Top
Introduction
Preclinical
Results
RCTs
Exclusions
Heterogeneity
Efficacy decreases with longer..
Discussion
Conclusion
Study Notes
Methods and Data
Supplementary
References

All studies
Mortality
Ventilation
ICU admission
Hospitalization
Progression
Recovery
Viral clearance
Peer reviewed
Exclusions
All RCTs
RCT mortality

Feedback
Home
Show Outline
Top   Intro   Preclinical   Results   RCT   Exc.   Heterogeneity   Followup   Discussion   Conclusion   StudyNotes   Appendix   SupplementarySupp.   ReferencesRef.
Home   COVID-19 treatment studies for Remdesivir  COVID-19 treatment studies for Remdesivir  C19 studies: Remdesivir  Remdesivir   Select treatmentSelect treatmentTreatmentsTreatments
Alkalinization Meta Lactoferrin Meta
Melatonin Meta
Bromhexine Meta Metformin Meta
Budesonide Meta Molnupiravir Meta
Cannabidiol Meta
Colchicine Meta Nigella Sativa Meta
Conv. Plasma Meta Nitazoxanide Meta
Curcumin Meta Nitric Oxide Meta
Ensovibep Meta Paxlovid Meta
Famotidine Meta Peg.. Lambda Meta
Favipiravir Meta Povidone-Iod.. Meta
Fluvoxamine Meta Quercetin Meta
Hydroxychlor.. Meta Remdesivir Meta
Iota-carragee.. Meta
Ivermectin Meta Zinc Meta

Other Treatments Global Adoption
Loading...

Remdesivir for COVID-19: real-time meta analysis of 54 studies

Covid Analysis, June 2023
https://c19early.org/smeta.html
 
0 0.5 1 1.5+ All studies 11% 54 135,962 Improvement, Studies, Patients Relative Risk Mortality 12% 46 133,889 Ventilation -10% 8 33,882 ICU admission -33% 3 3,403 Hospitalization -6% 7 5,843 Progression 0% 5 14,546 Viral clearance -2% 3 322 RCTs 12% 9 10,177 RCT mortality 9% 8 9,615 Peer-reviewed 6% 47 125,295 Early 40% 6 1,505 Late 11% 49 134,829 Remdesivir for COVID-19 c19early.org/s Jun 2023 Favorsremdesivir Favorscontrol after exclusions
Meta analysis shows 12% [4‑20%] lower mortality, and pooled analysis using the most serious outcome reported shows 11% [4‑17%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and worse for peer-reviewed studies.
While studies to date show a small mortality improvement, meta regression with followup duration shows decreasing efficacy. This may reflect antiviral efficacy being offset by side effects of treatment.
0 0.5 1 1.5+ All studies 11% 54 135,962 Improvement, Studies, Patients Relative Risk Mortality 12% 46 133,889 Ventilation -10% 8 33,882 ICU admission -33% 3 3,403 Hospitalization -6% 7 5,843 Progression 0% 5 14,546 Viral clearance -2% 3 322 RCTs 12% 9 10,177 RCT mortality 9% 8 9,615 Peer-reviewed 6% 47 125,295 Early 40% 6 1,505 Late 11% 49 134,829 Remdesivir for COVID-19 c19early.org/s Jun 2023 Favorsremdesivir Favorscontrol after exclusions
Studies show significantly increased risk of acute kidney injury [Gérard, Wu, Zhou].
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 2% of remdesivir studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Highlights
Remdesivir shows a small mortality improvement, however this is primarily from studies with short followup duration, and efficacy declines with extended followup.
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+ Madan (ES) 66% 0.34 [0.12-0.96] death 4/112 27/260 Improvement, RR [CI] Treatment Control PINETREE Gottlieb (DB RCT) 87% 0.13 [0.03-0.59] death/hosp. 2/279 15/283 Piccicacco 66% 0.34 [0.01-8.32] death 0/82 1/90 Kneidinger 20% 0.80 [0.35-1.82] severe case 6/46 28/172 Ong -75% 1.75 [0.23-13.0] recov. time 4 (n) 14 (n) Chew -68% 1.68 [0.51-5.58] progression 12 (n) 151 (n) Tau​2 = 0.43, I​2 = 49.6%, p = 0.2 Early treatment 40% 0.60 [0.28-1.30] 12/535 71/970 40% improvement Wang (RCT) -9% 1.09 [0.54-2.18] death 22/158 10/78 Improvement, RR [CI] Treatment Control Olender 59% 0.41 [0.24-0.71] death 24/312 102/818 Spinner (RCT) 35% 0.65 [0.18-2.40] death 5/384 4/200 Pasquini (ICU) 16% 0.84 [0.69-0.94] death 14/25 24/26 ICU patients Fried 61% 0.39 [0.15-0.99] death 4/48 2,510/11,673 Beigel (RCT) 27% 0.73 [0.52-1.03] death 541 (n) 521 (n) SOLIDARITY SOLIDARITY (RCT) 5% 0.95 [0.81-1.11] death 301/2,743 303/2,708 Solh 47% 0.53 [0.39-0.70] death 63/219 202/424 SARSTer Flisiak 49% 0.51 [0.19-1.30] death 5/122 17/211 Garibaldi 20% 0.80 [0.46-1.41] death 23/303 45/303 Ullah -100% 2.00 [0.67-5.94] death 8/30 4/30 Yeramaneni -24% 1.24 [0.11-14.2] death 32 (n) 7,126 (n) Goldberg 9% 0.91 [0.50-1.67] hosp. time 29 (n) 113 (n) Tsuzuki -4% 1.04 [0.98-1.09] death 69/824 285/11,663 Mahajan (RCT) -76% 1.76 [0.46-6.82] death 5/34 3/36 Mulhem -86% 1.86 [0.21-5.24] death 1/8 515/3,211 Aghajani 19% 0.81 [0.46-1.46] death 46 (n) 945 (n) Elhadi (ICU) -11% 1.11 [0.81-1.51] death 14/21 267/444 ICU patients Pourhoseingholi -2% 1.02 [0.72-1.44] death 42/123 297/2,345 Arch (PSM) 20% 0.80 [0.64-0.98] death 203/1,491 777/4,676 Barrat-Due (DB RCT) 0% 1.00 [0.20-4.60] death 3/42 4/57 Ohl (PSM) -6% 1.06 [0.83-1.36] death 143/1,172 124/1,172 Madan 44% 0.56 [0.33-0.95] death 23/398 27/260 Kuno (PSM) 1% 0.99 [0.84-1.17] death 214/999 216/999 Diaz 35% 0.65 [0.46-0.92] death 33/286 173/852 DISCOVERY Ader (RCT) 6% 0.94 [0.59-1.45] death 34/414 37/418 Mozaffari 12% 0.88 [0.81-0.96] death 4,441/28,855 5,499/28,855 Schmidt (PSM) -509% 6.09 [2.71-13.7] severe case 43 (n) 434 (n) Jamir (ICU) 8% 0.92 [0.55-1.55] death 60/181 41/85 ICU patients Mustafa 33% 0.67 [0.38-1.20] death 16/200 29/244 CATCO Ali (RCT) 12% 0.88 [0.72-1.07] death 127/634 152/647 Kurniyanto -460% 5.60 [2.32-13.5] death 7/45 12/432 Siraj 53% 0.47 [0.35-0.62] death 108/413 197/587 Salehi (ICU) 37% 0.63 [0.43-0.94] death 17/40 57/85 ICU patients Elec 19% 0.81 [0.38-1.69] death 7/38 29/127 Zangeneh (ICU) 32% 0.68 [0.45-1.01] death n/a n/a ICU patients Malundo -17% 1.17 [0.80-1.70] death 24/115 197/1,100 Bowen -57% 1.57 [1.25-1.97] death 817 (n) 3,814 (n) Raad 42% 0.58 [0.39-0.88] death n/a n/a Oku -40% 1.40 [0.41-4.36] death 3/46 8/172 Behboodikhah 38% 0.62 [0.30-1.30] death 1,214 (n) 960 (n) Hartantri 11% 0.89 [0.31-2.53] death n/a n/a Alshamrani (PSM) 17% 0.83 [0.72-0.93] death 137/246 725/1,078 Mitsushima -44% 1.44 [1.09-1.90] death n/a n/a Punzalan -42% 1.42 [0.92-2.20] death 47/224 26/176 Kim -1612% 17.12 [0.19-1565] death 14/145 0/22 Aweimer -13% 1.13 [0.93-1.37] death 40/51 68/98 Intubated patients Arfijanto 1% 0.99 [0.64-1.53] viral+ 17/44 46/118 Bavaro (PSW) 7% 0.93 [0.89-0.97] severe case 120 (n) 211 (n) Tau​2 = 0.03, I​2 = 76.8%, p = 0.0044 Late treatment 11% 0.89 [0.83-0.97] 6,318/44,275 13,032/90,554 11% improvement All studies 11% 0.89 [0.83-0.96] 6,330/44,810 13,103/91,524 11% improvement Remdesivir COVID-19 studies c19early.org/s Jun 2023 Tau​2 = 0.03, I​2 = 75.4%, p = 0.0035 Effect extraction pre-specified(most serious outcome, see appendix) Favors remdesivir Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Madan (ES) 66% death Relative Risk [CI] PINETREE Gottlieb (DB RCT) 87% death/hosp. Piccicacco 66% death Kneidinger 20% severe case Ong -75% recovery Chew -68% progression Tau​2 = 0.43, I​2 = 49.6%, p = 0.2 Early treatment 40% 40% improvement Wang (RCT) -9% death Olender 59% death Spinner (RCT) 35% death Pasquini (ICU) 16% death ICU patients Fried 61% death Beigel (RCT) 27% death SOLIDARITY SOLIDARITY (RCT) 5% death Solh 47% death SARSTer Flisiak 49% death Garibaldi 20% death Ullah -100% death Yeramaneni -24% death Goldberg 9% hospitalization Tsuzuki -4% death Mahajan (RCT) -76% death Mulhem -86% death Aghajani 19% death Elhadi (ICU) -11% death ICU patients Pourhoseingholi -2% death Arch (PSM) 20% death Barrat-.. (DB RCT) 0% death Ohl (PSM) -6% death Madan 44% death Kuno (PSM) 1% death Diaz 35% death DISCOVERY Ader (RCT) 6% death Mozaffari 12% death Schmidt (PSM) -509% severe case Jamir (ICU) 8% death ICU patients Mustafa 33% death CATCO Ali (RCT) 12% death Kurniyanto -460% death Siraj 53% death Salehi (ICU) 37% death ICU patients Elec 19% death Zangeneh (ICU) 32% death ICU patients Malundo -17% death Bowen -57% death Raad 42% death Oku -40% death Behboodikhah 38% death Hartantri 11% death Alshamrani (PSM) 17% death Mitsushima -44% death Punzalan -42% death Kim -1612% death Aweimer -13% death Intubated patients Arfijanto 1% viral- Bavaro (PSW) 7% severe case Tau​2 = 0.03, I​2 = 76.8%, p = 0.0044 Late treatment 11% 11% improvement All studies 11% 11% improvement 55 remdesivir COVID-19 studies c19early.org/s Jun 2023 Tau​2 = 0.03, I​2 = 75.4%, p = 0.0035 Effect extraction pre-specifiedRotate device for details Favors remdesivir Favors control
B
Loading..
C
Loading..
D
Loading..
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 3,989 proposed treatments show efficacy [c19early.org]. D. Timeline of results in remdesivir studies.
We analyze all significant studies concerning the use of remdesivir 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.
3 In Vitro studies support the efficacy of remdesivir [De Forni, Delandre, Jeffreys].
An In Vivo animal study supports the efficacy of remdesivir [Vermillion].
[Vermillion] investigate a novel formulation of remdesivir that may be more effective for COVID-19.
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, 7, 8, 9, 10, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, viral clearance, and peer reviewed studies.
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. * p<0.05  ** p<0.01.
Improvement Studies Patients Authors
All studies11% [4‑17%]
**
55 135,962 883
After exclusions11% [4‑17%]
**
41 115,973 709
Peer-reviewed studiesPeer-reviewed6% [-2‑14%]47 125,295 762
Randomized Controlled TrialsRCTs12% [-3‑24%]9 10,177 279
Mortality12% [4‑20%]
**
47 133,889 739
VentilationVent.-10% [-54‑22%]8 33,882 153
ICU admissionICU-33% [-62‑-10%]
**
3 3,403 18
HospitalizationHosp.-6% [-34‑17%]7 5,843 153
Recovery21% [12‑29%]
****
4 2,487 139
Viral-2% [-17‑11%]3 322 27
RCT mortality9% [-1‑18%]8 9,615 249
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. * p<0.05  ** p<0.01.
Early treatment Late treatment
All studies40% [-30‑72%]11% [3‑17%]
**
After exclusions30% [-79‑73%]11% [4‑17%]
**
Peer-reviewed studiesPeer-reviewed30% [-79‑73%]6% [-2‑13%]
Randomized Controlled TrialsRCTs87% [41‑97%]
**
9% [-1‑18%]
Mortality66% [9‑87%]
*
12% [4‑20%]
**
VentilationVent.--10% [-54‑22%]
ICU admissionICU--33% [-62‑-10%]
**
HospitalizationHosp.31% [-81‑74%]-13% [-42‑10%]
Recovery28% [10‑43%]
**
18% [5‑29%]
**
Viral-61% [-200‑14%]0% [-9‑8%]
RCT mortality-9% [-1‑18%]
Loading..
Loading..
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.
Loading..
Loading..
Figure 4. Random effects meta-analysis for mortality results.
Loading..
Figure 5. Random effects meta-analysis for ventilation.
Loading..
Figure 6. Random effects meta-analysis for ICU admission.
Loading..
Figure 7. Random effects meta-analysis for hospitalization.
Loading..
Figure 8. Random effects meta-analysis for progression.
Loading..
Figure 9. Random effects meta-analysis for recovery.
Loading..
Figure 10. Random effects meta-analysis for viral clearance.
Loading..
Figure 11. 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 12 shows a comparison of results for RCTs and non-RCT studies. Figure 13 and 14 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. RCT results are included in Table 1 and Table 2.
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, 37 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 37 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
Loading..
Figure 12. Results for RCTs and non-RCT studies.
Loading..
Figure 13. 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.
Loading..
Figure 14. Random effects meta-analysis for RCT mortality results.
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 15 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Arfijanto], unadjusted results with no group details.
[Elec], substantial confounding by time possible due to significant changes in SOC and treatment propensity during the study period.
[Elhadi], unadjusted results with no group details.
[Fried], excessive unadjusted differences between groups; substantial unadjusted confounding by indication likely.
[Kurniyanto], unadjusted results with no group details; substantial unadjusted confounding by indication likely.
[Madan], unadjusted results with no group details.
[Madan (B)], excessive unadjusted differences between groups.
[Malundo], unadjusted results with no group details.
[Mulhem], substantial unadjusted confounding by indication likely; substantial confounding by time possible due to significant changes in SOC and treatment propensity during the study period.
[Mustafa], unadjusted results with no group details.
[Oku], unadjusted results with no group details.
[Salehi], unadjusted results with no group details.
[Schmidt], confounding by indication is likely and adjustments do not consider COVID-19 severity at baseline.
[Solh], very late stage, >50% on oxygen/ventilation at baseline; substantial unadjusted confounding by indication likely.
Loading..
Figure 15. 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 3. 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 16 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 16. 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 17. 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, 37 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 94% 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.
Loading..
Loading..
Figure 17. 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.
Figure 18 shows a mixed-effects meta-regression of efficacy as a function of followup duration, which shows decreasing efficacy with longer followup. This may reflect antiviral efficacy being offset by side effects of treatment.
Figure 18. Efficacy decreases with longer followup. Meta-regression showing mortality efficacy as a function of followup duration in COVID-19 remdesivir studies.
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).
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 19 shows a scatter plot of results for prospective and retrospective studies. 38% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 31% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 12% improvement, compared to 5% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy.
Loading..
Figure 19. 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 20 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 20. 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 (B), Gasmi, Jeffreys (B), 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 shows 12% [4‑20%] lower mortality, and pooled analysis using the most serious outcome reported shows 11% [4‑17%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and worse for peer-reviewed studies. While studies to date show a small mortality improvement, meta regression with followup duration shows decreasing efficacy. This may reflect antiviral efficacy being offset by side effects of treatment.
Studies show significantly increased risk of acute kidney injury [Gérard, Wu, Zhou].
0 0.5 1 1.5 2+ Mortality, day 28 6% Improvement Relative Risk Mortality, day 15 12% 7-point scale 10% 7-point scale (b) -2% c19early.org/s Ader et al. NCT04315948 DISCOVERY Remdesivir RCT LATE Is late treatment with remdesivir beneficial for COVID-19? RCT 832 patients in multiple countries No significant difference in outcomes seen Ader et al., Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00485-0 Favors remdesivir Favors control
[Ader] RCT 857 hospitalized patients, showing no significant differences with remdesivir treatment. EudraCT2020-000936-23.
0 0.5 1 1.5 2+ Mortality 19% Improvement Relative Risk c19early.org/s Aghajani et al. Remdesivir for COVID-19 LATE Is late treatment with remdesivir beneficial for COVID-19? Retrospective 991 patients in Iran Lower mortality with remdesivir (not stat. sig., p=0.49) Aghajani et al., J. Medical Virology, doi:10.1002/jmv.27053 Favors remdesivir Favors control
[Aghajani] Retrospective 991 hospitalized patients in Iran focusing on aspirin use but also showing results for HCQ, remdesivir, and favipiravir.
0 0.5 1 1.5 2+ Mortality, day 60 12% Improvement Relative Risk Mortality 17% Mortality, day 15 21% Ventilation 47% Recovery 9% Hospitalization time -11% c19early.org/s Ali et al. NCT04330690 CATCO Remdesivir RCT LATE Is late treatment with remdesivir beneficial for COVID-19? RCT 1,281 patients in Canada Lower ventilation (p=0.00028) and longer hospitalization (p=0.036) Ali et al., Canadian Medical Association J., doi:10.1503/cmaj.211698 Favors remdesivir Favors control
[Ali] RCT 1,282 hospitalized patients in Canada showing lower mechanical ventilation with remdesivir treatment, but no significant difference for mortality.
0 0.5 1 1.5 2+ Mortality 17% Improvement Relative Risk Progression 4% ICU time -43% Hospitalization time 7% c19early.org/s Alshamrani et al. Remdesivir for COVID-19 LATE Is late treatment with remdesivir beneficial for COVID-19? PSM retrospective 1,324 patients in Saudi Arabia (Mar 2020 - Jan 2021) Lower mortality (p=0.0031) and longer ICU admission (p=0.003) Alshamrani et al., Saudi Pharmaceutical J., doi:10.1016/j.jsps.2023.02.004 Favors remdesivir Favors control
[Alshamrani] PSM retrospective 29 hospitals in Saudi Arabia, showing lower mortality with remdesivir treatment.
0 0.5 1 1.5 2+ Mortality, day 28 20% Improvement Relative Risk Mortality, day 14 18% Ventilation -68% c19early.org/s Arch et al. Remdesivir for COVID-19 LATE TREATMENT Is late treatment with remdesivir beneficial for COVID-19? PSM prospective study of 6,230 patients in the United Kingdom Lower mortality (p=0.034) and higher ventilation (p=0.003) Arch et al., medRxiv, doi:10.1101/2021.06.18.21259072 Favors remdesivir Favors control
[Arch] Prospective PSM analysis of remdesivir use in the UK showing statistically significantly lower mortality at 28 days. For unspecified reasons, the study prioritized short-term outcomes. Mortality at 14 days was also lower but not statistically significant. Confounding by indication is likely and may only be partially addressed by the variables included in the PSM.
0 0.5 1 1.5 2+ Delayed viral clearance 1% Improvement Relative Risk c19early.org/s Arfijanto et al. Remdesivir for COVID-19 LATE Is late treatment with remdesivir beneficial for COVID-19? Retrospective 162 patients in Indonesia (June - December 2021) No significant difference in viral clearance Arfijanto et al., Pathophysiology, doi:10.3390/pathophysiology30020016 Favors remdesivir Favors control
[Arfijanto] Retrospective 162 hospitalized COVID-19 patients in Indonesia, showing no significant difference in delayed viral clearance with remdesivir treatment in unadjusted results.
0 0.5 1 1.5 2+ Mortality -13% Improvement Relative Risk c19early.org/s Aweimer et al. Remdesivir for COVID-19 INTUBATED PATIENTS Is very late treatment with remdesivir beneficial for COVID-19? Retrospective 149 patients in Germany (March 2020 - August 2021) No significant difference in mortality Aweimer et al., Scientific Reports, doi:10.1038/s41598-023-31944-7 Favors remdesivir Favors control
[Aweimer] Retrospective 149 patients under invasive mechanical ventilation in Germany showing no significant difference in mortality with remdesivir in unadjusted results.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk Mortality, day 60 -36% Mortality, day 28 55% c19early.org/s Barrat-Due et al. NCT04321616 Remdesivir RCT LATE Is late treatment with remdesivir beneficial for COVID-19? Double-blind RCT 99 patients in Norway Trial underpowered to detect differences Barrat-Due et al., Annals of Internal Medicine, doi:10.7326/M21-0653 Favors remdesivir Favors control
[Barrat-Due] Small RCT in Norway with 52 HCQ and 42 remdesivir patients, showing no significant differences with treatment. Add-on trial to WHO Solidarity. NCT04321616.
0 0.5 1 1.5 2+ Severe case 7% Improvement Relative Risk c19early.org/s Bavaro et al. Remdesivir for COVID-19 LATE TREATMENT Is late treatment with remdesivir beneficial for COVID-19? Retrospective 331 patients in Italy (July 2021 - March 2022) Lower severe cases with remdesivir (p=0.00099) Bavaro et al., Viruses, doi:10.3390/v15051199 Favors remdesivir Favors control
[Bavaro] Retrospective 331 hospitalized COVID-19 patients in Italy, showing lower progression with remdesivir. Combination therapy with mAbs was more effective, and improved results were seen for immunocompromised patients.
0 0.5 1 1.5 2+ Mortality 38% Improvement Relative Risk c19early.org/s Behboodikhah et al. Remdesivir for COVID-19 LATE Is late treatment with remdesivir beneficial for COVID-19? Retrospective 2,174 patients in Iran Lower mortality with remdesivir (not stat. sig., p=0.21) Behboodikhah et al., Iranian J. Science and Tech.., doi:10.1007/s40995-022-01351-0 Favors remdesivir Favors control
[Behboodikhah] Retrospective 2,174 hospitalized patients showing no significant differences with remdesivir treatment.
0 0.5 1 1.5 2+ Mortality, day 29 27% Improvement Relative Risk Mortality, day 15 45% Recovery 22% c19early.org/s Beigel et al. Remdesivir for COVID-19 RCT LATE TREATMENT Is late treatment with remdesivir beneficial for COVID-19? RCT 1,062 patients in the USA Improved recovery with remdesivir (p=0.0005) Beigel et al., NEJM, doi:10.1056/NEJMoa2007764 Favors remdesivir Favors control
[Beigel] RCT 1,062 hospitalized patients showing faster recovery time with treatment, median 10 days vs. 15 days for placebo, rate ratio for recovery 1.29, p<0.001. Day 29 mortality was 11.4% with remdesivir and 15.2% with placebo, hazard ratio HR 0.73 [0.52-1.03].
0 0.5 1 1.5 2+ Mortality -57% Improvement Relative Risk c19early.org/s Bowen et al. Remdesivir for COVID-19 LATE TREATMENT Is late treatment with remdesivir beneficial for COVID-19? Retrospective 4,631 patients in the USA (March 2020 - March 2021) Higher mortality with remdesivir (p=0.00011) Bowen et al., Open Forum Infectious Diseases, doi:10.1093/ofid/ofac436 Favors remdesivir Favors control
[Bowen] Retrospective 4,631 hospitalized patients in New York, showing higher mortality with remdesivir, and lower mortality with HCQ. Authors suggest that increased mortality during the first epidemic wave was partly due to strain on hospital resources.
0 0.5 1 1.5 2+ Abnormal ALT -68% Improvement Relative Risk c19early.org/s Chew et al. Remdesivir for COVID-19 EARLY TREATMENT Is early treatment with remdesivir beneficial for COVID-19? Retrospective 163 patients in Singapore (January - April 2020) Higher progression with remdesivir (not stat. sig., p=0.4) Chew et al., Pathogens, doi:10.3390/pathogens12030473 Favors remdesivir Favors control
[Chew] Retrospective 163 COVID-19 patients in Singapore, showing increased risk of liver injury (abnormal ALT) with acetaminophen in a dose-dependent manner, and with remdesivir, without statistical significance in both cases.
0 0.5 1 1.5 2+ Mortality, day 60 35% Improvement Relative Risk Mortality, day 30 44% c19early.org/s Diaz et al. Remdesivir for COVID-19 LATE TREATMENT Is late treatment with remdesivir beneficial for COVID-19? Retrospective 1,138 patients in the USA Lower mortality with remdesivir (p=0.014) Diaz et al., Clinical Infectious Diseases, doi:10.1093/cid/ciab698 Favors remdesivir Favors control
[Diaz] Retrospective 1138 hospitalized patients in the USA, 286 treated with remdesivir, showing lower mortality with treatment.

Age was not included in the adjustments (authors excluded variables that contributed to another score, in this case age is in Pneumonia Severity Index).
0 0.5 1 1.5 2+ Mortality 19% Improvement Relative Risk Ventilation 11% ICU admission -72% c19early.org/s Elec et al. Remdesivir for COVID-19 LATE TREATMENT Is late treatment with remdesivir beneficial for COVID-19? Retrospective 165 patients in Romania (March 2020 - May 2021) Higher ICU admission with remdesivir (p=0.01) Elec et al., Int. J. Infectious Diseases, doi:10.1016/j.ijid.2022.03.015 Favors remdesivir Favors control
[Elec] Retrospective 165 hospitalized COVID-19+ kidney transplant patients, 38 treated with remdesivir, showing no significant difference in mortality, higher ICU admission, and lower ICU mortality. Subject to confounding by time with significant changes to SOC and treatment propensity during the study period.
0 0.5 1 1.5 2+ Mortality -11% Improvement Relative Risk c19early.org/s Elhadi et al. Remdesivir for COVID-19 ICU PATIENTS Is very late treatment with remdesivir beneficial for COVID-19? Prospective study of 465 patients in Libya (May - December 2020) No significant difference in mortality Elhadi et al., PLOS ONE, doi:10.1371/journal.pone.0251085 Favors remdesivir Favors control
[Elhadi] Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.
0 0.5 1 1.5 2+ Mortality 49% Improvement Relative Risk SpO2<95% 58% Clinical improvement 56% c19early.org/s Flisiak et al. SARSTer Remdesivir LATE TREATMENT Is late treatment with remdesivir beneficial for COVID-19? Retrospective 333 patients in Poland (March - August 2020) Greater improvement with remdesivir (p=0.01) Flisiak et al., Polish Archives of Internal Medi.., doi:10.20452/pamw.15735 Favors remdesivir Favors control
[Flisiak] Retrospective study comparing 122 remdesivir patients and 211 lopinavir/ritonavir patients, showing higher rates of clinical improvement with remdesivir and lower mortality (not statistically significant).
0 0.5 1 1.5 2+ Mortality 61% Improvement Relative Risk Ventilation -37% c19early.org/s Fried et al. Remdesivir for COVID-19 LATE TREATMENT Is late treatment with remdesivir beneficial for COVID-19? Retrospective 11,721 patients in the USA Lower mortality with remdesivir (p=0.022) Fried et al., Clinical Infectious Disease, doi:10.1093/cid/ciaa1268 Favors remdesivir Favors control
[Fried] Database analysis of 11,721 hospitalized patients, 48 treated with remdesivir.

Data inconsistencies have been found in this study, for example 99.4% of patients treated with HCQ were treated in urban hospitals, compared to 65% of untreated patients (Supplemental Table 3), while patients are distributed in a more balanced manner between teaching or not-teaching hospitals, as well as in the most urbanized (Northeast) and less urbanized (Midwest) regions of the United States [academic.oup.com].
0 0.5 1 1.5 2+ Mortality 20% Improvement Relative Risk Improvement 35% c19early.org/s Garibaldi et al. Remdesivir for COVID-19 LATE Is late treatment with remdesivir beneficial for COVID-19? Retrospective 606 patients in the USA Greater improvement with remdesivir (p=0.000015) Garibaldi et al., medRxiv, doi:10.1101/2020.11.19.20234153 Favors remdesivir Favors control
[Garibaldi] Retrospective 303 remdesivir patients and 303 matched controls showing significantly faster clinical improvement, and lower (but not statistically significant) mortality.
0 0.5 1 1.5 2+ Hospitalization time 9% Improvement Relative Risk Hospitalization time (b) 22% Viral clearance 0% c19early.org/s Goldberg et al. Remdesivir for COVID-19 LATE Is late treatment with remdesivir beneficial for COVID-19? Retrospective 142 patients in Israel No significant difference in outcomes seen Goldberg et al., Clinical Microbiology and Infec.., doi:10.1016/j.cmi.2021.02.029 Favors remdesivir Favors control
[Goldberg] Retrospective 29 remdesivir patients and 113 controls, not finding a significant difference in nasopharyngeal viral load or hospitalization time. Hospitalization time was lower with treatment, with a larger reduction for non-intubated patients, although not statistically significant in both cases.
0 0.5 1 1.5 2+ Death/hospitalization 87% primary Improvement Relative Risk Hospitalization 72% Recovery 29% Recovery (b) 48% c19early.org/s Gottlieb et al. NCT04501952 PINETREE Remdesivir