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Bamlanivimab/etesevimab for COVID-19: real-time meta analysis of 14 studies
Covid Analysis, December 2022
https://c19early.org/lmeta.html
 
0 0.5 1 1.5+ All studies 55% 14 24,423 Improvement, Studies, Patients Relative Risk Mortality 56% 10 22,988 ICU admission 51% 2 12,628 Hospitalization 41% 9 21,880 Progression 47% 3 607 Recovery 11% 2 1,129 Cases 57% 1 965 Viral clearance 50% 2 1,101 RCTs 45% 5 2,784 RCT mortality 58% 2 1,349 Peer-reviewed 53% 12 22,990 Prophylaxis 57% 1 965 Early 69% 8 17,980 Late 29% 5 5,478 Bamlanivimab/etesevimab for COVID-19 c19early.org/l Dec 2022 Favorsbamlanivimab/e.. Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ICU admission, hospitalization, recovery, and cases. 11 studies from 9 independent teams (all from the same country) show statistically significant improvements in isolation (4 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 55% [30‑71%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 6 of 14 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 55% 14 24,423 Improvement, Studies, Patients Relative Risk Mortality 56% 10 22,988 ICU admission 51% 2 12,628 Hospitalization 41% 9 21,880 Progression 47% 3 607 Recovery 11% 2 1,129 Cases 57% 1 965 Viral clearance 50% 2 1,101 RCTs 45% 5 2,784 RCT mortality 58% 2 1,349 Peer-reviewed 53% 12 22,990 Prophylaxis 57% 1 965 Early 69% 8 17,980 Late 29% 5 5,478 Bamlanivimab/etesevimab for COVID-19 c19early.org/l Dec 2022 Favorsbamlanivimab/e.. Favorscontrol after exclusions
Efficacy is highly variant dependent. In Vitro studies suggest a lack of efficacy for omicron [Liu, Sheward, VanBlargan]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
No treatment, vaccine, or intervention is 100% effective and available. 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. Only 7% of bamlanivimab/etesevimab studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Highlights
Bamlanivimab/etesevimab reduces risk for COVID-19 with very high confidence for hospitalization and in pooled analysis, high confidence for mortality, low confidence for ICU admission, recovery, cases, and viral clearance, and very low confidence for progression. Efficacy is highly variant dependent. In Vitro research suggests a lack of efficacy for omicron.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 47 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 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 45% 0.55 [0.07-3.99] death 1/473 33/8,534 Rubin 44% 0.56 [0.07-4.33] death 1/191 10/1,066 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 Wilden 51% 0.49 [0.23-1.04] hosp. n/a n/a Tau​2 = 0.31, I​2 = 37.9%, p = 0.00058 Early treatment 69% 0.31 [0.16-0.60] 15/2,071 126/15,909 69% improvement ACTIV-3/T.. (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 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% improvement 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] 0/483 0/482 57% improvement All studies 55% 0.45 [0.29-0.70] 42/5,278 165/19,145 55% improvement 14 bamlanivimab/etesevimab COVID-19 studies c19early.org/l Dec 2022 Tau​2 = 0.28, I​2 = 47.9%, p = 0.00041 Effect extraction pre-specified(most serious outcome, see appendix) 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 Relative Risk [CI] Webb 80% death Dougan (DB RCT) 95% death Cooper 45% death Rubin 44% death Delasobera -119% death Dale 89% death Wilden 51% hospitalization Tau​2 = 0.31, I​2 = 37.9%, p = 0.00058 Early treatment 69% 69% improvement ACTIV-3/.. (RCT) -100% death Bariola 67% death Ganesh 74% death Priest (PSM) 0% death Chew (RCT) 25% hospitalization Tau​2 = 0.29, I​2 = 45.8%, p = 0.35 Late treatment 29% 29% improvement Lilly (RCT) 57% symp. case Tau​2 = 0.00, I​2 = 0.0%, p = 0.00021 Prophylaxis 57% 57% improvement All studies 55% 55% improvement 14 bamlanivimab/etesevimab COVID-19 studies c19early.org/l Dec 2022 Tau​2 = 0.28, I​2 = 47.9%, p = 0.00041 Effect extraction pre-specifiedRotate device for details 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, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. 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. C. Results within the context of multiple COVID-19 treatments. D. Timeline of results in bamlanivimab/etesevimab studies.
We analyze all significant studies concerning the use of bamlanivimab/etesevimab for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Efficacy is variant dependent, for example in vitro studies suggest that bamlanivimab/etesevimab is not effective for omicron [Liu, Sheward, VanBlargan, Zhou].
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 1. Predicted efficacy by variant from [Davis].    : likely effective    : likely ineffective    : unknown. Submit updates.
Table 2 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 3 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, and 11 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 studies55% [30‑71%]14 24,423 200
After exclusions56% [28‑73%]12 14,159 184
Peer-reviewed studiesPeer-reviewed53% [16‑74%]12 22,990 177
Randomized Controlled TrialsRCTs45% [-18‑74%]5 2,784 88
Mortality56% [5‑80%]10 22,988 137
ICU admissionICU51% [5‑75%]2 12,628 29
HospitalizationHosp.41% [21‑56%]9 21,880 124
Viral50% [-10‑77%]2 1,101 59
RCT mortality58% [-1321‑99%]2 1,349 34
Table 2. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Early treatment Late treatment Prophylaxis
All studies69% [40‑84%] 829% [-44‑65%] 557% [33‑72%] 1
After exclusions72% [37‑88%] 629% [-44‑65%] 557% [33‑72%] 1
Peer-reviewed studiesPeer-reviewed69% [40‑84%] 812% [-85‑58%] 4-
Randomized Controlled TrialsRCTs79% [19‑95%] 2-21% [-218‑54%] 257% [33‑72%] 1
Mortality72% [24‑90%] 631% [-76‑73%] 4-
ICU admissionICU58% [-72‑90%] 149% [-9‑76%] 1-
HospitalizationHosp.47% [20‑65%] 532% [-7‑57%] 4-
Viral67% [55‑75%] 126% [10‑38%] 1-
RCT mortality95% [10‑100%] 1-100% [-483‑31%] 1-
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.
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ICU admission.
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Figure 6. Random effects meta-analysis for hospitalization.
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Figure 7. Random effects meta-analysis for progression.
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Figure 8. Random effects meta-analysis for recovery.
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Figure 9. Random effects meta-analysis for cases.
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Figure 10. Random effects meta-analysis for viral clearance.
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Figure 11. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 12 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 57% improvement, compared to 51% for other studies. Figure 13 and 14 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results.
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Figure 12. Results for RCTs and non-RCT studies.
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Figure 13. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 14. Random effects meta-analysis for RCT mortality results.
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 15 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 15. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction 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 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] 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] report only 2.5 hours improvement for inpatient treatment.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Table 4. Early treatment is more effective for baloxavir and influenza.
Figure 16 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 16. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 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 (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 17. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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Figure 17. 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.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For bamlanivimab/etesevimab, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
78% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 80% of prospective studies, showing similar results. The median effect size for retrospective studies is 51% improvement, compared to 57% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 18 shows a scatter plot of results for prospective and retrospective studies.
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Figure 18. Prospective vs. retrospective studies.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 19 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 19. Example funnel plot analysis for simulated perfect trials.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
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 by 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.
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 affiliated with special interests 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 alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. 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, vaccine, 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.
Studies to date show that bamlanivimab/etesevimab is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ICU admission, hospitalization, recovery, and cases. 11 studies from 9 independent teams (all from the same country) show statistically significant improvements in isolation (4 for the most serious outcome). Meta analysis using the most serious outcome reported shows 55% [30‑71%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment. Results are robust — in exclusion sensitivity analysis 6 of 14 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
Efficacy is highly variant dependent. In Vitro studies suggest a lack of efficacy for omicron [Liu, Sheward, VanBlargan]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
0 0.5 1 1.5 2+ Mortality -100% Improvement Relative Risk c19early.org/l ACTIV-3/TICO et al. NCT04501978 ACTIV-3 Bamlaniv../e.. RCT LATE Favors bamlanivimab/e.. Favors control
[ACTIV-3/TICO] Late stage RCT of LY-CoV555 added to remdesivir, showing non-statistically significant higher mortality with the addition of LY-CoV555.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk Death/hospitalization 64% primary Hospitalization 61% c19early.org/l Bariola et al. Bamlaniv../e.. for COVID-19 LATE Favors bamlanivimab/e.. Favors control
[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.
0 0.5 1 1.5 2+ Hospitalization 25% Improvement Relative Risk Hospitalization, 7000mg.. 52% Hospitalization, 700mg.. -1% Time to symptom impro.. -14% primary Time to symptom i.. (b) -17% primary Progression, 7000mg -1% Progression, 700mg 2% Viral load, 7000mg, day 3 26% Viral load, 700mg, day 3 35% c19early.org/l Chew et al. NCT04427501 ACTIV-2/A5401 Bamlaniv../e.. RCT LATE Favors bamlanivimab/e.. Favors control
[Chew] RCT 317 outpatients in the USA showing faster viral load and inflammatory biomarker decline, but no significant differences in clinical outcomes.
0 0.5 1 1.5 2+ Mortality 45% unadjusted Improvement Relative Risk ICU admission 58% unadjusted Hospitalization 5% primary, unadjusted Mortality (b) -17% unadjusted ICU admission (b) 9% unadjusted Hospitalization (b) 28% unadjusted c19early.org/l Cooper et al. Bamlaniv../e.. for COVID-19 EARLY Favors bamlanivimab/e.. Favors control
[Cooper] Retrospective 2,879 patients and matched controls in the USA, showing significantly lower mortality and hospitalization with bamlanivimab, bamlanivimab/etesevimab, and casirivimab/imdevimab. There was significantly lower hospitalization with casirivimab/imdevimab compared to bamlanivimab or bamlanivimab/etesevimab. PSM and multivariate analysis is only provided for all treatments combined.
0 0.5 1 1.5 2+ Mortality 89% Improvement Relative Risk Progression 86% Progression (b) 54% c19early.org/l Dale et al. Bamlaniv../e.. for COVID-19 EARLY Favors bamlanivimab/e.. Favors control
[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.
0 0.5 1 1.5 2+ Mortality -119% Improvement Relative Risk Hospitalization 52% Progression 20% c19early.org/l Delasobera et al. Bamlaniv../e.. for COVID-19 EARLY Favors bamlanivimab/e.. Favors control
[Delasobera] Retrospective 438 patients in the USA, 253 treated with bamlanivimab, showing significantly lower hospitalization with treatment.
0 0.5 1 1.5 2+ Mortality 95% Improvement Relative Risk Death/hospitalization 70% primary Recovery time 11% Viral clearance 67% c19early.org/l Dougan et al. NCT04427501 Bamlaniv../e.. RCT EARLY Favors bamlanivimab/e.. Favors control
[Dougan] Results from the BLAZE-1 RCT of combined bamlanivimab/etesevimab, showing significantly lower mortality and combined mortality/hospitalization with treatment. NCT04427501.
0 0.5 1 1.5 2+ Mortality 74% Improvement Relative Risk ICU admission 49% Hospitalization 37% primary c19early.org/l Ganesh et al. Bamlaniv../e.. for COVID-19 LATE Favors bamlanivimab/e.. Favors control
[Ganesh] Retrospective 2,335 bamlanivimab patients and 2,335 PSM controls in the USA, showing significantly lower hospitalization with treatment.
0 0.5 1 1.5 2+ Hospitalization/ER 71% Improvement Relative Risk Hospitalization/ER (b) 80% Hospitalization/ER (c) 75% Hospitalization/ER (d) 56% Hospitalization/ER (e) 92% c19early.org/l Gottlieb et al. Bamlaniv../e.. for COVID-19 RCT EARLY Favors bamlanivimab/e.. Favors control
[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.
0 0.5 1 1.5 2+ Symptomatic case 57% Improvement Relative Risk Symptomatic case (b) 80% c19early.org/l Lilly et al. Bamlaniv../e.. for COVID-19 RCT Prophylaxis Favors bamlanivimab/e.. Favors control
[Lilly] Press release on the BLAZE-2 trial at nursing homes showing significantly lower symptomatic COVID-19 with treatment.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk Hospitalization -4% Hospitalization/ER -5% c19early.org/l Priest et al. Bamlaniv../e.. for COVID-19 LATE Favors bamlanivimab/e.. Favors control
[Priest] Retrospective 379 bamlanivimab patients and 379 matched controls in the USA, showing no significant differences with treatment.
0 0.5 1 1.5 2+ Mortality 44% Improvement Relative Risk Hospitalization 65% c19early.org/l Rubin et al. Bamlaniv../e.. for COVID-19 EARLY Favors bamlanivimab/e.. Favors control
[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.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk Hospitalization 53% Hospitalization/ER 27% primary c19early.org/l Webb et al. Bamlaniv../e.. for COVID-19 EARLY Favors bamlanivimab/e.. Favors control
[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".
0 0.5 1 1.5 2+ Hospitalization 51% Improvement Relative Risk c19early.org/l Wilden et al. Bamlaniv../e.. for COVID-19 EARLY Favors bamlanivimab/e.. Favors control
[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 performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were bamlanivimab, etesevimab, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of 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 are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at 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.
[Cooper], 10/8/2021, retrospective, USA, peer-reviewed, 9 authors, excluded in exclusion analyses: unadjusted results with no group details. 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 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 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 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, 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, 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.
[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], 10/7/2021, Double Blind Randomized Controlled Trial, USA, peer-reviewed, 33 authors, 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.
[Gottlieb], 1/21/2021, Randomized Controlled Trial, USA, peer-reviewed, 27 authors, 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.
[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], 12/22/2020, Randomized Controlled Trial, USA, peer-reviewed, 1 author, 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. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, 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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