Covid Analysis, February 2023
•Meta analysis using the most serious outcome reported shows 24% [-20‑52%] improvement, without reaching statistical significance. Results are worse for Randomized Controlled Trials, similar after exclusions, and worse 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 US EUA has been revoked.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].
•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 more effective. Only 20% of sotrovimab studies show zero events with treatment.
•All data to reproduce this paper and sources are in the appendix.
|All studies||Early treatment||Late treatment||Studies||Patients||Authors|
|All studies||24% [-20‑52%]||33% [-11‑59%]||-40% [-214‑38%]||15||32,637||933|
|Randomized Controlled TrialsRCTs||10% [-109‑61%]||80% [-316‑99%]||-2% [-117‑52%]||2||1,417||715|
|Mortality||56% [-34‑85%]||85% [76‑91%]|
|HospitalizationHosp.||39% [-2‑64%]||39% [-2‑64%]||-||5||7,876||61|
Sotrovimab reduces risk for COVID-19 with low confidence for ventilation and hospitalization, and very low confidence for mortality, ICU admission, progression, and in pooled analysis. 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 US EUA has been revoked.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.
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.
Efficacy is variant dependent, for example in vitro research suggests that sotrovimab is not effective for omicron BA.2 [Zhou].
|Bamlanivimab/ etesevimab||Casirivimab/ imdevimab||Sotrovimab||Bebtelovimab||Tixagevimab/ cilgavimab|
|Beta/ Gamma BA1.351/ P.1|
|Omicron BA.1/ BA.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.
|All studies||24% [-20‑52%]||15||32,637||933|
|After exclusions||32% [-12‑59%]||13||24,546||907|
|Peer-reviewed studiesPeer-reviewed||-2% [-70‑39%]||10||14,888||833|
|Randomized Controlled TrialsRCTs||10% [-109‑61%]||2||1,417||715|
|RCT mortality||10% [-109‑61%]||2||1,417||715|
|Early treatment||Late treatment|
|All studies||33% [-11‑59%]||-40% [-214‑38%]|
|After exclusions||43% [0‑67%]|
|Peer-reviewed studiesPeer-reviewed||11% [-67‑52%]||-40% [-214‑38%]|
|Randomized Controlled TrialsRCTs||80% [-316‑99%]||-2% [-117‑52%]|
|RCT mortality||80% [-316‑99%]||-2% [-117‑52%]|
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.
[Jadad]. For example, 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.
[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].
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.
Heterogeneity in COVID-19 studies arises from many factors including:
[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.
|Post exposure prophylaxis||86% 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 48 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
[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].
[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.
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.
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.
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.
46% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 50% of prospective studies, showing similar 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. Figure 16 shows a scatter plot of results for prospective and retrospective studies.
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.
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.
Meta analysis using the most serious outcome reported shows 24% [-20‑52%] improvement, without reaching statistical significance. Results are worse for Randomized Controlled Trials, similar after exclusions, and worse 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].
[Aggarwal] Retrospective 30,247 outpatients in the USA, showing no significant differences with sotrovimab with omicron BA.1.
[Aggarwal (B)] Retrospective 522 sotrovimab patients and matched controls in the USA, showing significantly lower hospitalization and mortality with treatment.
[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.
[Cheng] Retrospective 1,530,501 high-risk patients in the USA, 15,633 treated with sotrovimab, showing significantly lower mortality and hospitalization with treatment. Sotrovimab maintained efficacy throughout the period analyzed - September 2021 to April 2022.
[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.
[Gupta] RCT 1,057 outpatients, 529 treated with sotrovimab, showing significantly lower hospitalization >24h or mortality with treatment.
[Kneidinger] Retrospective 218 COVID+ lung transplant patients in Germany, showing no significant difference in severe cases with early sotrovimab use.
[Ong] Retrospective 19 sotrovimab patients and 75 controls is Singapore, showing lower progression with treatment.
[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.
[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.
[Suzuki] Retrospective 1,921 patients in Japan, showing no significant difference in progression with sotrovimab use.
[Woo] PSM retrospective 1,254 hospitalized patients in Germany, 147 treated with sotrovimab, showing higher mortality with sotrovimab, without statistical significance.
[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.
[Zheng] OpenSAFELY retrospective 7,683 outpatients in the UK, showing no significant difference in hospitalization/death between paxlovid and sotrovimab.
[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 (3.10.9) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.5), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at 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, preprint, 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.|
|[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%).|
|[Cheng], 9/11/2022, retrospective, USA, preprint, 13 authors, study period 1 September, 2021 - 30 April, 2022.||risk of death, 88.0% lower, RR 0.12, p < 0.001, NNT 219, adjusted per study, propensity score matching, multivariable.|
|risk of hospitalization, 61.0% lower, RR 0.39, p < 0.001, NNT 35, adjusted per study, propensity score matching, multivariable.|
|[Evans], 1/25/2023, retrospective, United Kingdom, preprint, 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.|
|[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.|
|[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.|
|[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.|
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