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Sotrovimab for COVID-19: real-time meta analysis of 17 studies
Covid Analysis, June 2023
https://c19early.org/vmeta.html
 
0 0.5 1 1.5+ All studies 16% 17 34,641 Improvement, Studies, Patients Relative Risk Mortality 16% 8 9,904 Ventilation 89% 1 1,057 ICU admission 56% 1 94 Hospitalization 32% 5 8,381 Progression 23% 7 11,476 RCTs 10% 2 1,417 RCT mortality 10% 2 1,417 Peer-reviewed 17% 14 25,029 Early 19% 15 33,861 Late -40% 2 780 Sotrovimab for COVID-19 c19early.org/v Jun 2023 Favorssotrovimab Favorscontrol after exclusions
Meta analysis using the most serious outcome reported shows 16% [-1‑30%] improvement, without reaching statistical significance. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
7 studies from 7 independent teams in 3 different countries show statistically significant improvements in isolation (4 for the most serious outcome).
0 0.5 1 1.5+ All studies 16% 17 34,641 Improvement, Studies, Patients Relative Risk Mortality 16% 8 9,904 Ventilation 89% 1 1,057 ICU admission 56% 1 94 Hospitalization 32% 5 8,381 Progression 23% 7 11,476 RCTs 10% 2 1,417 RCT mortality 10% 2 1,417 Peer-reviewed 17% 14 25,029 Early 19% 15 33,861 Late -40% 2 780 Sotrovimab for COVID-19 c19early.org/v Jun 2023 Favorssotrovimab Favorscontrol after exclusions
Efficacy is variant dependent. In Vitro studies suggest lower efficacy for omicron BA.1 [Liu, Sheward, VanBlargan] and no efficacy for omicron BA.2 [Zhou]. US EUA has been revoked. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are significantly more effective. Only 24% of sotrovimab studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Sotrovimab p=0.065 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org June 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Percentage improvement with sotrovimab (more)
All studies Early treatment Late treatment Studies Patients Authors
All studies16% [-1‑30%]19% [3‑33%]
*
-40% [-214‑38%] 17 34,641 970
Randomized Controlled TrialsRCTs10% [-109‑61%]80% [-316‑99%]-2% [-117‑52%] 2 1,417 715
Mortality16% [-57‑55%]68% [10‑89%]
*
-40% [-214‑38%] 8 9,904 772
HospitalizationHosp.32% [-18‑60%]32% [-18‑60%]- 5 8,381 51
Highlights
Sotrovimab reduces risk for COVID-19 with low confidence for ventilation and in pooled analysis, and very low confidence for ICU admission, hospitalization, and progression. Efficacy is variant dependent. In Vitro studies predict lower efficacy for BA.1 and a lack of efficacy for BA.2. US EUA has been revoked.
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 51 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ COMET-ICE Gupta (DB RCT) 80% 0.20 [0.01-4.16] death 0/528 2/529 Improvement, RR [CI] Treatment Control Ong 61% 0.39 [0.05-2.90] death 1/19 10/75 Aggarwal (PSM) 89% 0.11 [0.00-0.79] death 0/522 15/1,563 Zaqout -165% 2.65 [0.60-11.3] progression 4/345 3/583 Aggarwal 38% 0.62 [0.07-2.77] death 1/1,542 7/3,663 Piccicacco 66% 0.34 [0.01-8.13] death 0/88 1/90 Kneidinger -20% 1.20 [0.64-2.27] severe case 21/125 13/93 Suzuki -8% 1.08 [0.69-1.70] progression 672 (n) 1,257 (n) Brown -258% 3.58 [0.73-17.5] hosp. 6/186 2/222 Zheng 50% 0.50 [0.31-0.81] death/hosp. 34/3,331 61/2,689 OT​1 Zheng (PSW) 4% 0.96 [0.52-1.79] death/hosp. 2,847 (n) 4,836 (n) OT​1 Evans 27% 0.73 [0.55-0.98] death/hosp. 1,079 (n) 4,973 (n) Goodwin 75% 0.25 [0.01-5.17] death 0/169 2/336 Kip 30% 0.70 [0.43-1.12] death/hosp. 22/500 63/999 Tazare 16% 0.84 [0.75-0.93] death/hosp. Tau​2 = 0.03, I​2 = 29.4%, p = 0.022 Early treatment 19% 0.81 [0.67-0.97] 89/11,953 179/21,908 19% improvement TICO Self (DB RCT) -2% 1.02 [0.48-2.17] death 14/182 13/178 Improvement, RR [CI] Treatment Control Woo (PSM) -140% 2.40 [0.78-7.41] death 4/60 10/360 Tau​2 = 0.13, I​2 = 36.1%, p = 0.43 Late treatment -40% 1.40 [0.62-3.14] 18/242 23/538 40% increased risk All studies 16% 0.84 [0.70-1.01] 107/12,195 202/22,446 16% improvement 17 sotrovimab COVID-19 studies c19early.org/v Jun 2023 Tau​2 = 0.03, I​2 = 32.2%, p = 0.065 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors sotrovimab Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ COMET-ICE Gupta (DB RCT) 80% death Relative Risk [CI] Ong 61% death Aggarwal (PSM) 89% death Zaqout -165% progression Aggarwal 38% death Piccicacco 66% death Kneidinger -20% severe case Suzuki -8% progression Brown -258% hospitalization Zheng 50% death/hosp. OT​1 Zheng (PSW) 4% death/hosp. OT​1 Evans 27% death/hosp. Goodwin 75% death Kip 30% death/hosp. Tazare 16% death/hosp. Tau​2 = 0.03, I​2 = 29.4%, p = 0.022 Early treatment 19% 19% improvement TICO Self (DB RCT) -2% death Woo (PSM) -140% death Tau​2 = 0.13, I​2 = 36.1%, p = 0.43 Late treatment -40% 40% increased risk All studies 16% 16% improvement 17 sotrovimab COVID-19 studies c19early.org/v Jun 2023 Tau​2 = 0.03, I​2 = 32.2%, p = 0.065 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors sotrovimab Favors control
B
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D
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. 0.9% of 3,946 proposed treatments show efficacy [c19early.org]. D. Timeline of results in sotrovimab studies.
We analyze all significant studies concerning the use of sotrovimab 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 after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Efficacy is variant dependent, for example in vitro research suggests that sotrovimab is not effective for omicron BA.2 [Zhou].
Table 1. Predicted efficacy by variant from [Davis].    : 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
Table 2 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 3 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, and 10 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, and peer reviewed studies.
Table 2. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01.
Improvement Studies Patients Authors
All studies16% [-1‑30%]17 34,641 970
After exclusions18% [2‑32%]
*
15 26,550 944
Peer-reviewed studiesPeer-reviewed17% [-12‑39%]14 25,029 877
Randomized Controlled TrialsRCTs10% [-109‑61%]2 1,417 715
Mortality16% [-57‑55%]8 9,904 772
HospitalizationHosp.32% [-18‑60%]5 8,381 51
RCT mortality10% [-109‑61%]2 1,417 715
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.
Early treatment Late treatment
All studies19% [3‑33%]
*
-40% [-214‑38%]
After exclusions22% [6‑35%]
**
-40% [-214‑38%]
Peer-reviewed studiesPeer-reviewed26% [-1‑46%]-40% [-214‑38%]
Randomized Controlled TrialsRCTs80% [-316‑99%]-2% [-117‑52%]
Mortality68% [10‑89%]
*
-40% [-214‑38%]
HospitalizationHosp.32% [-18‑60%]-
RCT mortality80% [-316‑99%]-2% [-117‑52%]
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] 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. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 11 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 39% improvement, compared to 27% for other studies. Figure 12 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 2 and Table 3.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases [Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. 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, 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.
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 51 treatments we have analyzed, 64% 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 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].
Currently, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 36 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 13 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 9 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is 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.
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Figure 11. Results for RCTs and non-RCT studies.
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Figure 12. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 13 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Brown], unadjusted results with no group details; significant unadjusted confounding possible.
[Zheng], study compares against another treatment showing significant efficacy.
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Figure 13. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and [Kumar] report only 2.5 hours improvement for inpatient treatment.
Table 4. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Figure 14 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 14. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 15. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 97% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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Figure 15. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For sotrovimab, 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 16 shows a scatter plot of results for prospective and retrospective studies. 40% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 50% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 27% improvement, compared to 39% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 16. 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 17 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 17. 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 by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
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 affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
2 of the 17 studies compare against other treatments, which may reduce the effect seen.
Meta analysis using the most serious outcome reported shows 16% [-1‑30%] improvement, without reaching statistical significance. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment. 7 studies from 7 independent teams in 3 different countries show statistically significant improvements in isolation (4 for the most serious outcome).
Efficacy is variant dependent. In Vitro studies suggest lower efficacy for omicron BA.1 [Liu, Sheward, VanBlargan] and no efficacy for omicron BA.2 [Zhou]. US EUA has been revoked. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
0 0.5 1 1.5 2+ Mortality 38% Improvement Relative Risk Hospitalization 18% primary Progression -3% c19early.org/v Aggarwal et al. Sotrovimab for COVID-19 EARLY Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 30,247 patients in the USA (December 2021 - March 2022) Lower hospitalization with sotrovimab (not stat. sig., p=0.32) Aggarwal et al., Int. J. Infectious Diseases, doi:10.1016/j.ijid.2022.10.002 Favors sotrovimab Favors control
[Aggarwal] Retrospective 30,247 outpatients in the USA, showing no significant differences with sotrovimab with omicron BA.1.
0 0.5 1 1.5 2+ Mortality 89% Improvement Relative Risk Hospitalization 62% primary ED visit -11% c19early.org/v Aggarwal et al. Sotrovimab for COVID-19 EARLY Is early treatment with sotrovimab beneficial for COVID-19? PSM retrospective 10,036 patients in the USA (Oct - Dec 2021) Lower mortality (p=0.048) and hospitalization (p=0.0021) Aggarwal et al., The J. Infectious Diseases, doi:10.1093/infdis/jiac206 Favors sotrovimab Favors control
[Aggarwal (B)] PSM retrospective 10,036 outpatients, 522 treated with sotrovimab, showing lower mortality and hospitalization with treatment.

Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to receive adjuvant treatments that are not tracked in the data (e.g., nasal/oral hygiene [c19early.org (B), c19early.org (C)], vitamin D [c19early.org (D)], etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. Therefore, these kind of studies may overestimate the efficacy of treatments.
0 0.5 1 1.5 2+ Hospitalization -258% Improvement Relative Risk c19early.org/v Brown et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 408 patients in the United Kingdom Higher hospitalization with sotrovimab (not stat. sig., p=0.15) Brown et al., Open Forum Infectious Diseases, doi:10.1093/ofid/ofac527 Favors sotrovimab Favors control
[Brown] Retrospective 186 patients in the UK treated with sotrovimab, and 222 eligible but declining treatment, showing no significant difference in hospitalization. No group details are provided and the results are subject to confounding by indication.
0 0.5 1 1.5 2+ Death/hospitalization 27% Improvement Relative Risk c19early.org/v Evans et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 6,052 patients in the United Kingdom (Dec 2021 - Apr 2022) Lower death/hosp. with sotrovimab (p=0.032) Evans et al., J. Infection, doi:10.1016/j.jinf.2023.02.012 Favors sotrovimab Favors control
[Evans] Retrospective high risk outpatients in the UK, showing lower hospitalization/death with sotrovimab treatment. Residual confounding is likely with adjustments having no detail on specific comorbidities.
0 0.5 1 1.5 2+ Mortality 75% Improvement Relative Risk Hospitalization, COVID-19 60% Hospitalization, all cause -21% c19early.org/v Goodwin et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 505 patients in the United Kingdom (Dec 2021 - Feb 2022) Lower mortality (p=0.55) and hospitalization (p=0.35), not stat. sig. Goodwin et al., PLOS ONE, doi:10.1371/journal.pone.0281915 Favors sotrovimab Favors control
[Goodwin] Retrospective 604 outpatients in the UK, showing lower risk of hospitalization with sotrovimab treatment, without statistical significance due to the small number of hospitalizations.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk Ventilation 89% Progression 75% Hospitalization >24hrs or.. 79% primary c19early.org/v Gupta et al. NCT04545060 COMET-ICE Sotrovimab RCT EARLY Is early treatment with sotrovimab beneficial for COVID-19? Double-blind RCT 1,057 patients in multiple countries Lower progression (p=0.00041) and death/hosp. (p=0.00039) Gupta et al., JAMA, doi:10.1001/jama.2022.2832 Favors sotrovimab Favors control
[Gupta] RCT 1,057 outpatients, 529 treated with sotrovimab, showing significantly lower hospitalization >24h or mortality with treatment.
0 0.5 1 1.5 2+ Death/hospitalization 30% Improvement Relative Risk c19early.org/v Kip et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 2,571 patients in the USA (December 2020 - August 2022) Lower death/hosp. with sotrovimab (not stat. sig., p=0.14) Kip et al., Annals of Internal Medicine, doi:10.7326/M22-1286 Favors sotrovimab Favors control
[Kip] Retrospective 2,571 patients treated with mAbs in the USA, and 5,135 control patients, showing lower combined mortality/hospitalization for bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, sotrovimab, and bebtelovimab, with statistical significance only for casirivimab/imdevimab.

Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to receive adjuvant treatments that are not tracked in the data (e.g., nasal/oral hygiene [c19early.org (B), c19early.org (C)], vitamin D [c19early.org (D)], etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. Therefore, these kind of studies may overestimate the efficacy of treatments.
0 0.5 1 1.5 2+ Severe case -20% Improvement Relative Risk c19early.org/v Kneidinger et al. Sotrovimab for COVID-19 EARLY Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 218 patients in Germany (January - March 2022) No significant difference in severe cases Kneidinger et al., Infection, doi:10.1007/s15010-022-01914-8 Favors sotrovimab Favors control
[Kneidinger] Retrospective 218 COVID+ lung transplant patients in Germany, showing no significant difference in severe cases with early sotrovimab use.
0 0.5 1 1.5 2+ Mortality 61% Improvement Relative Risk ICU admission 56% Progression 59% c19early.org/v Ong et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 94 patients in Singapore Lower progression with sotrovimab (p=0.047) Ong et al., Antibiotics, doi:10.3390/antibiotics11030345 Favors sotrovimab Favors control
[Ong] Retrospective 19 sotrovimab patients and 75 controls is Singapore, showing lower progression with treatment.
0 0.5 1 1.5 2+ Mortality 66% Improvement Relative Risk Hospitalization 35% Hospitalization/ER 66% Progression, ER visit 90% c19early.org/v Piccicacco et al. Sotrovimab for COVID-19 EARLY Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 178 patients in the USA (December 2021 - February 2022) Fewer hosp./ER visits (p=0.012) and lower progression (p=0.0095) Piccicacco et al., J. Antimicrobial Chemotherapy, doi:10.1093/jac/dkac256 Favors sotrovimab Favors control
[Piccicacco] Retrospective high-risk outpatients in the USA, 82 treated with remdesivir, 88 with sotrovimab, and 90 control patients, showing significantly lower combined hospitalization/ER visits with both treatments in unadjusted results. The dominant variant was omicron B.1.1.529.
0 0.5 1 1.5 2+ Mortality -2% Improvement Relative Risk Recovery 11% primary Recovery (b) 7% c19early.org/v Self et al. NCT04501978 TICO Sotrovimab RCT LATE Is late treatment with sotrovimab beneficial for COVID-19? Double-blind RCT 360 patients in multiple countries (Dec 2020 - Mar 2021) Improved recovery with sotrovimab (not stat. sig., p=0.29) Self et al., The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00751-9 Favors sotrovimab Favors control
[Self] RCT with 182 sotrovimab patients and 178 control patients, median 8 days from symptom onset, showing no significant differences and terminated early due to futility.
0 0.5 1 1.5 2+ Progression -8% Improvement Relative Risk c19early.org/v Suzuki et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 1,929 patients in Japan No significant difference in progression Suzuki et al., Research Square, doi:10.21203/rs.3.rs-2118653/v1 Favors sotrovimab Favors control
[Suzuki] Retrospective 1,921 patients in Japan, showing no significant difference in progression with sotrovimab use.
0 0.5 1 1.5 2+ Death/hospitalization 16% Improvement Relative Risk c19early.org/v Tazare et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 71,976 patients in the United Kingdom (Dec 2021 - May 2022) Lower death/hosp. with sotrovimab (p=0.0015) Tazare et al., medRxiv, doi:10.1101/2023.05.12.23289914 Favors sotrovimab Favors control
[Tazare] OpenSAFELY retrospective 75,048 outpatients in the UK, using the clone-censor-weight approach to address immortal time bias, showing lower combined mortality/hospitalization with sotrovimab treatment.
0 0.5 1 1.5 2+ Mortality -140% Improvement Relative Risk Mortality (b) -50% c19early.org/v Woo et al. Sotrovimab for COVID-19 LATE TREATMENT Is late treatment with sotrovimab beneficial for COVID-19? PSM retrospective 420 patients in Germany Higher mortality with sotrovimab (not stat. sig., p=0.12) Woo et al., Microbiology Spectrum, doi:10.1128/spectrum.04103-22 Favors sotrovimab Favors control
[Woo] PSM retrospective 1,254 hospitalized patients in Germany, 147 treated with sotrovimab, showing higher mortality with sotrovimab, without statistical significance.
0 0.5 1 1.5 2+ Progression -165% Improvement Relative Risk c19early.org/v Zaqout et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 928 patients in Qatar (October 2021 - February 2022) Higher progression with sotrovimab (not stat. sig., p=0.19) Zaqout et al., Int. J. Infectious Diseases, doi:10.1016/j.ijid.2022.09.023 Favors sotrovimab Favors control
[Zaqout] Retrospective 345 sotrovimab treated patients in Qatar matched with 583 patients that opted not to receive treatment, showing higher progression with treatment, without statistical significance.
0 0.5 1 1.5 2+ Death/hospitalization, d.. 4% Improvement Relative Risk Death/hospitalizatio.. (b) -14% c19early.org/v Zheng et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 7,683 patients in the United Kingdom (Feb - Oct 2022) Study compares with paxlovid, results vs. placebo may differ No significant difference in death/hosp. Zheng et al., medRxiv, doi:10.1101/2023.01.20.23284849 Favors sotrovimab Favors paxlovid
[Zheng] OpenSAFELY retrospective 7,683 outpatients in the UK, showing no significant difference in hospitalization/death between paxlovid and sotrovimab.
0 0.5 1 1.5 2+ Death/hospitalization, d.. 50% Improvement Relative Risk Death/hospitalizatio.. (b) 46% c19early.org/v Zheng et al. Sotrovimab for COVID-19 EARLY TREATMENT Is early treatment with sotrovimab beneficial for COVID-19? Retrospective 6,020 patients in the United Kingdom (Dec 2021 - Feb 2022) Study compares with molnupiravir, results vs. placebo may differ Lower death/hosp. with sotrovimab (p=0.0047) Zheng et al., BMJ, doi:10.1136/bmj-2022-071932 Favors sotrovimab Favors molnupiravir
[Zheng (B)] Retrospective 3,331 sotrovimab and 2,689 molnupiravir patients in the UK, showing lower risk of combined hospitalization/death with sotrovimab.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were sotrovimab, 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 sotrovimab for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.11.3) with scipy (1.10.1), pythonmeta (1.26), numpy (1.24.3), statsmodels (0.14.0), and plotly (5.14.1).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/vmeta.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.
[Aggarwal], 6/18/2022, retrospective, USA, peer-reviewed, 10 authors, study period 26 December, 2021 - 10 March, 2022. risk of death, 38.0% lower, RR 0.62, p = 0.62, treatment 1 of 1,542 (0.1%), control 7 of 3,663 (0.2%), odds ratio converted to relative risk.
risk of hospitalization, 17.5% lower, RR 0.82, p = 0.32, treatment 39 of 1,542 (2.5%), control 116 of 3,663 (3.2%), NNT 157, odds ratio converted to relative risk, primary outcome.
risk of progression, 2.8% higher, RR 1.03, p = 0.83, treatment 93 of 1,542 (6.0%), control 224 of 3,663 (6.1%), NNT 1189, odds ratio converted to relative risk, ED visit.
[Aggarwal (B)], 4/5/2022, retrospective, USA, peer-reviewed, 14 authors, study period 1 October, 2021 - 11 December, 2021. risk of death, 88.9% lower, RR 0.11, p = 0.048, treatment 0 of 522 (0.0%), control 15 of 1,563 (1.0%), NNT 104, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28.
risk of hospitalization, 61.6% lower, RR 0.38, p = 0.002, treatment 11 of 522 (2.1%), control 89 of 1,563 (5.7%), NNT 28, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28, primary outcome.
ED visit, 11.0% higher, RR 1.11, p = 0.55, treatment 44 of 522 (8.4%), control 119 of 1,563 (7.6%), adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28.
[Brown], 10/6/2022, retrospective, United Kingdom, peer-reviewed, 17 authors, excluded in exclusion analyses: unadjusted results with no group details; significant unadjusted confounding possible. risk of hospitalization, 258.1% higher, RR 3.58, p = 0.15, treatment 6 of 186 (3.2%), control 2 of 222 (0.9%).
[Evans], 1/25/2023, retrospective, United Kingdom, peer-reviewed, 11 authors, study period 16 December, 2021 - 22 April, 2022. risk of death/hospitalization, 27.0% lower, HR 0.73, p = 0.03, treatment 1,079, control 4,973, Cox proportional hazards.
[Goodwin], 3/15/2023, retrospective, United Kingdom, peer-reviewed, 3 authors, study period 22 December, 2021 - 20 February, 2022. risk of death, 75.0% lower, RR 0.25, p = 0.55, treatment 0 of 169 (0.0%), control 2 of 336 (0.6%), NNT 168, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 60.2% lower, RR 0.40, p = 0.35, treatment 2 of 169 (1.2%), control 10 of 336 (3.0%), NNT 56, COVID-19 related.
risk of hospitalization, 21.5% higher, RR 1.21, p = 0.69, treatment 11 of 169 (6.5%), control 18 of 336 (5.4%), all cause.
[Gupta], 12/4/2021, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 68 authors, average treatment delay 2.6 days, trial NCT04545060 (history) (COMET-ICE), conflicts of interest: research funding from the drug patent holder, employee of the drug patent holder. risk of death, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 528 (0.0%), control 2 of 529 (0.4%), NNT 264, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29.
risk of mechanical ventilation, 88.9% lower, RR 0.11, p = 0.12, treatment 0 of 528 (0.0%), control 4 of 529 (0.8%), NNT 132, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29.
risk of progression, 75.0% lower, RR 0.25, p < 0.001, treatment 7 of 528 (1.3%), control 28 of 529 (5.3%), NNT 25, day 29.
risk of hospitalization >24hrs or death, 79.0% lower, RR 0.21, p < 0.001, treatment 6 of 528 (1.1%), control 30 of 529 (5.7%), NNT 22, day 29, ITT, primary outcome.
[Kip], 4/4/2023, retrospective, USA, peer-reviewed, 16 authors, study period 8 December, 2020 - 31 August, 2022. risk of death/hospitalization, 30.0% lower, RR 0.70, p = 0.14, treatment 22 of 500 (4.4%), control 63 of 999 (6.3%), NNT 52, delta and omicron variants, day 28.
[Kneidinger], 9/9/2022, retrospective, Germany, peer-reviewed, 11 authors, study period 1 January, 2022 - 20 March, 2022, lung transplant patients. risk of severe case, 20.2% higher, RR 1.20, p = 0.79, treatment 21 of 125 (16.8%), control 13 of 93 (14.0%).
[Ong], 3/5/2022, retrospective, Singapore, peer-reviewed, 10 authors, average treatment delay 2.0 days. risk of death, 60.5% lower, RR 0.39, p = 0.45, treatment 1 of 19 (5.3%), control 10 of 75 (13.3%), NNT 12.
risk of ICU admission, 56.1% lower, RR 0.44, p = 0.35, treatment 2 of 19 (10.5%), control 18 of 75 (24.0%), NNT 7.4.
risk of progression, 59.0% lower, HR 0.41, p = 0.047, treatment 19, control 75, Cox proportional hazards.
[Piccicacco], 8/1/2022, retrospective, USA, peer-reviewed, 7 authors, study period 27 December, 2021 - 4 February, 2022, average treatment delay 4.4 days. risk of death, 66.4% lower, RR 0.34, p = 1.00, treatment 0 of 88 (0.0%), control 1 of 90 (1.1%), NNT 90, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29.
risk of hospitalization, 34.9% lower, RR 0.65, p = 0.46, treatment 7 of 88 (8.0%), control 11 of 90 (12.2%), NNT 23, day 29.
risk of hospitalization/ER, 66.3% lower, RR 0.34, p = 0.01, treatment 7 of 88 (8.0%), control 21 of 90 (23.3%), NNT 6.5, odds ratio converted to relative risk, day 29.
risk of progression, 89.8% lower, RR 0.10, p = 0.009, treatment 1 of 88 (1.1%), control 10 of 90 (11.1%), NNT 10, ER visit, day 29.
[Suzuki], 10/5/2022, retrospective, Japan, preprint, 53 authors. risk of progression, 8.3% higher, OR 1.08, p = 0.73, treatment 672, control 1,257, adjusted per study, multivariable, RR approximated with OR.
[Tazare], 5/16/2023, retrospective, United Kingdom, preprint, 31 authors, study period 16 December, 2021 - 21 May, 2022. risk of death/hospitalization, 16.0% lower, HR 0.84, p = 0.002, treatment 6,408, control 65,568.
[Zaqout], 4/21/2022, retrospective, Qatar, peer-reviewed, median age 40.0, 17 authors, study period 20 October, 2021 - 28 February, 2022. risk of progression, 164.7% higher, RR 2.65, p = 0.19, treatment 4 of 345 (1.2%), control 3 of 583 (0.5%), adjusted per study, odds ratio converted to relative risk, progression to severe/critical disease or mortality.
[Zheng], 1/22/2023, retrospective, United Kingdom, preprint, mean age 54.3, 9 authors, study period 11 February, 2022 - 1 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/hospitalization, 3.8% lower, HR 0.96, p = 0.91, treatment 2,847, control 4,836, inverted to make HR<1 favor treatment, COVID-19 related, propensity score weighting, Cox proportional hazards, day 60, model 4.
risk of death/hospitalization, 13.6% higher, HR 1.14, p = 0.70, treatment 19 of 2,847 (0.7%), control 33 of 4,836 (0.7%), inverted to make HR<1 favor treatment, COVID-19 related, propensity score weighting, Cox proportional hazards, day 28, model 4.
[Zheng (B)], 11/16/2022, retrospective, United Kingdom, peer-reviewed, mean age 52.0, 33 authors, study period 16 December, 2021 - 10 February, 2022, this trial compares with another treatment - results may be better when compared to placebo. risk of death/hospitalization, 50.0% lower, HR 0.50, p = 0.005, treatment 34 of 3,331 (1.0%), control 61 of 2,689 (2.3%), NNT 80, adjusted per study, multivariable, Cox proportional hazards, day 60, model 4.
risk of death/hospitalization, 46.0% lower, HR 0.54, p = 0.01, treatment 32 of 3,331 (1.0%), control 55 of 2,689 (2.0%), NNT 92, adjusted per study, multivariable, Cox proportional hazards, day 28, model 4.
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.
[Self], 12/23/2021, Double Blind Randomized Controlled Trial, multiple countries, peer-reviewed, 647 authors, study period 16 December, 2020 - 1 March, 2021, average treatment delay 8.0 days, trial NCT04501978 (history) (TICO). risk of death, 2.0% higher, RR 1.02, p = 0.96, treatment 14 of 182 (7.7%), control 13 of 178 (7.3%), day 90.
risk of no recovery, 10.7% lower, RR 0.89, p = 0.29, treatment 22 of 160 (13.8%), control 27 of 178 (15.2%), NNT 70, inverted to make RR<1 favor treatment, day 90, primary outcome.
risk of no recovery, 7.4% lower, RR 0.93, p = 0.69, treatment 160, control 178, inverted to make RR<1 favor treatment, pulmonary-plus ordinal outcome @day 5.
[Woo], 12/8/2022, retrospective, Germany, peer-reviewed, 13 authors. risk of death, 140.0% higher, RR 2.40, p = 0.12, treatment 4 of 60 (6.7%), control 10 of 360 (2.8%), non-ICU, propensity score matching.
risk of death, 50.0% higher, RR 1.50, p = 0.08, treatment 36 of 87 (41.4%), control 24 of 87 (27.6%), ICU, propensity score matching.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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