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Tixagevimab/cilgavimab for COVID-19: real-time meta analysis of 17 studies

@CovidAnalysis, July 2024, Version 26V26
 
0 0.5 1 1.5+ All studies 43% 17 29,530 Improvement, Studies, Patients Relative Risk Mortality 42% 10 16,858 Ventilation -96% 1 0 ICU admission 65% 2 430 Hospitalization 63% 9 14,264 Recovery 6% 2 1,643 Cases 69% 10 25,258 Viral clearance -24% 1 108 RCTs 33% 5 8,839 RCT mortality 31% 4 7,718 Peer-reviewed 41% 15 21,335 Prophylaxis 49% 13 26,876 Early -29% 2 1,011 Late 32% 2 1,643 Tixagevimab/cilgavimab for COVID-19 c19early.org July 2024 after exclusions Favorstixagevimab/ci.. Favorscontrol
Abstract
Statistically significant lower risk is seen for mortality, hospitalization, and cases. 9 studies from 9 independent teams in 3 countries show significant improvements.
Meta analysis using the most serious outcome reported shows 43% [26‑56%] lower risk. Results are similar for higher quality and peer-reviewed studies and slightly worse for Randomized Controlled Trials.
Results are very robust — in exclusion sensitivity analysis 10 of 17 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 43% 17 29,530 Improvement, Studies, Patients Relative Risk Mortality 42% 10 16,858 Ventilation -96% 1 0 ICU admission 65% 2 430 Hospitalization 63% 9 14,264 Recovery 6% 2 1,643 Cases 69% 10 25,258 Viral clearance -24% 1 108 RCTs 33% 5 8,839 RCT mortality 31% 4 7,718 Peer-reviewed 41% 15 21,335 Prophylaxis 49% 13 26,876 Early -29% 2 1,011 Late 32% 2 1,643 Tixagevimab/cilgavimab for COVID-19 c19early.org July 2024 after exclusions Favorstixagevimab/ci.. Favorscontrol
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BA.2.75.2, BA.4.6, and BQ.1.11, BA.5, BA.2.75, XBB2, ХВВ.1.9.1, XBB.1.9.3, XBB.1.5.24, XBB.1.16, XBB.2.9, BQ.1.1.45, CL.1, and CH.1.13. US EUA has been revoked. mAb use may create new variants that spread globally4,5, and may be associated with prolonged viral loads, clinical deterioration, and immune escape5-7.
No treatment or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Tixagevimab/cilgavimab p=0.000029 Acetaminophen p=0.00000029 2020 2021 2022 2023 2024 Effective Harmful c19early.org July 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Tixagevimab/cilgavimab for COVID-19 — Highlights
Tixagevimab/cilgavimab reduces risk with very high confidence for mortality, hospitalization, and in pooled analysis, and high confidence for cases, however increased risk is seen with very low confidence for ventilation and viral clearance. Efficacy is variant dependent.
39th treatment shown effective with ≥3 clinical studies in May 2022, now with p = 0.000029 from 17 studies, and recognized in 31 countries.
Outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 79 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ TACKLE Montgom.. (DB RCT) 0% 1.00 [0.32-3.07] death 6/452 6/451 Improvement, RR [CI] Treatment Control Lombardi -368% 4.68 [0.31-71.6] death 1/19 1/89 Immunocompromised OT​1 Tau​2 = 0.06, I​2 = 5.3%, p = 0.67 Early treatment -29% 1.29 [0.42-3.95] 7/471 7/540 29% higher risk ACTIV-3-TICO Holland (DB RCT) 30% 0.70 [0.50-0.97] death 61/710 86/707 Improvement, RR [CI] Treatment Control DisCoVeRy Hites (DB RCT) 40% 0.60 [0.28-1.23] death 12/123 16/103 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0085 Late treatment 32% 0.68 [0.51-0.91] 73/833 102/810 32% lower risk STORM CHASER Levin (DB RCT) 42% 0.58 [0.34-1.00] symp. case 27/749 23/372 Improvement, RR [CI] Treatment Control PROVENT Levin (DB RCT) 86% 0.14 [0.01-2.98] death 0/3,441 2/1,731 Young-Xu (PSM) 64% 0.36 [0.18-0.73] death 1,733 (n) 6,354 (n) Immunocompromised Ollila 76% 0.24 [0.01-5.61] death 0/25 1/12 Immunocompromised Kertes 92% 0.08 [0.01-0.54] death/hosp. 1/825 63/4,299 Immunocompromised Najjar-Debbiny 59% 0.41 [0.19-0.89] hosp. 72/703 377/2,812 Immunocompromised Kaminski 92% 0.08 [0.01-1.16] death 1/333 2/97 Immunocompromised Al Jurdi 86% 0.14 [0.01-2.75] death 0/222 3/222 Immunocompromised Sindu (PSM) -1% 1.01 [0.14-7.21] death 2/17 2/17 Immunocompromised Din 19% 0.81 [0.32-2.04] hosp. 5/23 11/41 Immunocompromised Desai 12% 0.88 [0.33-2.35] hosp. 391 (n) 391 (n) Solera 26% 0.74 [0.32-1.60] severe case 7/156 283/1,819 Bes-Berlandier -10% 1.10 [0.37-3.31] progression 3/14 15/77 Tau​2 = 0.07, I​2 = 27.0%, p < 0.0001 Prophylaxis 49% 0.51 [0.38-0.69] 118/8,632 782/18,244 49% lower risk All studies 43% 0.57 [0.44-0.74] 198/9,936 891/19,594 43% lower risk 17 tixagevimab/cilgavimab COVID-19 studies c19early.org July 2024 Tau​2 = 0.07, I​2 = 36.4%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors tixagevimab/ci.. Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ TACKLE Montgo.. (DB RCT) 0% death Improvement Relative Risk [CI] Lombardi -368% death Immunocompromised OT​1 Tau​2 = 0.06, I​2 = 5.3%, p = 0.67 Early treatment -29% 29% higher risk ACTIV-3-TICO Holland (DB RCT) 30% death DisCoVeRy Hites (DB RCT) 40% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.0085 Late treatment 32% 32% lower risk STORM CHASER Levin (DB RCT) 42% symp. case PROVENT Levin (DB RCT) 86% death Young-Xu (PSM) 64% death Immunocompromised Ollila 76% death Immunocompromised Kertes 92% death/hosp. Immunocompromised Najjar-Debbiny 59% hospitalization Immunocompromised Kaminski 92% death Immunocompromised Al Jurdi 86% death Immunocompromised Sindu (PSM) -1% death Immunocompromised Din 19% hospitalization Immunocompromised Desai 12% hospitalization Solera 26% severe case Bes-Berlandier -10% progression Tau​2 = 0.07, I​2 = 27.0%, p < 0.0001 Prophylaxis 49% 49% lower risk All studies 43% 43% lower risk 17 tixagevimab/cilgavimab C19 studies c19early.org July 2024 Tau​2 = 0.07, I​2 = 36.4%, p < 0.0001 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors tixagevimab/ci.. Favors control
B
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix. B. Timeline of results in tixagevimab/cilgavimab studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 1.3 months, compared to using all studies.
Introduction
SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury8-15 and cognitive deficits10,15, cardiovascular complications16, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factorsA,17-21, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk22, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
We analyze all significant controlled studies of tixagevimab/cilgavimab for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Variant Dependence
Extensive mutations in SARS-CoV-2 have resulted in variants that evade neutralizing antibodies from monoclonal antibody treatments23, resulting in efficacy that is highly variant dependent. For example, in vitro research suggests that tixagevimab/cilgavimab is not effective for omicron BA.4.6 and BQ.1.11. While the FDA has suspended the EUA for tixagevimab/cilgavimab due to a predicted lack of efficacy, it may retain efficacy for certain post-suspension variants24. Table 1 shows efficacy by variant for several monoclonal antibodies. This table covers earlier SARS-CoV-2 variants and has not been updated for more recent variants.
Table 1. Predicted efficacy by variant from Davis et al. (not updated for more recent variants).    : likely effective    : likely ineffective    : unknown. Submit updates.
Bamlanivimab/
etesevimab
Casirivimab/
imdevimab
Sotrovimab Bebtelovimab Tixagevimab/
cilgavimab
Alpha B.1.1.7
Beta/ ​Gamma BA1.351/ ​P.1
Delta B.1.617.2
Omicron BA.1/ ​BA.1.1
Omicron BA.2
Omicron BA.5
Omicron BA.4.6
Omicron BQ.1.1
Results
Table 2 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Table 3 shows results by treatment stage. Figure 3 plots individual results by treatment stage. Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
Table 2. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies43% [26‑56%]
****
17 29,530 397
After exclusions45% [29‑57%]
****
15 29,358 352
Peer-reviewed studiesPeer-reviewed41% [24‑55%]
****
15 21,335 366
Randomized Controlled TrialsRCTs33% [15‑48%]
**
5 8,839 254
Mortality42% [16‑60%]
**
10 16,858 311
ICU admissionICU65% [-2078‑99%]2 430 28
HospitalizationHosp.63% [39‑77%]
****
9 14,264 118
Recovery6% [-4‑15%]2 1,643 189
Cases69% [11‑89%]
*
10 25,258 121
RCT mortality31% [9‑48%]
**
4 7,718 233
Table 3. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies-29% [-295‑58%]32% [9‑49%]
**
49% [31‑62%]
****
After exclusions0% [-207‑68%]32% [9‑49%]
**
51% [33‑64%]
****
Peer-reviewed studiesPeer-reviewed0% [-207‑68%]32% [9‑49%]
**
46% [24‑62%]
***
Randomized Controlled TrialsRCTs0% [-207‑68%]32% [9‑49%]
**
44% [5‑67%]
*
Mortality-29% [-295‑58%]32% [9‑49%]
**
67% [39‑82%]
***
ICU admissionICU65% [-2078‑99%]
HospitalizationHosp.55% [24‑74%]
**
67% [35‑83%]
**
Recovery6% [-4‑15%]
Cases69% [11‑89%]
*
RCT mortality0% [-207‑68%]32% [9‑49%]
**
86% [-198‑99%]
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Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 4. Random effects meta-analysis for all studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for viral clearance.
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Figure 13. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below. Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Davidson et al. also showed no important difference between meta analysis results of preprints and peer-reviewed publications for COVID-19, based on 37 meta analyses including 114 trials.
Randomized Controlled Trials (RCTs)
Figure 14 shows a comparison of results for RCTs and non-RCT studies. Figure 15 and 16 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. RCT results are included in Table 2 and Table 3.
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Figure 14. Results for RCTs and non-RCT studies.
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Figure 15. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
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Figure 16. Random effects meta-analysis for RCT mortality results.
RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases28, and analysis of double-blind RCTs has identified extreme levels of bias29. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, reporting, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
RCTs are expensive and many RCTs are funded by pharmaceutical companies or interests closely aligned with pharmaceutical companies. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 79 treatments we have analyzed, 63% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.
Evidence shows that non-RCT studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see34,35.
Currently, 47 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 30 have been confirmed in RCTs, with a mean delay of 7.0 months. When considering only low cost treatments, 25 have been confirmed with a delay of 8.4 months. For the 17 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 14 are all consistent with the overall results (benefit or harm), with 11 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
Exclusions
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which can be easily influenced by potential bias, may ignore or underemphasize serious issues not captured in the checklists, and may overemphasize issues unlikely to alter outcomes in specific cases (for example certain specifics of randomization with a very large effect size and well-matched baseline characteristics).
The studies excluded are as below. Figure 17 shows a forest plot for random effects meta-analysis of all studies after exclusions.
Din, unadjusted results with no group details.
Lombardi, study compares against another treatment showing significant efficacy.
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Figure 17. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours38,39. Baloxavir studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar et al. report only 2.5 hours improvement for inpatient treatment.
Table 4. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases40
<24 hours-33 hours symptoms41
24-48 hours-13 hours symptoms41
Inpatients-2.5 hours to improvement42
Figure 18 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 79 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 18. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 79 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results, for example as in López-Medina et al.
Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants44, for example the Gamma variant shows significantly different characteristics45-48. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants49,50.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic51-61, therefore efficacy may depend strongly on combined treatments.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
Pooled Effects
This section validates the use of pooled effects for COVID-19, which enables earlier detection of efficacy, however note that pooled effects are no longer required for tixagevimab/cilgavimab as of May 2022. Efficacy is now known for tixagevimab/cilgavimab based on specific outcomes for all studies and when restricted to RCTs.
For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results.
We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.
Another way to view pooled analysis is that we are using more of the available information. Logically we should, and do, use additional information. For example dose-response and treatment delay-response relationships provide significant additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from all 79 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Figure 19 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.000000000001). Similarly, Figure 20 shows that improved recovery is very strongly associated with lower mortality (p < 0.000000000001). Considering the extremes, Singh et al. show an association between viral clearance and hospitalization or death, with p = 0.003 after excluding one large outlier from a mutagenic treatment, and based on 44 RCTs including 52,384 patients. Figure 21 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.0000011 to p = 0.0000000036.
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Figure 19. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 20. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 19. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 47 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.2 months. When restricting to RCTs only, 54% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months. Figure 22 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 22. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral clearance may show no efficacy if most studies only examine viral clearance. In practice, it is rare for a non-antiviral treatment to report viral clearance and to not report clinical outcomes; and in practice other sources of heterogeneity such as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster detection of efficacy on average. However, as with all meta analyses, it is important to review the different studies included. We also present individual outcome analyses, which may be more informative for specific use cases.
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 tixagevimab/cilgavimab, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
Figure 23 shows a scatter plot of results for prospective and retrospective studies. 50% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 60% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 42% improvement, compared to 40% for prospective studies, showing similar results.
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Figure 23. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 24 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 < 0.0565-72. 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 24. Example funnel plot analysis for simulated perfect trials.
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses for specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials with conflicts of interest may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone51-61. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
1 of the 17 studies compare against other treatments, which may reduce the effect seen.
Focosi (B) et al. present a review covering tixagevimab/cilgavimab for COVID-19.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors17-21, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk22, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Figure 25 shows an overview of the results for tixagevimab/cilgavimab in the context of multiple COVID-19 treatments, and Figure 26 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Figure 25. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random effects meta-analysis. 0.6% of 7,000+ proposed treatments show efficacy74.
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Figure 26. Efficacy vs. cost for COVID-19 treatments.
Tixagevimab/cilgavimab is an effective treatment for COVID-19. Statistically significant lower risk is seen for mortality, hospitalization, and cases. 9 studies from 9 independent teams in 3 countries show significant improvements. Meta analysis using the most serious outcome reported shows 43% [26‑56%] lower risk. Results are similar for higher quality and peer-reviewed studies and slightly worse for Randomized Controlled Trials. Results are very robust — in exclusion sensitivity analysis 10 of 17 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BA.2.75.2, BA.4.6, and BQ.1.11, BA.5, BA.2.75, XBB2, ХВВ.1.9.1, XBB.1.9.3, XBB.1.5.24, XBB.1.16, XBB.2.9, BQ.1.1.45, CL.1, and CH.1.13. US EUA has been revoked. mAb use may create new variants that spread globally4,5, and may be associated with prolonged viral loads, clinical deterioration, and immune escape5-7.
Mortality 86% Improvement Relative Risk Hospitalization 83% Case 66% Tixagevimab/c..  Al Jurdi et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 444 patients in the USA Fewer cases with tixagevimab/cilgavimab (p=0.0011) c19early.org Al Jurdi et al., American J. Transplan.., Dec 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Al Jurdi: Retrospective cohort study of 444 solid organ transplant recipients showing significantly lower risk of SARS-CoV-2 breakthrough infections with tixagevimab/cilgavimab pre-exposure prophylaxis compared to controls during the omicron period.

Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene76,77, vitamin D78, etc.) — either because the physician recommending tixagevimab/cilgavimab also recommended them, or because the patient seeking out tixagevimab/cilgavimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Oxygen increase, ICU, o.. -10% Improvement Relative Risk Tixagevimab/c..  Bes-Berlandier et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 91 patients in France (March 2020 - April 2022) Study underpowered to detect differences c19early.org Bes-Berlandier et al., BMC Infectious .., May 2024 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Bes-Berlandier: Retrospective 91 lung transplant recipients with COVID-19 showing no significant difference in poor outcomes with casirivimab/imdevimab or tixagevimab/cilgavimab prophylaxis in univariate analysis.
Hospitalization 12% Improvement Relative Risk Case -35% Tixagevimab/c..  Desai et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 782 patients in the USA (January - October 2022) More cases with tixagevimab/cilgavimab (not stat. sig., p=0.18) c19early.org Desai et al., Crohn's & Colitis 360, Sep 2023 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Desai: TriNetX PSM retrospective 408 IBD patients receiving tixagevimab/cilgavimab and matched controls, showing no significant difference in COVID-19 cases or hospitalization.
Hospitalization 19% Improvement Relative Risk Tixagevimab/c.. for COVID-19  Din et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 64 patients in the USA (June 2020 - February 2022) Study underpowered to detect differences c19early.org Din et al., Hemasphere, August 2023 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Din: Retrospective 64 COVID+ CAR-T cell therapy recipients, showing lower hospitalization with tixagevimab/cilgavimab prophylaxis in unadjusted results, without statistical significance.
Mortality 40% Improvement Relative Risk 7-point scale -18% primary Recovery time 1% Time to discharge 9% Tixagevimab/c..  DisCoVeRy  LATE TREATMENT  DB RCT Is late treatment with tixagevimab/cilgavimab beneficial for COVID-19? Double-blind RCT 226 patients in France Lower mortality (p=0.17) and worse 7-point scale results (p=0.52), not sig. c19early.org Hites et al., J. Infection, February 2024 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Hites: RCT 173 hospitalized COVID-19 patients showing no significant difference in clinical status, time to recovery, viral clearance, or mortality with tixagevimab/cilgavimab. Mortality was lower, without statistical significance. The trial was terminated early due to concerns about reduced efficacy against circulating variants.
Mortality 30% Improvement Relative Risk Recovery 7% primary Tixagevimab/c..  ACTIV-3-TICO  LATE TREATMENT  DB RCT Is late treatment with tixagevimab/cilgavimab beneficial for COVID-19? Double-blind RCT 1,417 patients in the USA (February - September 2021) Lower mortality with tixagevimab/cilgavimab (p=0.032) c19early.org Holland et al., The Lancet Respiratory.., Jul 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Holland: RCT with 710 hospitalized patients treated with tixagevimab/cilgavimab, and 707 placebo patients, showing lower mortality with treatment.
Mortality 92% Improvement Relative Risk ICU admission 96% Hospitalization 95% Symp. case 99% Tixagevimab/c..  Kaminski et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 430 patients in France (December 2021 - February 2022) Lower ICU admission (p=0.001) and hospitalization (p=0.001) c19early.org Kaminski et al., Kidney Int., October 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Kaminski: Retrospective 430 kidney transplant recipients showing significantly lower symptomatic COVID-19 and hospitalization with tixagevimab/cilgavimab preexposure prophylaxis compared to 97 patients who did not receive it, during an omicron wave.
Death/hospitalization 92% Improvement Relative Risk Case 47% Tixagevimab/c..  Kertes et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 5,124 patients in Israel Lower death/hosp. (p=0.013) and fewer cases (p=0.012) c19early.org Kertes et al., Clinical Infectious Dis.., Jul 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Kertes: Retrospective 825 immunocompromised individuals treated with tixagevimab-cilgavimab and 4229 untreated in Israel, showing significantly lower infection and hospitalization/death with treatment. Omicron was the dominant variant.
Mortality 86% Improvement Relative Risk Symp. case 82% Symp. case (b) 76% Tixagevimab/c..  PROVENT  Prophylaxis  DB RCT Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Double-blind RCT 5,172 patients in multiple countries (Nov 2020 - Mar 2021) Fewer symptomatic cases with tixagevimab/cilgavimab (p<0.000001) c19early.org Levin et al., New England J. Medicine, Apr 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Levin: PrEP RCT with 3,441 tixagevimab/cilgavimab patients and 1,731 control patients, showing lower risk of symptomatic cases with treatment.
Symp. case 42% Improvement Relative Risk Symp. case (b) 33% primary Tixagevimab/c..  STORM CHASER  Prophylaxis  DB RCT Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Double-blind RCT 1,121 patients in the USA (December 2020 - March 2021) Fewer symptomatic cases with tixagevimab/cilgavimab (not stat. sig., p=0.064) c19early.org Levin et al., Clinical Infectious Dise.., Dec 2021 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Levin (B): 1,121 patient PEP RCT showing lower symptomatic cases with tixagevimab/cilgavimab, without statistical significance.
Mortality -368% Improvement Relative Risk Hospitalization 33% Viral clearance -24% Tixagevimab/c..  Lombardi et al.  EARLY TREATMENT Is early treatment with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 108 patients in Italy (August - October 2022) Study compares with other mAbs, results vs. placebo may differ Higher mortality (p=0.32) and worse viral clearance (p=0.3), not sig. c19early.org Lombardi et al., MDPI AG, January 2023 Favorstixagevimab/ci.. Favorsother mAbs 0 0.5 1 1.5 2+
Lombardi: Retrospective immunocompromised patients, showing no significant difference between tixagevimab/cilgavimab and other mAbs.
Mortality 0% Improvement Relative Risk Mortality (b) 50% Severe case 50% primary Hospitalization 57% Tixagevimab/c..  TACKLE  EARLY TREATMENT  DB RCT Is early treatment with tixagevimab/cilgavimab beneficial for COVID-19? Double-blind RCT 903 patients in the USA (January - July 2021) Lower severe cases (p=0.0096) and hospitalization (p=0.0023) c19early.org Montgomery et al., The Lancet Respirat.., Jun 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Montgomery: RCT 910 outpatients in the USA, 456 treated with tixagevimab/cilgavimab, showing significantly lower severe cases and hospitalization with treatment, but no difference in mortality.
Hospitalization 59% Improvement Relative Risk Case 25% Tixagevimab/c..  Najjar-Debbiny et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 3,515 patients in Israel Lower hospitalization (p=0.023) and fewer cases (p=0.025) c19early.org Najjar-Debbiny et al., Clinical Infect.., Oct 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Najjar-Debbiny: Retrospective 732 immunocompromised patients in Israel treated with tixagevimab/cilgavimab, and 2,812 matched controls, showing significantly lower cases and hospitalization with treatment.
Mortality 76% Improvement Relative Risk Case 90% Tixagevimab/c..  Ollila et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 37 patients in the USA (February 2021 - February 2022) Fewer cases with tixagevimab/cilgavimab (p=0.028) c19early.org Ollila et al., Cancer, July 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Ollila: Retrospective 378 patients with hematologic malignancies analyzing seroconversion and outcomes post-vaccination. Among 25 seronegative patients after booster vaccination who received tixagevimab/cilgavimab prophylaxis, no COVID-19 infections occurred, whereas 3 infections and 1 death occurred among 12 comparable patients not receiving prophylaxis.
Mortality -1% Improvement Relative Risk Ventilation -96% ICU admission -210% Hospitalization 53% Symp. case 29% unadjusted Tixagevimab/c..  Sindu et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 546 patients in the USA (December 2021 - August 2022) Higher ventilation (p=0.58) and ICU admission (p=0.33), not sig. c19early.org Sindu et al., Transplantation Direct, May 2023 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Sindu: Retrospective 546 lung transplant recipients, 203 receiving tixagevimab/cilgavimab, and 343 out of state or declining treatment, showing a trend towards lower incidence of cases, but no significant difference in clinical outcomes.
Severe case 26% Improvement Relative Risk Tixagevimab/c..  Solera et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? Retrospective 1,975 patients in Canada Lower severe cases with tixagevimab/cilgavimab (not stat. sig., p=0.48) c19early.org Solera et al., American J. Transplanta.., Mar 2024 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Solera: Retrospective 1,975 solid organ transplant recipients with COVID-19 showing lower risk of severe cases with tixagevimab/cilgavimab prophylaxis, without statistical significance.
Mortality 64% Improvement Relative Risk Death/hospitalization/c.. 69% Hospitalization 87% Case 66% Tixagevimab/c..  Young-Xu et al.  Prophylaxis Is prophylaxis with tixagevimab/cilgavimab beneficial for COVID-19? PSM retrospective 8,087 patients in the USA Lower mortality (p=0.0043) and death/hosp./cases (p<0.0001) c19early.org Young-Xu et al., medRxiv, May 2022 Favorstixagevimab/ci.. Favorscontrol 0 0.5 1 1.5 2+
Young-Xu: PSM retrospective 1,848 immunocompromised patients given tixagevimab/cilgavimab prophylaxis, showing lower mortality, hospitalization, and cases.
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms are tixagevimab, cilgavimab, Evusheld and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of tixagevimab/cilgavimab for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcomes are considered more important than viral test status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available. After most or all patients have recovered there is little or no room for an effective treatment to do better, however faster recovery is valuable. If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to93. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed Altman, Altman (B), and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 196. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.12.4) with scipy (1.14.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.22.0).
Forest plots are computed using PythonMeta97 with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I2 statistic. Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor (4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.0 is used to parse PDF documents.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective38,39.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/tcmeta.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.
Lombardi, 1/19/2023, retrospective, Italy, preprint, 21 authors, study period 28 August, 2022 - 15 October, 2022, this trial compares with another treatment - results may be better when compared to placebo, excluded in exclusion analyses: study compares against another treatment showing significant efficacy. risk of death, 368.4% higher, RR 4.68, p = 0.32, treatment 1 of 19 (5.3%), control 1 of 89 (1.1%), day 14.
risk of hospitalization, 33.1% lower, RR 0.67, p = 1.00, treatment 1 of 19 (5.3%), control 7 of 89 (7.9%), NNT 38, day 14.
risk of no viral clearance, 23.7% higher, RR 1.24, p = 0.30, treatment 14 of 19 (73.7%), control 53 of 89 (59.6%), day 14.
Montgomery, 6/7/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 46.0, 20 authors, study period 28 January, 2021 - 22 July, 2021, trial NCT04723394 (history) (TACKLE). risk of death, 0.2% lower, RR 1.00, p = 1.00, treatment 6 of 452 (1.3%), control 6 of 451 (1.3%), NNT 33975, all cause mortality.
risk of death, 50.1% lower, RR 0.50, p = 0.34, treatment 3 of 452 (0.7%), control 6 of 451 (1.3%), NNT 150, COVID-19 mortality.
risk of severe case, 50.4% lower, RR 0.50, p = 0.010, treatment 18 of 407 (4.4%), control 37 of 415 (8.9%), NNT 22, primary outcome.
risk of hospitalization, 56.7% lower, RR 0.43, p = 0.002, treatment 17 of 413 (4.1%), control 40 of 421 (9.5%), NNT 19.
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.
Hites, 2/16/2024, Double Blind Randomized Controlled Trial, placebo-controlled, France, peer-reviewed, 86 authors, trial NCT04315948 (history) (DisCoVeRy). risk of death, 39.9% lower, RR 0.60, p = 0.17, treatment 12 of 123 (9.8%), control 16 of 103 (15.5%), NNT 17, odds ratio converted to relative risk, day 90.
risk of 7-point scale, 17.6% higher, OR 1.18, p = 0.52, treatment 123, control 103, inverted to make OR<1 favor treatment, day 15, primary outcome, RR approximated with OR.
recovery time, 1.0% lower, relative time 0.99, p = 0.93, treatment 123, control 103, inverted to make RR<1 favor treatment.
time to discharge, 9.1% lower, relative time 0.91, p = 0.49, treatment 123, control 103, inverted to make RR<1 favor treatment.
Holland, 7/8/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 103 authors, study period 10 February, 2021 - 30 September, 2021, average treatment delay 8.0 days, trial NCT04501978 (history) (ACTIV-3-TICO). risk of death, 30.0% lower, RR 0.70, p = 0.03, treatment 61 of 710 (8.6%), control 86 of 707 (12.2%), NNT 28, day 90.
risk of no recovery, 7.4% lower, RR 0.93, p = 0.21, treatment 710, control 707, inverted to make RR<1 favor treatment, sustained recovery, day 90, primary outcome.
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.
Al Jurdi, 12/31/2022, retrospective, USA, peer-reviewed, 6 authors. risk of death, 85.7% lower, RR 0.14, p = 0.25, treatment 0 of 222 (0.0%), control 3 of 222 (1.4%), NNT 74, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 83.3% lower, RR 0.17, p = 0.12, treatment 1 of 222 (0.5%), control 6 of 222 (2.7%), NNT 44.
risk of case, 65.6% lower, RR 0.34, p = 0.001, treatment 11 of 222 (5.0%), control 32 of 222 (14.4%), NNT 11.
Bes-Berlandier, 5/28/2024, retrospective, France, peer-reviewed, median age 51.0, 10 authors, study period March 2020 - April 2022. oxygen increase, ICU, or mortality, 10.0% higher, RR 1.10, p = 1.00, treatment 3 of 14 (21.4%), control 15 of 77 (19.5%).
Desai, 9/6/2023, retrospective, USA, peer-reviewed, 4 authors, study period 1 January, 2022 - 28 October, 2022. risk of hospitalization, 12.0% lower, OR 0.88, p = 0.81, treatment 391, control 391, RR approximated with OR.
risk of case, 35.0% higher, OR 1.35, p = 0.18, treatment 391, control 391, RR approximated with OR.
Din, 8/8/2023, retrospective, USA, peer-reviewed, 24 authors, study period June 2020 - February 2022, excluded in exclusion analyses: unadjusted results with no group details. risk of hospitalization, 19.0% lower, RR 0.81, p = 0.77, treatment 5 of 23 (21.7%), control 11 of 41 (26.8%), NNT 20.
Kaminski, 10/31/2022, retrospective, France, peer-reviewed, 21 authors, study period 28 December, 2021 - 28 February, 2022. risk of death, 92.4% lower, HR 0.08, p = 0.07, treatment 1 of 333 (0.3%), control 2 of 97 (2.1%), NNT 57, Cox proportional hazards.
risk of ICU admission, 95.5% lower, HR 0.04, p = 0.001, treatment 2 of 333 (0.6%), control 6 of 97 (6.2%), NNT 18, Cox proportional hazards.
risk of hospitalization, 95.4% lower, HR 0.05, p = 0.001, treatment 4 of 333 (1.2%), control 11 of 97 (11.3%), NNT 9.9, Cox proportional hazards.
risk of symptomatic case, 98.9% lower, HR 0.01, p = 0.001, treatment 41 of 333 (12.3%), control 42 of 97 (43.3%), NNT 3.2, Cox proportional hazards.
Kertes, 7/29/2022, retrospective, Israel, peer-reviewed, 10 authors. risk of death/hospitalization, 91.9% lower, RR 0.08, p = 0.01, treatment 1 of 825 (0.1%), control 63 of 4,299 (1.5%), NNT 74, adjusted per study, odds ratio converted to relative risk, multivariable.
risk of case, 47.1% lower, RR 0.53, p = 0.01, treatment 29 of 825 (3.5%), control 308 of 4,299 (7.2%), NNT 27, adjusted per study, odds ratio converted to relative risk, multivariable.
Levin, 4/20/2022, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 24 authors, study period 21 November, 2020 - 22 March, 2021, trial NCT04625725 (history) (PROVENT). risk of death, 85.7% lower, RR 0.14, p = 0.11, treatment 0 of 3,441 (0.0%), control 2 of 1,731 (0.1%), NNT 866, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of symptomatic case, 82.1% lower, RR 0.18, p < 0.001, treatment 11 of 3,441 (0.3%), control 31 of 1,731 (1.8%), NNT 68, 6 months.
risk of symptomatic case, 76.3% lower, RR 0.24, p < 0.001, treatment 8 of 3,441 (0.2%), control 17 of 1,731 (1.0%), NNT 133, median 83 days followup.
Levin (B), 12/8/2021, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 46.0, 21 authors, study period 2 December, 2020 - 19 March, 2021, trial NCT04625972 (history) (STORM CHASER). risk of symptomatic case, 41.7% lower, RR 0.58, p = 0.06, treatment 27 of 749 (3.6%), control 23 of 372 (6.2%), NNT 39, extended data cutoff.
risk of symptomatic case, 32.8% lower, RR 0.67, p = 0.23, treatment 23 of 749 (3.1%), control 17 of 372 (4.6%), NNT 67, primary outcome.
Najjar-Debbiny, 10/31/2022, retrospective, Israel, peer-reviewed, 5 authors. risk of hospitalization, 59.0% lower, HR 0.41, p = 0.02, treatment 72 of 703 (10.2%), control 377 of 2,812 (13.4%), Cox proportional hazards.
risk of case, 25.0% lower, HR 0.75, p = 0.03, treatment 72 of 703 (10.2%), control 377 of 2,812 (13.4%), NNT 32, Cox proportional hazards.
Ollila, 7/11/2022, retrospective, USA, peer-reviewed, 13 authors, study period February 2021 - February 2022. risk of death, 75.5% lower, RR 0.24, p = 0.32, treatment 0 of 25 (0.0%), control 1 of 12 (8.3%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of case, 90.2% lower, RR 0.10, p = 0.03, treatment 0 of 25 (0.0%), control 3 of 12 (25.0%), NNT 4.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Sindu, 5/12/2023, retrospective, USA, peer-reviewed, median age 67.4, 7 authors, study period December 2021 - August 2022. risk of death, 1.5% higher, HR 1.01, p = 0.99, treatment 2 of 17 (11.8%), control 2 of 17 (11.8%), propensity score matching, Cox proportional hazards.
risk of mechanical ventilation, 95.8% higher, HR 1.96, p = 0.58, propensity score matching, Cox proportional hazards.
risk of ICU admission, 209.6% higher, HR 3.10, p = 0.33, propensity score matching, Cox proportional hazards.
risk of hospitalization, 53.2% lower, HR 0.47, p = 0.17, propensity score matching, Cox proportional hazards.
risk of symptomatic case, 28.9% lower, RR 0.71, p = 0.14, treatment 24 of 203 (11.8%), control 57 of 343 (16.6%), NNT 21, unadjusted.
Solera, 3/31/2024, retrospective, Canada, peer-reviewed, median age 57.5, 12 authors. risk of severe case, 25.6% lower, RR 0.74, p = 0.48, treatment 7 of 156 (4.5%), control 283 of 1,819 (15.6%), NNT 9.0, adjusted per study, odds ratio converted to relative risk, multivariable.
Young-Xu, 5/29/2022, retrospective, propensity score matching, USA, preprint, 10 authors. risk of death, 64.0% lower, HR 0.36, p = 0.004, treatment 1,733, control 6,354.
risk of death/hospitalization/cases, 69.0% lower, HR 0.31, p < 0.001, treatment 17 of 1,733 (1.0%), control 206 of 6,354 (3.2%), NNT 44.
risk of hospitalization, 87.0% lower, HR 0.13, p = 0.04, treatment 1,733, control 6,354.
risk of case, 66.0% lower, HR 0.34, p = 0.03, treatment 1,733, control 6,354.
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