results (benefit or harm), with 11 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
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.
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 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.
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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
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 hours40,41. Baloxavir 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.
Table 4. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases42
<24 hours-33 hours symptoms43
24-48 hours-13 hours symptoms43
Inpatients-2.5 hours to improvement44
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 79 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 79 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 variants46, for example the Gamma variant shows significantly different characteristics47-50. 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 variants51,52.
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 synergistic53-63, 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.
Pooled Effects
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 79 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 = 0.0000011 to p = 0.0000000036.
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Figure 18. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 19. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 18. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 47 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.2 months. When restricting to RCTs only, 54% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months. Figure 21 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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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.
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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 < 0.0567-74. 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 alone53-63. 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.
Focosi et al. present a review covering bamlanivimab/etesevimab for COVID-19.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors20-24, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk25, 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.
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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 7,000+ proposed treatments show efficacy75.
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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 escape7-9.
Prescription treatments have been preferentially used by patients at lower risk10. 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.
Mortality -100% Improvement Relative Risk Bamlanivimab/e..  ACTIV-3  LATE TREATMENT  RCT Is late treatment with bamlanivimab/etesevimab beneficial for COVID-19? RCT 314 patients in the USA (August - October 2020) Higher mortality with bamlanivimab/etesevimab (not stat. sig., p=0.22) c19early.org ACTIV-3/TICO LY-CoV555 study group, NEJM, Dec 2020 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality 75% Improvement Relative Risk Hospitalization 65% Bamlanivimab/e..  Alam et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 246 patients in the USA (November 2020 - January 2021) Lower mortality with bamlanivimab/etesevimab (p=0.026) c19early.org Alam et al., Cureus, May 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
Alam: Retrospective 246 nursing home patients showing lower mortality with early bamlanivimab treatment.
Mortality 67% Improvement Relative Risk Death/hospitalization 64% primary Hospitalization 61% Bamlanivimab/e..  Bariola et al.  LATE TREATMENT Is late treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 468 patients in the USA Lower death/hosp. (p=0.00029) and hospitalization (p=0.001) c19early.org Bariola et al., medRxiv, March 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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 hygiene79,80, vitamin D81, 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.
Hospitalization 25% Improvement Relative Risk Hospitalization, 7000mg.. 52% Hospitalization, 700mg, d.. -1% Time to symptom improv.. -14% primary Time to symptom im.. (b) -17% primary Progression, 7000mg -1% Progression, 700mg 2% Viral load, 7000mg, day 3 26% Viral load, 700mg, day 3 35% Bamlanivimab/e..  ACTIV-2/A5401  LATE TREATMENT  RCT Is late treatment with bamlanivimab/etesevimab beneficial for COVID-19? RCT 317 patients in the USA (August - November 2020) Improved viral clearance with bamlanivimab/etesevimab (p=0.002) c19early.org Chew et al., Nature Communications, Aug 2022 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
Chew: RCT 317 outpatients in the USA showing faster viral load and inflammatory biomarker decline, but no significant differences in clinical outcomes.
Mortality -7% unadjusted Improvement Relative Risk Mortality (b) 45% unadjusted Mortality (c) -17% unadjusted ICU admission 17% unadjusted ICU admission (b) 58% unadjusted ICU admission (c) 9% unadjusted Hospitalization 24% primary, unadjusted Hospitalization (b) 5% primary, unadjusted Hospitalization (c) 28% unadjusted Bamlanivimab/e..  Cooper et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 11,434 patients in the USA Lower hospitalization with bamlanivimab/etesevimab (p=0.00046) c19early.org Cooper et al., Open Forum Infectious D.., Oct 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality 80% Improvement Relative Risk Hospitalization 39% Bamlanivimab/e..  Corwin et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 6,117 patients in the USA (November 2020 - January 2021) Lower hospitalization with bamlanivimab/etesevimab (p=0.00044) c19early.org Corwin et al., Open Forum Infectious D.., Jun 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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 hygiene79,80, vitamin D81, 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.
Mortality 89% Improvement Relative Risk Progression 86% Progression (b) 54% Bamlanivimab/e..  Dale et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 75 patients in the USA Lower mortality (p=0.0097) and progression (p=0.0022) c19early.org Dale et al., J. the American Geriatric.., Feb 2022 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality -119% Improvement Relative Risk Hospitalization 52% Progression 20% Bamlanivimab/e..  Delasobera et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 438 patients in the USA Lower hospitalization with bamlanivimab/etesevimab (p=0.014) c19early.org Delasobera et al., Infectious Diseases.., Jan 2022 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
Delasobera: Retrospective 438 patients in the USA, 253 treated with bamlanivimab, showing significantly lower hospitalization with treatment.
Hospitalization -51% Improvement Relative Risk Recovery time 12% no CI Viral load reduction, day 7 9% Viral load reduction, day 5 24% Viral load reduction, day 3 12% Viral clearance 35% primary Bamlanivimab/e..  BLAZE-4  EARLY TREATMENT  RCT Is early treatment with bamlan../e.. + bebtelovimab beneficial for COVID-19? RCT 714 patients in the USA (April - July 2021) Improved viral clearance with bamlan../e.. + bebtelovimab (p<0.000001) c19early.org Dougan et al., medRxiv, March 2022 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
Dougan: RCT showing improved viral clearance with bamlanivimab/etesevimab combined with bebtelovimab. Results refer to the placebo controlled portion of the trial.
Mortality 95% Improvement Relative Risk Death/hospitalization 70% primary Recovery time 11% Viral clearance 67% Bamlanivimab/e..  Dougan et al.  EARLY TREATMENT  DB RCT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Double-blind RCT 1,035 patients in the USA (September - December 2020) Lower mortality (p=0.0019) and death/hosp. (p=0.00016) c19early.org Dougan et al., New England J. Medicine, Oct 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
Dougan (B): Results from the BLAZE-1 RCT of combined bamlanivimab/etesevimab, showing significantly lower mortality and combined mortality/hospitalization with treatment. NCT04427501.
Mortality -144% Improvement Relative Risk Hospitalization 63% Bamlanivimab/e..  Fivelstad et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 483 patients in the USA Lower hospitalization with bamlanivimab/etesevimab (p=0.00059) c19early.org Fivelstad et al., Int. J. Scientific R.., Jul 2022 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality 74% Improvement Relative Risk ICU admission 49% Hospitalization 37% primary Bamlanivimab/e..  Ganesh et al.  LATE TREATMENT Is late treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 3,621 patients in the USA Lower hospitalization with bamlanivimab/etesevimab (p=0.014) c19early.org Ganesh et al., J. Clinical Investigation, Oct 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
Ganesh: Retrospective 2,335 bamlanivimab patients and 2,335 PSM controls in the USA, showing significantly lower hospitalization with treatment.
Hospitalization/ER 71% Improvement Relative Risk Hospitalization/ER (b) 80% Hospitalization/ER (c) 75% Hospitalization/ER (d) 56% Hospitalization/ER (e) 92% Bamlanivimab/e..  Gottlieb et al.  EARLY TREATMENT  RCT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? RCT 153 patients in the USA (June - October 2020) Fewer hosp./ER visits with bamlanivimab/etesevimab (p=0.046) c19early.org Gottlieb et al., JAMA, January 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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.
Hospitalization 40% Improvement Relative Risk Hospitalization/ER 62% Bamlanivimab/e..  Karr et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 46 patients in the USA (December 2020 - January 2021) Lower hospitalization (p=0.52) and fewer hosp./ER visits (p=0.22), not sig. c19early.org Karr et al., Military Medicine, May 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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."
Death/hospitalization 15% Improvement Relative Risk Death/hospitalization (b) 31% Bamlanivimab/e..  Kip et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 2,571 patients in the USA (December 2020 - August 2022) Lower death/hosp. with bamlanivimab/etesevimab (not stat. sig., p=0.54) c19early.org Kip et al., Annals of Internal Medicine, Apr 2023 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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 hygiene79,80, vitamin D81, 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.
Hospitalization 30% Improvement Relative Risk Emergency care 42% Bamlanivimab/e..  Leavitt et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 279 patients in the USA (December 2020 - January 2021) Lower hospitalization with bamlanivimab/etesevimab (not stat. sig., p=0.6) c19early.org Leavitt et al., Cureus, November 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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.
Symp. case 57% Improvement Relative Risk Symp. case (b) 80% Bamlanivimab/e..  Lilly et al.  Prophylaxis  RCT Is prophylaxis with bamlanivimab/etesevimab beneficial for COVID-19? RCT 965 patients in the USA Fewer symptomatic cases with bamlanivimab/etesevimab (p=0.00021) c19early.org Lilly, Press Release, January 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
Lilly: Press release on the BLAZE-2 trial at nursing homes showing significantly lower symptomatic COVID-19 with treatment.
Mortality 0% Improvement Relative Risk Hospitalization -4% Hospitalization/ER -5% Bamlanivimab/e..  Priest et al.  LATE TREATMENT Is late treatment with bamlanivimab/etesevimab beneficial for COVID-19? PSM retrospective 758 patients in the USA (October 2020 - March 2021) No significant difference in outcomes seen c19early.org Priest et al., Infectious Diseases in .., Jan 2022 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
Priest: Retrospective 379 bamlanivimab patients and 379 matched controls in the USA, showing no significant differences with treatment.
Mortality 44% Improvement Relative Risk Hospitalization 65% Bamlanivimab/e..  Rubin et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 1,257 patients in the USA (December 2020 - February 2021) Lower hospitalization with bamlanivimab/etesevimab (p=0.041) c19early.org Rubin et al., Open Forum Infectious Di.., Nov 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality 80% Improvement Relative Risk Hospitalization 53% Hospitalization/ER 27% primary Bamlanivimab/e..  Webb et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective 6,015 patients in the USA Lower hospitalization (p<0.0001) and fewer hosp./ER visits (p<0.0001) c19early.org Webb et al., Open Forum Infectious Dis.., Jun 2021 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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".
Hospitalization 51% Improvement Relative Risk Bamlanivimab/e..  Wilden et al.  EARLY TREATMENT Is early treatment with bamlanivimab/etesevimab beneficial for COVID-19? Retrospective study in the USA (December 2020 - July 2021) Lower hospitalization with bamlanivimab/etesevimab (not stat. sig., p=0.06) c19early.org Wilden et al., J. the National Compreh.., Mar 2022 Favorsbamlanivimab/e.. Favorscontrol 0 0.5 1 1.5 2+
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 to98. 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 1101. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.12.4) with scipy (1.14.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.22.0).
Forest plots are computed using PythonMeta102 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 effective40,41.
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 the bottom of this page.
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.
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.
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