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Bamlanivimab for COVID-19: real-time meta analysis of 11 studies
Covid Analysis, January 18, 2022, DRAFT
https://c19ly.com/meta.html
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 62% 11 23,338 Improvement, Studies, Patients Relative Risk With exclusions 57% 9 13,074 Mortality 71% 7 21,451 ICU admission 51% 2 12,628 Hospitalization 48% 7 21,136 Cases 57% 1 965 Viral clearance 26% 1 94 RCTs 50% 6 2,970 RCT mortality 47% 2 1,083 Peer-reviewed 68% 7 20,819 Prophylaxis 57% 1 965 Early 79% 6 17,653 Late 36% 4 4,720 Bamlanivimab for COVID-19 c19ly.com Jan 18, 2022 Favors bamlanivimab Favors control
Statistically significant improvements are seen for mortality, ICU admission, hospitalization, cases, and viral clearance. 10 studies from 7 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 62% [37‑77%] 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 11 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 62% 11 23,338 Improvement, Studies, Patients Relative Risk With exclusions 57% 9 13,074 Mortality 71% 7 21,451 ICU admission 51% 2 12,628 Hospitalization 48% 7 21,136 Cases 57% 1 965 Viral clearance 26% 1 94 RCTs 50% 6 2,970 RCT mortality 47% 2 1,083 Peer-reviewed 68% 7 20,819 Prophylaxis 57% 1 965 Early 79% 6 17,653 Late 36% 4 4,720 Bamlanivimab for COVID-19 c19ly.com Jan 18, 2022 Favors bamlanivimab Favors control
Efficacy is highly variant dependent. In Vitro studies suggest a lack of efficacy for omicron [Liu, Sheward, VanBlargan]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 9% of bamlanivimab studies show zero events in the treatment arm.
Multiple treatments are typically used in combination, and other treatments may be more effective.
Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used, including treatments, as supported by Pfizer [Pfizer]. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and sources are in the appendix.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 1179% [59‑89%]36% [-58‑74%]57% [33‑72%] 23,338 137
With exclusions 976% [50‑88%]36% [-58‑74%]57% [33‑72%] 13,074 121
Peer-reviewed 778% [54‑89%]23% [-477‑90%] 20,819 90
Randomized Controlled TrialsRCTs 675% [46‑88%]-21% [-218‑54%]57% [33‑72%] 2,970 65
Percentage improvement with bamlanivimab treatment
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chen (RCT) 74% 0.26 [0.09-0.75] hosp. 5/309 9/143 Improvement, RR [CI] Treatment Control Gottlieb (RCT) 71% 0.29 [0.09-0.96] hosp./ER 4/101 7/52 Lilly (DB RCT) 92% 0.08 [0.00-1.43] death 0/511 4/258 Webb 80% 0.20 [0.03-1.46] death 1/479 57/5,536 Cooper 45% 0.55 [0.07-3.99] death 1/473 33/8,534 Rubin 97% 0.03 [0.00-0.25] death 1/191 10/1,066 Tau​2 = 0.03, I​2 = 4.1%, p < 0.0001 Early treatment 79% 0.21 [0.11-0.41] 12/2,064 120/15,589 79% 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 Chew (RCT) 25% 0.75 [0.26-2.10] hosp. 6/159 8/158 Tau​2 = 0.48, I​2 = 57.4%, p = 0.34 Late treatment 36% 0.64 [0.26-1.58] 21/2,345 33/2,375 36% 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 62% 0.38 [0.23-0.63] 33/4,892 153/18,446 62% improvement 11 bamlanivimab COVID-19 studies c19ly.com Jan 18, 2022 Tau​2 = 0.31, I​2 = 48.1%, p = 0.00024 Effect extraction pre-specified, see appendix Favors bamlanivimab Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of bamlanivimab for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Variant Dependence
Efficacy is variant dependent, for example in vitro studies suggest that bamlanivimab is not effective for the omicron variant [Liu, Sheward, VanBlargan].
Results
Figure 3, 4, 5, 6, 7, 8, 9, and 10 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ICU admission, hospitalization, recovery, cases, viral clearance, and peer reviewed studies. Table 1 summarizes the results by treatment stage.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 6 6 100% 79% improvement
RR 0.21 [0.11‑0.41]
p < 0.0001
Late treatment 3 4 75.0% 36% improvement
RR 0.64 [0.26‑1.58]
p = 0.34
Prophylaxis 1 1 100% 57% improvement
RR 0.43 [0.28‑0.67]
p = 0.00021
All studies 10 11 90.9% 62% improvement
RR 0.38 [0.23‑0.63]
p = 0.00024
Table 1. Results by treatment stage.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chen (RCT) 74% 0.26 [0.09-0.75] hosp. 5/309 9/143 Improvement, RR [CI] Treatment Control Gottlieb (RCT) 71% 0.29 [0.09-0.96] hosp./ER 4/101 7/52 Lilly (DB RCT) 92% 0.08 [0.00-1.43] death 0/511 4/258 Webb 80% 0.20 [0.03-1.46] death 1/479 57/5,536 Cooper 45% 0.55 [0.07-3.99] death 1/473 33/8,534 Rubin 97% 0.03 [0.00-0.25] death 1/191 10/1,066 Tau​2 = 0.03, I​2 = 4.1%, p < 0.0001 Early treatment 79% 0.21 [0.11-0.41] 12/2,064 120/15,589 79% 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 Chew (RCT) 25% 0.75 [0.26-2.10] hosp. 6/159 8/158 Tau​2 = 0.48, I​2 = 57.4%, p = 0.34 Late treatment 36% 0.64 [0.26-1.58] 21/2,345 33/2,375 36% 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 62% 0.38 [0.23-0.63] 33/4,892 153/18,446 62% improvement 11 bamlanivimab COVID-19 studies c19ly.com Jan 18, 2022 Tau​2 = 0.31, I​2 = 48.1%, p = 0.00024 Effect extraction pre-specified, see appendix Favors bamlanivimab Favors control
Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lilly (DB RCT) 92% 0.08 [0.00-1.43] 0/511 4/258 Improvement, RR [CI] Treatment Control Webb 80% 0.20 [0.03-1.46] 1/479 57/5,536 Cooper 45% 0.55 [0.07-3.99] 1/473 33/8,534 Rubin 97% 0.03 [0.00-0.25] 1/191 10/1,066 Tau​2 = 0.51, I​2 = 29.3%, p = 0.0027 Early treatment 86% 0.14 [0.04-0.50] 3/1,654 104/15,394 86% improvement ACTIV-3/T.. (RCT) -100% 2.00 [0.69-5.83] 9/163 5/151 Improvement, RR [CI] Treatment Control Bariola 67% 0.33 [0.10-1.01] 4/234 12/234 Ganesh 74% 0.26 [0.05-1.20] 2/1,789 8/1,832 Tau​2 = 0.98, I​2 = 71.5%, p = 0.44 Late treatment 41% 0.59 [0.16-2.22] 15/2,186 25/2,217 41% improvement All studies 71% 0.29 [0.10-0.84] 18/3,840 129/17,611 71% improvement 7 bamlanivimab COVID-19 mortality results c19ly.com Jan 18, 2022 Tau​2 = 1.21, I​2 = 64.6%, p = 0.022 Favors bamlanivimab Favors control
Figure 4. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Cooper 58% 0.42 [0.10-1.72] 2/473 85/8,534 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.23 Early treatment 58% 0.42 [0.10-1.72] 2/473 85/8,534 58% improvement Ganesh 49% 0.51 [0.24-1.09] 10/1,789 20/1,832 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.082 Late treatment 49% 0.51 [0.24-1.09] 10/1,789 20/1,832 49% improvement All studies 51% 0.49 [0.25-0.95] 12/2,262 105/10,366 51% improvement 2 bamlanivimab COVID-19 ICU results c19ly.com Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.036 Favors bamlanivimab Favors control
Figure 5. Random effects meta-analysis for ICU admission.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chen (RCT) 74% 0.26 [0.09-0.75] hosp. 5/309 9/143 Improvement, RR [CI] Treatment Control Webb 53% 0.47 [0.31-0.72] hosp. 22/479 538/5,536 Cooper 5% 0.95 [0.69-1.30] hosp. 37/473 703/8,534 Rubin 65% 0.35 [0.12-0.94] hosp. 16/191 121/1,065 Tau​2 = 0.26, I​2 = 81.6%, p = 0.015 Early treatment 51% 0.49 [0.28-0.87] 80/1,452 1,371/15,278 51% improvement Bariola 61% 0.39 [0.22-0.70] hosp. 15/234 39/234 Improvement, RR [CI] Treatment Control Ganesh 37% 0.63 [0.43-0.91] hosp. 44/1,789 72/1,832 Chew (RCT) 25% 0.75 [0.26-2.10] hosp. 6/159 8/158 Tau​2 = 0.01, I​2 = 6.9%, p = 0.0003 Late treatment 44% 0.56 [0.40-0.76] 65/2,182 119/2,224 44% improvement All studies 48% 0.52 [0.37-0.74] 145/3,634 1,490/17,502 48% improvement 7 bamlanivimab COVID-19 hospitalization results c19ly.com Jan 18, 2022 Tau​2 = 0.13, I​2 = 67.9%, p = 0.00025 Favors bamlanivimab Favors control
Figure 6. Random effects meta-analysis for hospitalization.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chew (RCT) -14% 1.14 [0.00-455] recov. time 48 (n) 46 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.97 Late treatment -14% 1.14 [0.00-455] 0/48 0/46 -14% improvement All studies -14% 1.14 [0.00-455] 0/48 0/46 -14% improvement 1 bamlanivimab COVID-19 recovery result c19ly.com Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.97 Favors bamlanivimab Favors control
Figure 7. Random effects meta-analysis for recovery.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ 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 57% 0.43 [0.28-0.67] 0/483 0/482 57% improvement 1 bamlanivimab COVID-19 case result c19ly.com Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.00021 Favors bamlanivimab Favors control
Figure 8. Random effects meta-analysis for cases.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chew (RCT) 26% 0.74 [0.62-0.90] viral load 48 (n) 46 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.002 Late treatment 26% 0.74 [0.62-0.90] 0/48 0/46 26% improvement All studies 26% 0.74 [0.62-0.90] 0/48 0/46 26% improvement 1 bamlanivimab COVID-19 viral clearance result c19ly.com Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.002 Favors bamlanivimab Favors control
Figure 9. Random effects meta-analysis for viral clearance.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chen (RCT) 74% 0.26 [0.09-0.75] hosp. 5/309 9/143 Improvement, RR [CI] Treatment Control Gottlieb (RCT) 71% 0.29 [0.09-0.96] hosp./ER 4/101 7/52 Webb 80% 0.20 [0.03-1.46] death 1/479 57/5,536 Cooper 45% 0.55 [0.07-3.99] death 1/473 33/8,534 Rubin 97% 0.03 [0.00-0.25] death 1/191 10/1,066 Tau​2 = 0.11, I​2 = 15.1%, p < 0.0001 Early treatment 78% 0.22 [0.11-0.46] 12/1,553 116/15,331 78% improvement ACTIV-3/T.. (RCT) -100% 2.00 [0.69-5.83] death 9/163 5/151 Improvement, RR [CI] Treatment Control Ganesh 74% 0.26 [0.05-1.20] death 2/1,789 8/1,832 Tau​2 = 1.65, I​2 = 78.2%, p = 0.81 Late treatment 23% 0.77 [0.10-5.77] 11/1,952 13/1,983 23% improvement All studies 68% 0.32 [0.13-0.80] 23/3,505 129/17,314 68% improvement 7 bamlanivimab COVID-19 peer reviewed trials c19ly.com Jan 18, 2022 Tau​2 = 0.93, I​2 = 63.7%, p = 0.014 Effect extraction pre-specified, see appendix Favors bamlanivimab Favors control
Figure 10. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Exclusions
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 11 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.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chen (RCT) 74% 0.26 [0.09-0.75] hosp. 5/309 9/143 Improvement, RR [CI] Treatment Control Gottlieb (RCT) 71% 0.29 [0.09-0.96] hosp./ER 4/101 7/52 Lilly (DB RCT) 92% 0.08 [0.00-1.43] death 0/511 4/258 Webb 80% 0.20 [0.03-1.46] death 1/479 57/5,536 Tau​2 = 0.00, I​2 = 0.0%, p = 0.00012 Early treatment 76% 0.24 [0.12-0.50] 10/1,400 77/5,989 76% 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 Chew (RCT) 25% 0.75 [0.26-2.10] hosp. 6/159 8/158 Tau​2 = 0.48, I​2 = 57.4%, p = 0.34 Late treatment 36% 0.64 [0.26-1.58] 21/2,345 33/2,375 36% 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 57% 0.43 [0.27-0.70] 31/4,228 110/8,846 57% improvement 9 bamlanivimab COVID-19 studies after exclusions c19ly.com Jan 18, 2022 Tau​2 = 0.17, I​2 = 37.3%, p = 0.00056 Effect extraction pre-specified, see appendix Favors bamlanivimab Favors control
Figure 11. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Randomized Controlled Trials (RCTs)
Figure 12 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. Figure 13 and 14 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 2 summarizes the results.
Figure 12. The distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chen (RCT) 74% 0.26 [0.09-0.75] hosp. 5/309 9/143 Improvement, RR [CI] Treatment Control Gottlieb (RCT) 71% 0.29 [0.09-0.96] hosp./ER 4/101 7/52 Lilly (DB RCT) 92% 0.08 [0.00-1.43] death 0/511 4/258 Tau​2 = 0.00, I​2 = 0.0%, p = 0.00044 Early treatment 75% 0.25 [0.12-0.54] 9/921 20/453 75% improvement ACTIV-3/T.. (RCT) -100% 2.00 [0.69-5.83] death 9/163 5/151 Improvement, RR [CI] Treatment Control Chew (RCT) 25% 0.75 [0.26-2.10] hosp. 6/159 8/158 Tau​2 = 0.20, I​2 = 40.7%, p = 0.71 Late treatment -21% 1.21 [0.46-3.18] 15/322 13/309 -21% 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 50% 0.50 [0.26-0.93] 24/1,726 33/1,244 50% improvement 6 bamlanivimab COVID-19 Randomized Controlled Trials c19ly.com Jan 18, 2022 Tau​2 = 0.31, I​2 = 55.4%, p = 0.029 Effect extraction pre-specified, see appendix Favors bamlanivimab Favors control
Figure 13. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lilly (DB RCT) 92% 0.08 [0.00-1.43] 0/511 4/258 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.085 Early treatment 92% 0.08 [0.00-1.43] 0/511 4/258 92% improvement ACTIV-3/T.. (RCT) -100% 2.00 [0.69-5.83] 9/163 5/151 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Late treatment -100% 2.00 [0.69-5.83] 9/163 5/151 -100% improvement All studies 47% 0.53 [0.02-12.1] 9/674 9/409 47% improvement 2 bamlanivimab COVID-19 RCT mortality results c19ly.com Jan 18, 2022 Tau​2 = 4.03, I​2 = 76.2%, p = 0.7 Favors bamlanivimab Favors control
Figure 14. Random effects meta-analysis for RCT mortality results. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 5 6 83.3% 50% improvement
RR 0.50 [0.26‑0.93]
p = 0.029
RCT mortality results 1 2 50.0% 47% improvement
RR 0.53 [0.02‑12.14]
p = 0.7
Table 2. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Other medications might be beneficial for late stage complications, while early use may not be effective or may even be harmful. Figure 15 shows an example where efficacy declines as a function of treatment delay.
Figure 15. Effectiveness may depend critically on treatment delay.
Patient demographics.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Effect measured.
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
Variants.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Treatments.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations.
Discussion
Publication bias.
Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, several trials with negative results remain unpublished to date (NCT04575584, CTRI/2021/05/033864, and CTRI/2021/08/0354242). For bamlanivimab, 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.
The median effect size for retrospective studies is 74% improvement, compared to 64% for prospective studies, consistent with a positive publication bias. 100% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 83% of prospective studies, consistent with a bias toward publishing positive results. Figure 16 shows a scatter plot of results for prospective and retrospective studies.
Figure 16. Prospective vs. retrospective studies.
Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Conclusion
Bamlanivimab is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ICU admission, hospitalization, cases, and viral clearance. 10 studies from 7 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 62% [37‑77%] 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 11 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
Efficacy is highly variant dependent. In Vitro studies suggest a lack of efficacy for omicron [Liu, Sheward, VanBlargan]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
Study Notes
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality -100% Improvement Relative Risk c19ly.com/activ3.html Favors bamlanivimab 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 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 67% Improvement Relative Risk Death/hospitalization 64% primary Hospitalization 61% c19ly.com/bariola.html Favors bamlanivimab 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 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hospitalization 74% Improvement Relative Risk c19ly.com/chen2.html Favors bamlanivimab Favors control
[Chen] Interim analysis of the BLAZE-1 phase 2 trial of outpatients showing lower hospitalization or ER visits (1.6% versus 6.3%), and improvements in symptoms and viral load compared to placebo. NCT04427501.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hospitalization 25% Improvement Relative Risk Hospitalization (b) 52% Hospitalization (c) -1% Time to symptom improv.. -14% primary Time to symptom improv.. (b) -17% primary Viral load 26% Viral load (b) 35% c19ly.com/chew.html Favors bamlanivimab Favors control
[Chew] RCT 317 outpatients in the USA showing faster viral load and inflammatory biomarker decline, but no significant differences in clinical outcomes. ACTIV-2/A5401. NCT04518410. Supplementary data is not currently available.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 45% Improvement Relative Risk ICU admission 58% Hospitalization 5% primary Mortality (b) -17% ICU admission (b) 9% Hospitalization (b) 28% c19ly.com/cooper.html Favors bamlanivimab 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 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 74% Improvement Relative Risk ICU admission 49% Hospitalization 37% primary c19ly.com/ganesh.html Favors bamlanivimab Favors control
[Ganesh] Retrospective 2,335 bamlanivimab patients and 2,335 PSM controls in the USA, showing significantly lower hospitalization with treatment.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hospitalization/ER 71% Improvement Relative Risk Hospitalization/ER (b) 80% Hospitalization/ER (c) 75% Hospitalization/ER (d) 56% Hospitalization/ER (e) 92% c19ly.com/gottlieb.html Favors bamlanivimab 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 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Symptomatic case 57% Improvement Relative Risk Symptomatic case (b) 80% c19ly.com/lilly3.html Favors bamlanivimab Favors control
[Lilly (B)] Press release on the BLAZE-2 trial at nursing homes showing significantly lower symptomatic COVID-19 with treatment.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 92% Improvement Relative Risk Death/hospitalization 87% primary c19ly.com/lilly4.html Favors bamlanivimab Favors control
[Lilly] Results from the BLAZE-1 study of combined bamlanivimab/etesevimab, showing significantly lower mortality and combined mortality/hospitalization with treatment.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 97% Improvement Relative Risk Hospitalization 65% c19ly.com/rubin.html Favors bamlanivimab 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. The adjusted mortality result is in the abstract, with no details or mention in the paper, and we note that the change after adjustments is large: RR 0.56 -> OR 0.03.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 80% Improvement Relative Risk Hospitalization 53% Hospitalization/ER 27% primary c19ly.com/webb.html Favors bamlanivimab 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".
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 c19ly.com. Search terms were bamlanivimab, filtered for papers containing the terms COVID-19, SARS-CoV-2, or coronavirus. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of bamlanivimab 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. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.9.10) with scipy (1.7.3), pythonmeta (1.26), numpy (1.21.4), statsmodels (0.14.0), and plotly (5.4.0).
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.
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, 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://c19ly.com/meta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Chen], 10/28/2020, Randomized Controlled Trial, USA, North America, peer-reviewed, 12 authors. risk of hospitalization, 74.3% lower, RR 0.26, p = 0.02, treatment 5 of 309 (1.6%), control 9 of 143 (6.3%), NNT 21.
[Cooper], 10/8/2021, retrospective, USA, North America, 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.
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.
[Gottlieb], 1/21/2021, Randomized Controlled Trial, USA, North America, peer-reviewed, 27 authors. 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.
[Lilly], 3/10/2021, Double Blind Randomized Controlled Trial, USA, North America, preprint, 1 author. risk of death, 92.3% lower, RR 0.08, p = 0.01, treatment 0 of 511 (0.0%), control 4 of 258 (1.6%), NNT 64, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of death/hospitalization, 86.5% lower, RR 0.13, p < 0.001, treatment 4 of 511 (0.8%), control 15 of 258 (5.8%), NNT 20.
[Rubin], 11/3/2021, retrospective, USA, North America, peer-reviewed, 7 authors, study period 9 December, 2020 - 25 February, 2021, 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, 97.0% lower, RR 0.03, p < 0.01, treatment 1 of 191 (0.5%), control 10 of 1,066 (0.9%), NNT 241, odds ratio converted to relative risk.
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%), NNT 34, odds ratio converted to relative risk, IPTW weighted logistic regression.
[Webb], 6/23/2021, retrospective, USA, North America, 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.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in pooled analysis, 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, North America, peer-reviewed, 1 author, average treatment delay 7.0 days. risk of death, 100% higher, RR 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, North America, 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.
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], 12/21/2021, Randomized Controlled Trial, USA, North America, preprint, 23 authors, study period 19 August, 2020 - 17 November, 2020. 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.
relative time to symptom improvement, 17.1% higher, relative time 1.17, p = 0.08, treatment 111, control 112, 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, North America, 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.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Lilly (B)], 1/21/2021, Randomized Controlled Trial, USA, North America, 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.
Supplementary Data
References
Please send us corrections, updates, or comments. Vaccines and treatments are both valuable and complementary. All practical, effective, and safe means should be used. Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Denying the efficacy of any method increases mortality, morbidity, collateral damage, and the risk of endemic status. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. WCH and FLCCC provide treatment protocols.
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