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Ensovibep for COVID-19: real-time meta analysis of 2 studies
Covid Analysis, December 2022
https://c19early.org/evmeta.html
 
0 0.5 1 1.5+ All studies 46% 2 885 Improvement, Studies, Patients Relative Risk Mortality 46% 2 885 Hospitalization 87% 1 400 RCTs 46% 2 885 RCT mortality 46% 2 885 Peer-reviewed 17% 1 485 Early 89% 1 400 Late 17% 1 485 Ensovibep for COVID-19 c19early.org/ev Dec 2022 Favorsensovibep Favorscontrol
Statistically significant improvement is seen for hospitalization. One study shows statistically significant improvement in isolation (not for the most serious outcome).
Meta analysis using the most serious outcome reported shows 46% [-173‑89%] improvement, without reaching statistical significance. Results are worse for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.
0 0.5 1 1.5+ All studies 46% 2 885 Improvement, Studies, Patients Relative Risk Mortality 46% 2 885 Hospitalization 87% 1 400 RCTs 46% 2 885 RCT mortality 46% 2 885 Peer-reviewed 17% 1 485 Early 89% 1 400 Late 17% 1 485 Ensovibep for COVID-19 c19early.org/ev Dec 2022 Favorsensovibep Favorscontrol
Currently there is limited data, with only 885 patients and only 37 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
Ensovibep requires IV infusion, but may be less variant dependent than monoclonal antibodies.
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 may be more effective. Only 50% of ensovibep studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Highlights
Ensovibep reduces risk for COVID-19 with low confidence for hospitalization.
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 47 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Novartis (RCT) 89% 0.11 [0.01-2.27] death 0/301 2/99 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.15 Early treatment 89% 0.11 [0.01-2.27] 0/301 2/99 89% improvement Barkauskas (DB RCT) 17% 0.83 [0.51-1.35] death 30/247 35/238 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.43 Late treatment 17% 0.83 [0.51-1.35] 30/247 35/238 17% improvement All studies 46% 0.54 [0.11-2.73] 30/548 37/337 46% improvement 2 ensovibep COVID-19 studies c19early.org/ev Dec 2022 Tau​2 = 0.82, I​2 = 40.2%, p = 0.46 Effect extraction pre-specified(most serious outcome, see appendix) Favors ensovibep Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Novartis (RCT) 89% death Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.15 Early treatment 89% 89% improvement Barkaus.. (DB RCT) 17% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.43 Late treatment 17% 17% improvement All studies 46% 46% improvement 2 ensovibep COVID-19 studies c19early.org/ev Dec 2022 Tau​2 = 0.82, I​2 = 40.2%, p = 0.46 Effect extraction pre-specifiedRotate device for details Favors ensovibep Favors control
B
0 0.25 0.5 0.75 1 1.25 1.5+ All studies Late treatment Early treatment Efficacy in COVID-19 ensovibep studies (pooled effects) Favors ensovibep Favors control c19early.org/ev Dec 2022
C
0 0.25 0.5 0.75 1 1.25 1.5+ Cannabidiol Acetaminophen Conv. Plasma Ibuprofen Aspirin Remdesivir Molnupiravir Vitamin C HCQ Metformin Zinc Fluvoxamine Sotrovimab Vitamin D Paxlovid Melatonin Ensovibep PVP-I REGEN-COV Ivermectin Quercetin Efficacy in COVID-19 studies (pooled effects) Lower risk Increased risk c19early.org/ev Dec 2022
D
-100% -50% 0% 50% 100% Timeline of COVID-19 ensovibep studies (pooled effects) 2020 2021 2022 Favorsensovibep Favorscontrol c19early.org/ev Dec 2022
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. D. Timeline of results in ensovibep studies.
We analyze all significant studies concerning the use of ensovibep 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.
An In Vitro study supports the efficacy of ensovibep [Rothenberger].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, and 7 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, recovery, and peer reviewed studies.
Improvement Studies Patients Authors
All studies46% [-173‑89%]2 885 81
Peer-reviewed studiesPeer-reviewed17% [-31‑47%]1 485 80
Randomized Controlled TrialsRCTs46% [-173‑89%]2 885 81
Mortality46% [-173‑89%]2 885 81
RCT mortality46% [-173‑89%]2 885 81
Table 1. 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.
Early treatment Late treatment
All studies89% [-127‑99%] 117% [-35‑49%] 1
Peer-reviewed studiesPeer-reviewed-17% [-35‑49%] 1
Randomized Controlled TrialsRCTs89% [-127‑99%] 117% [-35‑49%] 1
Mortality89% [-127‑99%] 117% [-35‑49%] 1
RCT mortality89% [-127‑99%] 117% [-35‑49%] 1
Table 2. 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.
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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 hospitalization.
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Figure 6. Random effects meta-analysis for recovery.
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Figure 7. 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 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.
Currently all studies are RCTs.
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.
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]
Table 3. Early treatment is more effective for baloxavir and influenza.
Figure 8 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 8. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 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 9. 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.
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Figure 9. 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 ensovibep, there is currently not enough data to evaluate publication bias with high confidence.
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 10 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 10. Example funnel plot analysis for simulated perfect trials.
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.
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.
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.
Statistically significant improvement is seen for hospitalization. One study shows statistically significant improvement in isolation (not for the most serious outcome). Meta analysis using the most serious outcome reported shows 46% [-173‑89%] improvement, without reaching statistical significance. Results are worse for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.
Currently there is limited data, with only 885 patients and only 37 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
Ensovibep requires IV infusion, but may be less variant dependent than monoclonal antibodies.
0 0.5 1 1.5 2+ Mortality 17% Improvement Relative Risk Recovery 6% Recovery, day 5 -8% Discharge 7% c19early.org/ev Barkauskas et al. NCT04501978 ACTIV-3/TICO Ensovibep RCT LATE Favors ensovibep Favors control
[Barkauskas] RCT 485 hospitalized patients showing no significant differences with ensovibep treatment. Intravenous ensovibep, 600mg.
0 0.5 1 1.5 2+ Mortality 89% Improvement Relative Risk Hospitalization 87% Hospitalization/ER 78% c19early.org/ev Novartis et al. Ensovibep for COVID-19 RCT EARLY TREATMENT Favors ensovibep Favors control
[Novartis] EMPATHY Part A RCT with 407 patients, 301 treated with ensovibep, showing statistically significant viral load reduction (details not provided), and lower mortality and hospitalization. For discussion see [twitter.com].
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 ensovibep, 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 ensovibep 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 (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), 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 the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/evmeta.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.
[Novartis], 1/10/2022, Randomized Controlled Trial, multiple countries, preprint, 1 author. risk of death, 89.0% lower, RR 0.11, p = 0.06, treatment 0 of 301 (0.0%), control 2 of 99 (2.0%), NNT 49, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 86.8% lower, RR 0.13, p = 0.01, treatment 2 of 301 (0.7%), control 5 of 99 (5.1%), NNT 23.
risk of hospitalization/ER, 78.1% lower, RR 0.22, p = 0.02, treatment 4 of 301 (1.3%), control 6 of 99 (6.1%), NNT 21.
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.
[Barkauskas], 8/9/2022, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 80 authors, average treatment delay 8.0 days, trial NCT04501978 (history) (ACTIV-3/TICO). risk of death, 17.0% lower, HR 0.83, p = 0.46, treatment 30 of 247 (12.1%), control 35 of 238 (14.7%), NNT 39, Kaplan–Meier, day 90.
risk of no recovery, 5.7% lower, HR 0.94, p = 0.55, treatment 44 of 247 (17.8%), control 48 of 238 (20.2%), NNT 42, adjusted per study, inverted to make HR<1 favor treatment.
risk of no recovery, 7.5% higher, HR 1.08, p = 0.68, treatment 247, control 238, adjusted per study, inverted to make HR<1 favor treatment, pulmonary ordinal outcome, day 5.
risk of no hospital discharge, 6.5% lower, HR 0.93, p = 0.46, treatment 28 of 247 (11.3%), control 34 of 238 (14.3%), adjusted per study, inverted to make HR<1 favor treatment.
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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