Top
Introduction
Variant Dependence
Results
Randomized Controlled Trials
Heterogeneity
Discussion
Conclusion
Study Notes
Methods and Data
Supplementary
References

All studies
Mortality
Hospitalization
COVID-19 cases
Peer reviewed
All RCTs
RCT mortality

Feedback
Home
Show Outline
Top   Intro   Variant   Results   RCT   Heterogeneity   Discussion   Conclusion   StudyNotes   Appendix   SupplementarySupp.   ReferencesRef.
Home   COVID-19 treatment studies for Tixagevimab/cilgavimab  COVID-19 treatment studies for Tixagev../c..  C19 studies: Tixagev../c..  Tixagev../c..   Select treatmentSelect treatmentTreatmentsTreatments
Antiandrogens (meta) Lactoferrin (meta)
Aspirin (meta) Melatonin (meta)
Bamlaniv../e.. (meta) Metformin (meta)
Bebtelovimab (meta) Molnupiravir (meta)
Bromhexine (meta) N-acetylcys.. (meta)
Budesonide (meta) Nigella Sativa (meta)
Cannabidiol (meta) Nitazoxanide (meta)
Casirivimab/i.. (meta) Paxlovid (meta)
Colchicine (meta) Peg.. Lambda (meta)
Conv. Plasma (meta) Povidone-Iod.. (meta)
Curcumin (meta) Probiotics (meta)
Diet (meta) Proxalutamide (meta)
Ensitrelvir (meta) Quercetin (meta)
Ensovibep (meta) Remdesivir (meta)
Exercise (meta) Sleep (meta)
Famotidine (meta) Sotrovimab (meta)
Favipiravir (meta) Tixagev../c.. (meta)
Fluvoxamine (meta) Vitamin A (meta)
Hydroxychlor.. (meta) Vitamin C (meta)
Iota-carragee.. (meta) Vitamin D (meta)
Ivermectin (meta) Zinc (meta)

Other Treatments Global Adoption
Loading...
Antiandrogens
Aspirin
Bromhexine
Budesonide
Cannabidiol
Casirivimab/i..
Colchicine
Conv. Plasma
Curcumin
Diet
Ensovibep
Exercise
Famotidine
Favipiravir
Fluvoxamine
Hydroxychlor..
Iota-carragee..
Ivermectin
Lactoferrin
Melatonin
Metformin
Molnupiravir
Nigella Sativa
Nitazoxanide
Paxlovid
Peg.. Lambda
Povidone-Iod..
Proxalutamide
Quercetin
Remdesivir
Sleep
Sotrovimab
Vitamin A
Vitamin C
Vitamin D
Zinc
Tixagevimab/cilgavimab for COVID-19: real-time meta analysis of 4 studies
Covid Analysis, July 6, 2022, DRAFT
https://c19early.com/tcmeta.html
0 0.5 1 1.5+ All studies 47% 4 15,283 Improvement, Studies, Patients Relative Risk Mortality 52% 3 14,162 Hospitalization 65% 2 8,921 Cases 66% 3 14,380 RCTs 36% 3 7,196 RCT mortality 36% 2 6,075 Peer-reviewed 36% 2 6,075 Prophylaxis 51% 3 14,380 Early 0% 1 903 Tixagevimab/cilgavimab for COVID-19 c19early.com/tc Jul 2022 Favorstixagevimab/ci.. Favorscontrol
Statistically significant improvements are seen for hospitalization and cases. 3 studies from 3 independent teams (all from the same country) show statistically significant improvements in isolation (1 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 47% [18‑66%] improvement. Results are slightly worse for Randomized Controlled Trials and slightly worse for peer-reviewed studies.
0 0.5 1 1.5+ All studies 47% 4 15,283 Improvement, Studies, Patients Relative Risk Mortality 52% 3 14,162 Hospitalization 65% 2 8,921 Cases 66% 3 14,380 RCTs 36% 3 7,196 RCT mortality 36% 2 6,075 Peer-reviewed 36% 2 6,075 Prophylaxis 51% 3 14,380 Early 0% 1 903 Tixagevimab/cilgavimab for COVID-19 c19early.com/tc Jul 2022 Favorstixagevimab/ci.. Favorscontrol
Currently there is limited data, with only 31 control events for the most serious outcome in trials to date.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BA.2 [Zhou]. 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 25% of tixagevimab/cilgavimab studies show zero events in the treatment arm. Multiple treatments are typically used in combination, and other treatments may be more effective.
No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used. 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.
Highlights
Tixagevimab/cilgavimab reduces risk for COVID-19 with very high confidence for cases and in pooled analysis, high confidence for hospitalization, and low confidence for mortality. Efficacy is variant dependent.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 42 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Montgom.. (DB RCT) 0% 1.00 [0.32-3.07] death 6/452 6/451 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 1.00 [0.32-3.07] 6/452 6/451 0% improvement FDA (DB RCT) 40% 0.60 [0.35-1.03] symp. case 28/749 23/372 Improvement, RR [CI] Treatment Control 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) Tau​2 = 0.00, I​2 = 0.0%, p = 0.00088 Prophylaxis 51% 0.49 [0.32-0.74] 28/5,923 25/8,457 51% improvement All studies 47% 0.53 [0.34-0.82] 34/6,375 31/8,908 47% improvement 4 tixagevimab/cilgavimab COVID-19 studies c19early.com/tc Jul 2022 Tau​2 = 0.02, I​2 = 10.0%, p = 0.0046 Effect extraction pre-specified(most serious outcome, see appendix) Favors tixagevimab/ci.. Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Montgom.. (DB RCT) 0% death Improvement Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 0% improvement FDA (DB RCT) 40% symp. case Levin (DB RCT) 86% death Young-Xu (PSM) 64% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.00088 Prophylaxis 51% 51% improvement All studies 47% 47% improvement 4 tixagevimab/cilgavimab COVID-19 studies c19early.com/tc Jul 2022 Tau​2 = 0.02, I​2 = 10.0%, p = 0.0046 Effect extraction pre-specifiedRotate device for details Favors tixagevimab/ci.. 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 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, 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 research shows that tixagevimab/cilgavimab is not effective for the omicron BA.2 variant [Zhou].
Results
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, and 8 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, cases, and peer reviewed studies.
0 0.5 1 1.5+ ALL STUDIES MORTALITY HOSPITALIZATION CASES RCTS RCT MORTALITY PEER-REVIEWED All Prophylaxis Early Tixagevimab/cilgavimab for COVID-19 C19EARLY.COM/TC JUL 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 1 1 100% 0% improvement
RR 1.00 [0.32‑3.07]
p = 1.
Prophylaxis 3 3 100% 51% improvement
RR 0.49 [0.32‑0.74]
p = 0.00088
All studies 4 4 100% 47% improvement
RR 0.53 [0.34‑0.82]
p = 0.0046
Table 1. Results by treatment stage.
Studies Early treatment Prophylaxis PatientsAuthors
All studies 40% [-207‑68%]51% [26‑68%] 15,283 55
Peer-reviewed 20% [-207‑68%]86% [-198‑99%] 6,075 44
Randomized Controlled TrialsRCTs 30% [-207‑68%]42% [2‑66%] 7,196 45
Table 2. Results by treatment stage for all studies and with different exclusions.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Montgom.. (DB RCT) 0% 1.00 [0.32-3.07] death 6/452 6/451 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 1.00 [0.32-3.07] 6/452 6/451 0% improvement FDA (DB RCT) 40% 0.60 [0.35-1.03] symp. case 28/749 23/372 Improvement, RR [CI] Treatment Control 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) Tau​2 = 0.00, I​2 = 0.0%, p = 0.00088 Prophylaxis 51% 0.49 [0.32-0.74] 28/5,923 25/8,457 51% improvement All studies 47% 0.53 [0.34-0.82] 34/6,375 31/8,908 47% improvement 4 tixagevimab/cilgavimab COVID-19 studies c19early.com/tc Jul 2022 Tau​2 = 0.02, I​2 = 10.0%, p = 0.0046 Effect extraction pre-specified(most serious outcome, see appendix) Favors tixagevimab/ci.. Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Montgom.. (DB RCT) 0% death Improvement Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 0% improvement FDA (DB RCT) 40% symp. case Levin (DB RCT) 86% death Young-Xu (PSM) 64% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.00088 Prophylaxis 51% 51% improvement All studies 47% 47% improvement 4 tixagevimab/cilgavimab COVID-19 studies c19early.com/tc Jul 2022 Tau​2 = 0.02, I​2 = 10.0%, p = 0.0046 Effect extraction pre-specifiedRotate device for details Favors tixagevimab/ci.. Favors control
Figure 4. 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+ Montgom.. (DB RCT) 0% 1.00 [0.32-3.07] 6/452 6/451 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 1.00 [0.32-3.07] 6/452 6/451 0% improvement Levin (DB RCT) 86% 0.14 [0.01-2.98] 0/3,441 2/1,731 Improvement, RR [CI] Treatment Control Young-Xu (PSM) 64% 0.36 [0.18-0.73] 1,733 (n) 6,354 (n) Tau​2 = 0.00, I​2 = 0.0%, p = 0.0022 Prophylaxis 66% 0.34 [0.17-0.68] 0/5,174 2/8,085 66% improvement All studies 52% 0.48 [0.21-1.09] 6/5,626 8/8,536 52% improvement 3 tixagevimab/cilgavimab COVID-19 mortality results c19early.com/tc Jul 2022 Tau​2 = 0.17, I​2 = 30.1%, p = 0.081 Favors tixagevimab/ci.. Favors control
Figure 5. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Montgom.. (DB RCT) 57% 0.43 [0.25-0.75] hosp. 17/413 40/421 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.003 Early treatment 57% 0.43 [0.25-0.75] 17/413 40/421 57% improvement Young-Xu (PSM) 87% 0.13 [0.02-0.99] hosp. 1,733 (n) 6,354 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.04 Prophylaxis 87% 0.13 [0.02-0.99] 0/1,733 0/6,354 87% improvement All studies 65% 0.35 [0.14-0.87] 17/2,146 40/6,775 65% improvement 2 tixagevimab/cilgavimab COVID-19 hospitalization results c19early.com/tc Jul 2022 Tau​2 = 0.19, I​2 = 26.2%, p = 0.023 Favors tixagevimab/ci.. 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+ FDA (DB RCT) 40% 0.60 [0.35-1.03] symp. case 28/749 23/372 Improvement, RR [CI] Treatment Control Levin (DB RCT) 82% 0.18 [0.09-0.35] symp. case 11/3,441 31/1,731 Young-Xu (PSM) 66% 0.34 [0.13-0.87] cases 1,733 (n) 6,354 (n) Tau​2 = 0.36, I​2 = 73.6%, p = 0.0078 Prophylaxis 66% 0.34 [0.15-0.75] 39/5,923 54/8,457 66% improvement All studies 66% 0.34 [0.15-0.75] 39/5,923 54/8,457 66% improvement 3 tixagevimab/cilgavimab COVID-19 case results c19early.com/tc Jul 2022 Tau​2 = 0.36, I​2 = 73.6%, p = 0.0078 Favors tixagevimab/ci.. Favors control
Figure 7. Random effects meta-analysis for cases.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Montgom.. (DB RCT) 0% 1.00 [0.32-3.07] death 6/452 6/451 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 1.00 [0.32-3.07] 6/452 6/451 0% improvement Levin (DB RCT) 86% 0.14 [0.01-2.98] death 0/3,441 2/1,731 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Prophylaxis 86% 0.14 [0.01-2.98] 0/3,441 2/1,731 86% improvement All studies 36% 0.64 [0.13-3.16] 6/3,893 8/2,182 36% improvement 2 tixagevimab/cilgavimab COVID-19 peer reviewed trials c19early.com/tc Jul 2022 Tau​2 = 0.52, I​2 = 27.5%, p = 0.6 Effect extraction pre-specified(most serious outcome, see appendix) Favors tixagevimab/ci.. Favors control
Figure 8. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during a pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Randomized Controlled Trials (RCTs)
Figure 9 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. Figure 10 and 11 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 3 summarizes the results.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows 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 could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
In summary, 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 example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
Figure 9. 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+ Montgom.. (DB RCT) 0% 1.00 [0.32-3.07] death 6/452 6/451 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 1.00 [0.32-3.07] 6/452 6/451 0% improvement FDA (DB RCT) 40% 0.60 [0.35-1.03] symp. case 28/749 23/372 Improvement, RR [CI] Treatment Control Levin (DB RCT) 86% 0.14 [0.01-2.98] death 0/3,441 2/1,731 Tau​2 = 0.00, I​2 = 0.0%, p = 0.042 Prophylaxis 42% 0.58 [0.34-0.98] 28/4,190 25/2,103 42% improvement All studies 36% 0.64 [0.40-1.03] 34/4,642 31/2,554 36% improvement 3 tixagevimab/cilgavimab COVID-19 Randomized Controlled Trials c19early.com/tc Jul 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.066 Effect extraction pre-specified(most serious outcome, see appendix) Favors tixagevimab/ci.. Favors control
Figure 10. 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+ Montgom.. (DB RCT) 0% 1.00 [0.32-3.07] 6/452 6/451 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 1.00 [0.32-3.07] 6/452 6/451 0% improvement Levin (DB RCT) 86% 0.14 [0.01-2.98] 0/3,441 2/1,731 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Prophylaxis 86% 0.14 [0.01-2.98] 0/3,441 2/1,731 86% improvement All studies 36% 0.64 [0.13-3.16] 6/3,893 8/2,182 36% improvement 2 tixagevimab/cilgavimab COVID-19 RCT mortality results c19early.com/tc Jul 2022 Tau​2 = 0.52, I​2 = 27.5%, p = 0.6 Favors tixagevimab/ci.. Favors control
Figure 11. Random effects meta-analysis for RCT mortality results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 3 3 100% 36% improvement
RR 0.64 [0.40‑1.03]
p = 0.066
RCT mortality results 2 2 100% 36% improvement
RR 0.64 [0.13‑3.16]
p = 0.6
Table 3. 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]. Figure 12 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 42 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 12. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 42 treatments. Early treatment is critical.
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, 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.
The median effect size for retrospective studies is 64% improvement, compared to 40% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 13 shows a scatter plot of results for prospective and retrospective studies.
Figure 13. Prospective vs. retrospective studies.
Funnel plot 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 14 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 14. Example funnel plot analysis for simulated perfect trials.
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
Tixagevimab/cilgavimab is an effective treatment for COVID-19. Statistically significant improvements are seen for hospitalization and cases. 3 studies from 3 independent teams (all from the same country) show statistically significant improvements in isolation (1 for the most serious outcome). Meta analysis using the most serious outcome reported shows 47% [18‑66%] improvement. Results are slightly worse for Randomized Controlled Trials and slightly worse for peer-reviewed studies.
Currently there is limited data, with only 31 control events for the most serious outcome in trials to date.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BA.2 [Zhou]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
Study Notes
0 0.5 1 1.5 2+ Symptomatic case, D180 40% Improvement Relative Risk Symptomatic case 33% c19early.com/tc FDA et al. NCT04625972 Tixagev../c.. for COVID-19 RCT Prophylaxis Favors tixagevimab/ci.. Favors control
[FDA] PEP RCT with 749 tixagevimab/cilgavimab patients and 372 control patients, showing lower risk of symptomatic cases with treatment, without statistical significance. STORM CHASER. NCT04625972.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk Symptomatic case 82% Symptomatic case (b) 76% c19early.com/tc Levin et al. NCT04625725 PROVENT Tixagev../c.. RCT Prophylaxis Favors tixagevimab/ci.. Favors control
[Levin] PrEP RCT with 3,441 tixagevimab/cilgavimab patients and 1,731 control patients, showing lower risk of symptomatic cases with treatment.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk Severe case 50% primary Hospitalization 57% c19early.com/tc Montgomery et al. NCT04723394 Tixagev../c.. RCT EARLY Favors tixagevimab/ci.. Favors control
[Montgomery] RCT 910 outpatients in the USA, 456 treated with tixagevimab/cilgavimab, showing significantly lower combined severe COVID-19/death with treatment.
0 0.5 1 1.5 2+ Mortality 64% Improvement Relative Risk Death/hospitalization/c.. 69% Hospitalization 87% Case 66% c19early.com/tc Young-Xu et al. Tixagev../c.. for COVID-19 Prophylaxis Favors tixagevimab/ci.. Favors control
[Young-Xu] PSM retrospective 1,848 immunocompromised patients given tixagevimab/cilgavimab prophylaxis, showing lower mortality, hospitalization, and cases.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.com. Search terms were tixagevimab, cilgavimab, Evusheld, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of tixagevimab/cilgavimab for COVID-19 that report a comparison with a control group are included in the main analysis. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.9.13) with scipy (1.8.0), pythonmeta (1.26), numpy (1.22.2), statsmodels (0.14.0), and plotly (5.6.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.com/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.
[Montgomery], 6/7/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, North America, peer-reviewed, mean age 46.0, 20 authors, study period 28 January, 2021 - 22 July, 2021, trial NCT04723394. 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 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.
[FDA], 12/8/2021, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, multiple regions, preprint, 1 author, trial NCT04625972. risk of symptomatic case, 39.5% lower, RR 0.60, p = 0.07, treatment 28 of 749 (3.7%), control 23 of 372 (6.2%), NNT 41, from graph, day 180.
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.
[Levin], 4/20/2022, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, multiple regions, peer-reviewed, 24 authors, study period 21 November, 2020 - 22 March, 2021, trial NCT04625725 (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.
[Young-Xu], 5/29/2022, retrospective, propensity score matching, USA, North America, 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.
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. 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. FLCCC and WCH provide treatment protocols.
  or use drag and drop   
Submit