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Colchicine for COVID-19: real-time meta analysis of 40 studies
Covid Analysis, December 2022
https://c19early.org/ometa.html
 
0 0.5 1 1.5+ All studies 37% 40 29,991 Improvement, Studies, Patients Relative Risk Mortality 37% 33 27,946 Ventilation 38% 8 13,258 ICU admission 30% 6 967 Hospitalization 19% 13 11,092 Progression 51% 6 3,344 Recovery 27% 10 12,455 Cases -33% 2 1,522 RCTs 17% 21 25,995 RCT mortality 6% 18 25,613 Peer-reviewed 37% 38 29,570 Prophylaxis 41% 5 1,595 Early 68% 1 0 Late 35% 34 28,396 Colchicine for COVID-19 c19early.org/o Dec 2022 Favorscolchicine Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 23 studies from 23 independent teams in 14 different countries show statistically significant improvements in isolation (14 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 37% [26‑46%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral. Early treatment is more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 25 of 40 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 37% 40 29,991 Improvement, Studies, Patients Relative Risk Mortality 37% 33 27,946 Ventilation 38% 8 13,258 ICU admission 30% 6 967 Hospitalization 19% 13 11,092 Progression 51% 6 3,344 Recovery 27% 10 12,455 Cases -33% 2 1,522 RCTs 17% 21 25,995 RCT mortality 6% 18 25,613 Peer-reviewed 37% 38 29,570 Prophylaxis 41% 5 1,595 Early 68% 1 0 Late 35% 34 28,396 Colchicine for COVID-19 c19early.org/o Dec 2022 Favorscolchicine Favorscontrol after exclusions
RCT results are less favorable, however they are dominated by the very late stage RECOVERY RCT, for which the results are not generalizable to earlier usage.
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 12% of colchicine studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix. Other meta analyses for colchicine can be found in [Rai, Yasmin, Zein], showing significant improvements for mortality and severity.
Highlights
Colchicine reduces risk for COVID-19 with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for ICU admission, and low confidence for ventilation and progression, however increased risk is seen with very low confidence for cases.
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.
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0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hunt 68% 0.32 [0.15-0.67] death Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.0006 Early treatment 68% 0.32 [0.15-0.67] 68% improvement Deftereos (RCT) 77% 0.23 [0.03-1.97] death 1/55 4/50 Improvement, RR [CI] Treatment Control Lopes (DB RCT) 80% 0.20 [0.01-4.03] death 0/36 2/36 Brunetti (PSM) 73% 0.27 [0.08-0.89] death 3/33 11/33 Scarsi 85% 0.15 [0.06-0.37] death 122 (n) 140 (n) Salehzadeh (RCT) 23% 0.77 [0.66-0.90] hosp. time 50 (n) 50 (n) Pinzón 35% 0.65 [0.34-1.21] death 14/145 23/156 Sandhu 42% 0.58 [0.40-0.85] death 16/34 63/78 Rodriguez-Nava 6% 0.94 [0.61-1.47] death 16/52 85/261 Mahale -7% 1.07 [0.59-1.96] death 11/39 25/95 Valerio Pas.. (ICU) 23% 0.77 [0.31-1.94] death 5/35 12/30 ICU patients CT​1 Tardif (DB RCT) 44% 0.56 [0.19-1.67] death 5/2,235 9/2,253 Mareev 80% 0.20 [0.01-4.01] death 0/21 2/22 García-Posada 57% 0.43 [0.16-0.84] death 48/99 59/110 CT​1 Manenti (PSW) 76% 0.24 [0.09-0.67] death 71 (n) 70 (n) Mostafaie (RCT) 83% 0.17 [0.02-1.34] death 1/60 6/60 CT​1 Recovery C.. (RCT) -1% 1.01 [0.93-1.10] death 1,173/5,610 1,190/5,730 Hueda-Zavaleta 54% 0.46 [0.23-0.91] death 10/50 109/301 Kevorkian 96% 0.04 [0.01-0.21] progression 28 (n) 40 (n) CT​1 Gaitán-Dua.. (RCT) 22% 0.78 [0.44-1.36] death 22/153 28/161 CT​1 Pascual-Fi.. (RCT) 80% 0.20 [0.01-4.03] death 0/52 2/51 Dorward (RCT) 70% 0.30 [0.01-7.37] death 0/156 1/120 Absalón-.. (DB RCT) 29% 0.71 [0.21-2.40] death 4/56 6/60 Diaz (RCT) 12% 0.88 [0.70-1.12] death 131/640 142/639 Alsultan (RCT) 36% 0.64 [0.20-2.07] death 3/14 7/21 Karakaş 13% 0.87 [0.46-1.64] death 16/165 19/171 Pourdowlat (RCT) 73% 0.27 [0.11-0.71] hosp. 5/102 18/100 Gorial (RCT) 67% 0.33 [0.04-3.14] death 1/80 3/80 Pimenta B.. (RCT) 79% 0.21 [0.01-4.05] death 0/14 2/16 Jalal (RCT) 24% 0.76 [0.62-0.93] hosp. time 36 (n) 44 (n) Cecconi (DB RCT) 29% 0.71 [0.28-1.79] death 7/119 10/120 Eikelboom (RCT) -8% 1.08 [0.91-1.29] death 264/1,304 249/1,307 Eikelboom (RCT) -9% 1.09 [0.48-2.47] death 12/1,939 11/1,942 Perricone (RCT) -36% 1.36 [0.45-4.11] death 7/77 5/75 Rahman (DB RCT) 71% 0.29 [0.10-0.92] death 4/146 13/146 Tau​2 = 0.09, I​2 = 72.8%, p < 0.0001 Late treatment 35% 0.65 [0.56-0.77] 1,779/13,828 2,116/14,568 35% improvement Monserrat .. (PSM) 80% 0.20 [0.02-0.93] death n/a n/a Improvement, RR [CI] Treatment Control Topless 23% 0.77 [0.56-1.07] death Oztas -406% 5.06 [0.59-43.2] hosp. 5/635 1/643 Avanoglu Guler 79% 0.21 [0.04-0.83] oxygen 6/66 3/7 Correa-Rodríguez -150% 2.50 [0.10-60.6] oxygen 1/163 0/81 Tau​2 = 0.68, I​2 = 65.3%, p = 0.3 Prophylaxis 41% 0.59 [0.23-1.57] 12/864 4/731 41% improvement All studies 37% 0.63 [0.54-0.74] 1,791/14,692 2,120/15,299 37% improvement 40 colchicine COVID-19 studies c19early.org/o Dec 2022 Tau​2 = 0.10, I​2 = 73.0%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors colchicine Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hunt 68% death Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.0006 Early treatment 68% 68% improvement Deftereos (RCT) 77% death Lopes (DB RCT) 80% death Brunetti (PSM) 73% death Scarsi 85% death Salehzadeh (RCT) 23% hospitalization Pinzón 35% death Sandhu 42% death Rodriguez-Nava 6% death Mahale -7% death Valerio Pa.. (ICU) 23% death ICU patients CT​1 Tardif (DB RCT) 44% death Mareev 80% death García-Posada 57% death CT​1 Manenti (PSW) 76% death Mostafaie (RCT) 83% death CT​1 Recovery .. (RCT) -1% death Hueda-Zavaleta 54% death Kevorkian 96% progression CT​1 Gaitán-Du.. (RCT) 22% death CT​1 Pascual-F.. (RCT) 80% death Dorward (RCT) 70% death Absalón.. (DB RCT) 29% death Diaz (RCT) 12% death Alsultan (RCT) 36% death Karakaş 13% death Pourdowlat (RCT) 73% hospitalization Gorial (RCT) 67% death Pimenta .. (RCT) 79% death Jalal (RCT) 24% hospitalization Cecconi (DB RCT) 29% death Eikelboom (RCT) -8% death Eikelboom (RCT) -9% death Perricone (RCT) -36% death Rahman (DB RCT) 71% death Tau​2 = 0.09, I​2 = 72.8%, p < 0.0001 Late treatment 35% 35% improvement Monserrat.. (PSM) 80% death Topless 23% death Oztas -406% hospitalization Avanoglu Guler 79% oxygen therapy Correa-Rodríguez -150% oxygen therapy Tau​2 = 0.68, I​2 = 65.3%, p = 0.3 Prophylaxis 41% 41% improvement All studies 37% 37% improvement 40 colchicine COVID-19 studies c19early.org/o Dec 2022 Tau​2 = 0.10, I​2 = 73.0%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors colchicine Favors control
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. D. Timeline of results in colchicine studies.
We analyze all significant studies concerning the use of colchicine 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.
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, cases, and peer reviewed studies.
Improvement Studies Patients Authors
All studies37% [26‑46%]40 29,991 785
After exclusions46% [34‑55%]34 26,571 625
Peer-reviewed studiesPeer-reviewed37% [26‑46%]38 29,570 775
Randomized Controlled TrialsRCTs17% [5‑28%]21 25,995 540
Mortality37% [24‑47%]33 27,946 715
VentilationVent.38% [-5‑64%]8 13,258 231
ICU admissionICU30% [7‑47%]6 967 149
HospitalizationHosp.19% [10‑27%]13 11,092 241
Cases-33% [-128‑23%]2 1,522 21
RCT mortality6% [-6‑16%]18 25,613 516
RCT hospitalizationRCT hosp.18% [7‑28%]9 9,191 188
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 Prophylaxis
All studies68% [33‑85%] 135% [23‑44%] 3441% [-57‑77%] 5
After exclusions68% [33‑85%] 143% [30‑54%] 2957% [-13‑83%] 4
Peer-reviewed studiesPeer-reviewed68% [33‑85%] 134% [22‑44%] 3241% [-57‑77%] 5
Randomized Controlled TrialsRCTs-17% [5‑28%] 21-
Mortality68% [33‑85%] 134% [20‑45%] 3053% [-67‑87%] 2
VentilationVent.-38% [-5‑64%] 8-
ICU admissionICU-30% [7‑47%] 6-
HospitalizationHosp.-20% [11‑28%] 11-306% [-2308‑31%] 2
Cases---33% [-128‑23%] 2
RCT mortality-6% [-6‑16%] 18-
RCT hospitalizationRCT hosp.-18% [7‑28%] 9-
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 ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. 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.
Figure 12 shows a comparison of results for RCTs and non-RCT studies. Figure 13, 14, and 15 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for colchicine are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
In summary, we need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
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Figure 12. Results for RCTs and non-RCT studies.
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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.
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Figure 14. Random effects meta-analysis for RCT mortality results.
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Figure 15. Random effects meta-analysis for RCT hospitalization 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 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Diaz], very late stage, oxygen saturation <90% at baseline.
[Jalal], minimal details provided.
[Karakaş], excessive unadjusted differences between groups.
[Mahale], unadjusted results with no group details.
[Oztas], excessive unadjusted differences between groups.
[Rodriguez-Nava], substantial unadjusted confounding by indication likely; excessive unadjusted differences between groups; unadjusted results with no group details.
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Figure 16. 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.
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 17 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 17. 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 18. 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 18. 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, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso].
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.
67% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 50% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 55% improvement, compared to 39% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 19 shows a scatter plot of results for prospective and retrospective studies.
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Figure 19. Prospective vs. retrospective studies.
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.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Colchicine for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 colchicine trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all colchicine trials represent the optimal conditions for efficacy.
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.
5 of 40 studies combine treatments. The results of colchicine alone may differ. 2 of 21 RCTs use combined treatment. Other meta analyses for colchicine can be found in [Rai, Yasmin, Zein], showing significant improvements for mortality and 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.
Colchicine is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 23 studies from 23 independent teams in 14 different countries show statistically significant improvements in isolation (14 for the most serious outcome). Meta analysis using the most serious outcome reported shows 37% [26‑46%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral. Early treatment is more effective than late treatment. Results are robust — in exclusion sensitivity analysis 25 of 40 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
RCT results are less favorable, however they are dominated by the very late stage RECOVERY RCT, for which the results are not generalizable to earlier usage.
0 0.5 1 1.5 2+ Mortality 29% Improvement Relative Risk Progression to critical or.. 17% primary Recovery -13% c19early.org/o Absalón-Aguilar et al. Colchicine for COVID-19 RCT LATE Favors colchicine Favors control
[Absalón-Aguilar] Very late stage RCT with 56 colchicine and 60 control patients in Mexico, showing no significant differences.
0 0.5 1 1.5 2+ Mortality 36% Improvement Relative Risk Hospitalization time 20% no CI c19early.org/o Alsultan et al. Colchicine for COVID-19 RCT LATE Favors colchicine Favors control
[Alsultan] Small RCT 49 severe condition hospitalized patients in Syria, showing lower mortality with colchicine and shorter hospitalization time with both colchicine and budesonide (all of these were not statistically significant).
0 0.5 1 1.5 2+ Oxygen therapy 79% Improvement Relative Risk c19early.org/o Avanoglu Guler et al. Colchicine for COVID-19 Prophylaxis Favors colchicine Favors control
[Avanoglu Guler] Retrospective 73 familial Mediterranean fever patients with COVID-19 in Turkey, showing significantly higher risk of hospitalization for respiratory support with non-adherence to colchicine treatment before the infection.
0 0.5 1 1.5 2+ Mortality 73% Improvement Relative Risk Discharge 73% c19early.org/o Brunetti et al. Colchicine for COVID-19 LATE Favors colchicine Favors control
[Brunetti] PSM matched analysis from consecutive hospitalized patients, with 33 colchicine and 33 control matched patients, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality 29% Improvement Relative Risk Ventilation 50% ICU admission 21% Combined NIV/ICU/vent.. 15% primary c19early.org/o Cecconi et al. Colchicine for COVID-19 RCT LATE TREATMENT Favors colchicine Favors control
[Cecconi] RCT 240 hospitalized patients with COVID-19 pneumonia, mean 9 days from the onset of symptoms, showing no significant differences with colchicine treatment. EudraCT 2020-001841-38.
0 0.5 1 1.5 2+ Oxygen therapy -150% Improvement Relative Risk Hospitalization -150% Recovery 7% Case 1% c19early.org/o Correa-Rodríguez et al. Colchicine Prophylaxis Favors colchicine Favors control
[Correa-Rodríguez] Retrospective 244 Behçet disease patients in Spain, showing no significant difference in outcomes with colchicine treatment. Confounding by indication may significantly affect results - colchicine may be prescribed more often for more serious cases, which may have a higher baseline risk for COVID-19.
0 0.5 1 1.5 2+ Mortality 77% Improvement Relative Risk Ventilation 82% Clinical deterioration 87% c19early.org/o Deftereos et al. NCT04326790 GRECCO-19 Colchicine RCT LATE Favors colchicine Favors control
[Deftereos] RCT with 55 patients treated with colchicine and 50 control patients, showing lower mortality and ventilation with treatment.
0 0.5 1 1.5 2+ Mortality 12% primary Improvement Relative Risk Death/intubation 17% primary Death/intubation (b) 52% Mortality (b) 17% Death/intubation (c) 25% c19early.org/o Diaz et al. NCT04328480 Colchicine RCT LATE TREATMENT Favors colchicine Favors control
[Diaz] Very late stage RCT (O2 88%, 84% on oxygen) with 1,279 hospitalized patients in Argentina, showing lower mortality and lower combined mortality/ventilation, statistically significant only for the combined outcome and per-protocol analysis. NCT04328480. COLCOVID.
0 0.5 1 1.5 2+ Mortality 70% Improvement Relative Risk Death/hospitalization -30% Death/hospitalization (b) 22% Recovery -6% c19early.org/o Dorward et al. Colchicine for COVID-19 RCT LATE TREATMENT Favors colchicine Favors control
[Dorward] Late treatment RCT with 156 colchicine patients in the UK, showing no significant differences. ISRCTN86534580.
0 0.5 1 1.5 2+ Mortality -9% Improvement Relative Risk Death/hospitalization -2% primary Hospitalization -2% c19early.org/o Eikelboom et al. NCT04324463 ACT outpatient Colchicine RCT LATE Favors colchicine Favors control
[Eikelboom] Late (5.4 days) outpatient RCT showing no significant difference in outcomes with colchicine treatment. Authors include a meta analysis of 6 colchicine RCTs, however there were 19 RCTs as of the publication date [c19colchicine.com].
0 0.5 1 1.5 2+ Mortality -8% Improvement Relative Risk Progression -4% Progression (b) 2% c19early.org/o Eikelboom et al. NCT04324463 ACT inpatient Colchicine RCT LATE Favors colchicine Favors control
[Eikelboom (B)] RCT very late stage (baseline SpO2 80%) patients, showing no significant differences with colchicine treatment.
0 0.5 1 1.5 2+ Mortality 22% Improvement Relative Risk c19early.org/o Gaitán-Duarte et al. NCT04359095 Colchicine RCT LATE Favors colchicine Favors control
[Gaitán-Duarte] RCT 633 hospitalized patients in Colombia, 153 treated with colchicine + rosuvastatin, not showing statistically significant differences in outcomes. Improved results were seen with the combination of emtricitabine/tenofovir disoproxil + rosuvastatin + colchicine. NCT04359095.
0 0.5 1 1.5 2+ Mortality 57% Improvement Relative Risk c19early.org/o García-Posada et al. Colchicine for COVID-19 LATE Favors colchicine Favors control
[García-Posada] Retrospective 209 hospitalized patients in Colombia, showing lower mortality with antibiotics + LMWH + corticosteroids + colchicine in multivariable analysis.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk Recovery 63% c19early.org/o Gorial et al. Colchicine for COVID-19 RCT LATE TREATMENT Favors colchicine Favors control
[Gorial] RCT with 80 colchicine and 80 control patients, showing improved recovery with treatment. SOC included vitamin C, vitamin D, and zinc.
0 0.5 1 1.5 2+ Mortality 54% Improvement Relative Risk c19early.org/o Hueda-Zavaleta et al. Colchicine for COVID-19 LATE Favors colchicine Favors control
[Hueda-Zavaleta] Retrospective 450 late stage (median oxygen saturation 86%) COVID+ hospitalized patients in Peru, showing lower mortality with colchicine treatment.
0 0.5 1 1.5 2+ Mortality 68% Improvement Relative Risk c19early.org/o Hunt et al. Colchicine for COVID-19 EARLY TREATMENT Favors colchicine Favors control
[Hunt] Retrospective 26,508 consecutive COVID+ veterans in the USA, showing lower mortality with multiple treatments including colchicine. Treatment was defined as drugs administered ≥50% of the time within 2 weeks post-COVID+, and may be a continuation of prophylactic treatment in some cases, and may be early or late treatment in other cases. Further reduction in mortality was seen with combinations of treatments.
0 0.5 1 1.5 2+ Hospitalization time 24% Improvement Relative Risk c19early.org/o Jalal et al. NCT04867226 Colchicine RCT LATE TREATMENT Favors colchicine Favors control
[Jalal] Open label RCT of colchicine showing improved recovery with treatment. Only the abstract is currently available. Colchicine 0.5mg bid for 14 days.
0 0.5 1 1.5 2+ Mortality 13% Improvement Relative Risk ICU admission 16% Hospitalization time 25% c19early.org/o Karakaş et al. Colchicine for COVID-19 LATE TREATMENT Favors colchicine Favors control
[Karakaş] Retrospective 356 hospitalized COVID-19 patients, shorter hospitalization time with colchicine treatment. There were no statistically significant differences for mortality or ICU admission. Significantly lower mortality was seen with higher dosage (1mg/day vs 0.5mg/day). More control patients were on oxygen at baseline (65% vs. 54%).
0 0.5 1 1.5 2+ Mortality, ventilation, or.. 96% Improvement Relative Risk c19early.org/o Kevorkian et al. Colchicine for COVID-19 LATE Favors colchicine Favors control
[Kevorkian] Observational study in France with 28 hospitalized patients treated with prednisone/furosemide/colchicine/salicylate/direct anti-Xa inhibitor, and 40 control patients, showing lower combined mortality, ventilation, or high-flow oxygen therapy with treatment.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk ICU admission 50% Hospitalization time 22% c19early.org/o Lopes et al. Colchicine for COVID-19 RCT LATE TREATMENT Favors colchicine Favors control
[Lopes] RCT with 36 colchicine and 36 control patients, showing reduced length of hospitalization and oxygen therapy with treatment.
0 0.5 1 1.5 2+ Mortality -7% Improvement Relative Risk c19early.org/o Mahale et al. Colchicine for COVID-19 LATE TREATMENT Favors colchicine Favors control
[Mahale] Retrospective 134 hospitalized COVID-19 patients in India, showing no significant difference with colchicine treatment in unadjusted results.
0 0.5 1 1.5 2+ Mortality 76% Improvement Relative Risk Recovery 44% c19early.org/o Manenti et al. Colchicine for COVID-19 LATE TREATMENT Favors colchicine Favors control
[Manenti] IPTW retrospective 141 COVID-19 patients (83% hospitalized), 71 treated with colchicine and 70 matched control patients, showing lower mortality and faster recovery with treatment.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk ΔSHOCS-COVID 50% primary SHOCS-COVID 71% NEWS-2 67% Hospitalization time 26% c19early.org/o Mareev et al. Colchicine for COVID-19 LATE TREATMENT Favors colchicine Favors control
[Mareev] Small trial with 21 colchicine patients and 22 control patients in Russia, showing improved recovery with treatment. The trial was originally an RCT, however randomization to the control arm was stopped after 5 patients, and 17 retrospective patients were added for comparison.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk c19early.org/o Monserrat Villatoro et al. Colchicine Prophylaxis Favors colchicine Favors control
[Monserrat Villatoro] PSM retrospective 3,712 hospitalized patients in Spain, showing lower mortality with existing use of azithromycin, bemiparine, budesonide-formoterol fumarate, cefuroxime, colchicine, enoxaparin, ipratropium bromide, loratadine, mepyramine theophylline acetate, oral rehydration salts, and salbutamol sulphate, and higher mortality with acetylsalicylic acid, digoxin, folic acid, mirtazapine, linagliptin, enalapril, atorvastatin, and allopurinol.
0 0.5 1 1.5 2+ Mortality 83% primary Improvement Relative Risk Hospitalization time 35% c19early.org/o Mostafaie et al. NCT04392141 Colchicine RCT LATE Favors colchicine Favors control
[Mostafaie] RCT with 60 patients treated with colchicine and phenolic monoterpenes and 60 control patients in Iran, showing lower mortality with treatment. NCT04392141.
0 0.5 1 1.5 2+ Hospitalization -406% Improvement Relative Risk Symptomatic case -73% Case -24% c19early.org/o Oztas et al. Colchicine for COVID-19 Prophylaxis Favors colchicine Favors control
[Oztas] Retrospective 635 HCQ users and 643 household contacts, showing higher risk with colchicine in unadjusted results.

Patients with conditions leading to the use of colchicine may have significantly different baseline risk, e.g. [Topless].
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk Ventilation 80% ICU admission 51% 7-point scale 87% primary 7-point scale (b) 80% Hospitalization time -15% c19early.org/o Pascual-Figal et al. NCT04350320 Colchicine RCT LATE Favors colchicine Favors control
[Pascual-Figal] RCT with 52 colchicine patients and 51 control patients, showing lower risk of clinical deterioration with treatment. COL-COVID. NCT04350320.
0 0.5 1 1.5 2+ Mortality -36% Improvement Relative Risk Progression -7% primary Ventilation -30% ICU admission 76% Hospitalization time 4% c19early.org/o Perricone et al. NCT04375202 COLVID-19 Colchicine RCT LATE Favors colchicine Favors control
[Perricone] RCT 152 hospitalized patients in Italy, showing no significant difference in outcomes with colchicine treatment. Table 2 shows 13% of patients treated with antivirals in the colchicine arm, however 16.9% were treated with one specific antiviral (HCQ).
0 0.5 1 1.5 2+ Mortality 79% Improvement Relative Risk Improvement 85% c19early.org/o Pimenta Bonifácio et al. NCT04724629 Colchicine RCT LATE Favors colchicine Favors control
[Pimenta Bonifácio] Open label RCT late stage hospitalized patients in Brazil with 14 colchicine and 16 SOC patients, showing lower mortality and improved recovery with treatment, without statistical significance. Authors note that the colchicine group had one patient with SOFA ≥7 vs. zero for SOC, however both groups had one patient intubated and SOC had more patients not requiring high-flow oxygen (12 vs. 8).
0 0.5 1 1.5 2+ Mortality 35% Improvement Relative Risk c19early.org/o Pinzón et al. Colchicine for COVID-19 LATE TREATMENT Favors colchicine Favors control
[Pinzón] Retrospective 301 pneumonia patients in Colombia showing lower mortality with colchicine treatment.
0 0.5 1 1.5 2+ Hospitalization 73% Improvement Relative Risk Improvement in dyspnea 38% Improvement in Ct score 22% c19early.org/o Pourdowlat et al. Colchicine for COVID-19 RCT LATE Favors colchicine Favors control
[Pourdowlat] RCT 202 patients in Iran, 102 treated with colchicine, showing lower hospitalization and improved clinical outcomes with treatment.
0 0.5 1 1.5 2+ Mortality, day 28 71% Improvement Relative Risk Progression, day 28 71% Mortality, day 14 61% Ventilation 51% Progression, day 14 56% primary c19early.org/o Rahman et al. NCT04527562 Colchicine RCT LATE TREATMENT Favors colchicine Favors control
[Rahman] RCT 300 patients in Bangladesh, published 2 years after completion, showing significantly lower mortality with treatment at 28 days (not significant at 14 days). 1.2mg colchicine on day 1 followed by 0.6mg for 13 days.
0 0.5 1 1.5 2+ Mortality -1% Improvement Relative Risk Ventilation -18% Death/intubation -2% Discharge -2% c19early.org/o Recovery Collaborative Group et al. NCT04381936 RECOVERY Colchicine RCT LATE Favors colchicine Favors control
[Recovery Collaborative Group] RCT with 5,610 colchicine and 5,730 control patients showing mortality RR 1.01 [0.93-1.10]. Very late stage treatment, median 9 days after symptom onset. Baseline oxygen requirements unknown (data is provided but combined with "none"). ISRCTN 50189673.
0 0.5 1 1.5 2+ Mortality 6% Improvement Relative Risk c19early.org/o Rodriguez-Nava et al. Colchicine for COVID-19 LATE Favors colchicine Favors control
[Rodriguez-Nava] Retrospective 313 patients, mostly critical stage and mostly requiring respiratory support. Confounding by indication likely.
0 0.5 1 1.5 2+ Hospitalization time 23% Improvement Relative Risk c19early.org/o Salehzadeh et al. IRCT20200418047126N1 Colchicine RCT LATE Favors colchicine Favors control
[Salehzadeh] Open label RCT with 100 hospitalized patients in Iran, 50 treated with colchicine, showing shorter hospitalization time with treatment. There were no deaths.
0 0.5 1 1.5 2+ Mortality 42% Improvement Relative Risk Ventilation 53% Discharge 42% Hospitalization time 5% no CI c19early.org/o Sandhu et al. Colchicine for COVID-19 LATE TREATMENT Favors colchicine Favors control
[Sandhu] Prospective cohort study of hospitalized patients in the USA, 34 treated with colchicine, showing lower mortality and intubation with treatment.
0 0.5 1 1.5 2+ Mortality 85% Improvement Relative Risk c19early.org/o Scarsi et al. Colchicine for COVID-19 LATE TREATMENT Favors colchicine Favors control
[Scarsi] Retrospective 122 colchicine patients and 140 control patients in Italy, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality 44% Improvement Relative Risk Death/hospitalization 20% primary Ventilation 47% Hospitalization 20% c19early.org/o Tardif et al. NCT04322682 COLCORONA Colchicine RCT LATE Favors colchicine Favors control
[Tardif] RCT for relatively low risk outpatients, 2235 treated with colchicine a mean of 5.3 days after the onset of symptoms, and 2253 controls, showing lower mortality, ventilation, and hospitalization with treatment.
0 0.5 1 1.5 2+ Mortality 23% Improvement Relative Risk c19early.org/o Topless et al. Colchicine for COVID-19 Prophylaxis Favors colchicine Favors control
[Topless] UK Biobank retrospective showing a higher risk of COVID-19 cases and mortality for patients with gout. Among patients with gout, mortality risk was lower for those on colchicine, OR 1.06 [0.60-1.89], compared to those without colchicine, OR 1.38 [1.08-1.76].
0 0.5 1 1.5 2+ Mortality 23% Improvement Relative Risk ICU time 40% c19early.org/o Valerio Pascua et al. Colchicine for COVID-19 ICU Favors colchicine Favors control
[Valerio Pascua] Retrospective 65 ICU patients in the USA and Honduras, showing shorter ICU stay with combined treatment including colchicine, LMWH, tocilizumab, dexamethasone, and methylprednisolone.
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 colchicine, 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 colchicine 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 (3.10.8) 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/ometa.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.
[Hunt], 6/29/2022, retrospective, USA, peer-reviewed, 8 authors, study period 1 March, 2020 - 10 September, 2020, dosage not specified. risk of death, 68.0% lower, RR 0.32, p = 0.003, treatment 9 of 402 (2.2%), control 1,603 of 26,106 (6.1%), NNT 26, adjusted per study, day 30.
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.
[Absalón-Aguilar], 11/9/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, 18 authors, dosage 1.5mg day 1, 1mg days 2-10. risk of death, 28.6% lower, RR 0.71, p = 0.74, treatment 4 of 56 (7.1%), control 6 of 60 (10.0%), NNT 35.
progression to critical or death, 17.0% lower, OR 0.83, p = 0.67, treatment 56, control 60, primary outcome, RR approximated with OR.
risk of no recovery, 13.0% higher, RR 1.13, p = 0.59, treatment 56, control 60, Kaplan–Meier.
[Alsultan], 12/31/2021, Randomized Controlled Trial, Syria, peer-reviewed, 11 authors, dosage 2mg day 1, 1mg days 2-5. risk of death, 35.7% lower, RR 0.64, p = 0.70, treatment 3 of 14 (21.4%), control 7 of 21 (33.3%), NNT 8.4.
[Brunetti], 9/14/2020, retrospective, propensity score matching,