Dexamethasone for COVID-19: real-time meta-analysis of 15 studies

@CovidAnalysis, October 2026, Version 18V18 (V1 2025)
Abstract
Meta-analysis using the most serious outcome reported shows 11% [-4‑28%] higher risk, without reaching statistical significance.
While overall meta-analysis shows poor results, dexamethasone shows lower risk for a subgroup of very late-stage patients with severe disease1,2. Dexamethasone is a highly potent synthetic corticosteroid that broadly suppresses immune function and attenuates pro-inflammatory signaling. It may also impair the innate antiviral response. Early use, when viral replication dominates, may therefore worsen outcomes. However, for late-stage dysregulated hyperinflammatory responses, beneficial immunosuppression may outweigh that cost. RECOVERY showed lower mortality in mechanically ventilated COVID-19 patients, consistent with pre-COVID-19 evidence from the DEXA-ARDS RCT that shows lower mortality in mechanically ventilated ARDS patients. However, RECOVERY and other studies show higher risk in non-hypoxic patients. The evidence for benefit in patients on less-intensive forms of respiratory support remains unclear. The RECOVERY "oxygen only" subgroup combines patients at many different levels including high-flow oxygen and NIV, and authors do not provide analysis for patients at different baseline levels of respiratory support, or provide details of potential rapid progression to more intensive forms.
Corticosteroids for COVID-19 carry harms - broad immunosuppression that impairs viral clearance and host defenses, hyperglycemia, impaired healing, and myopathy, offset by an anti-inflammatory benefit that depends on how much steroid-suppressible inflammation is driving lung injury. While guidelines often tie treatment to the level of oxygen support, this is a crude proxy for the underlying inflammatory biology. Measures of steroid-suppressible inflammation may be used to more accurately predict the benefit-harm crossover point for each patient.
All data and sources to reproduce this analysis are in the appendix.
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Evolution of COVID-19 clinical evidence Meta-analysis results over time Dexamethasone p=0.15 Vitamin D p<0.0000000001 2020 2021 2022 2023 2024 2025 2026 Lowerrisk Higherrisk c19early.org October 2026 50% 0% -50%
Dexamethasone for COVID-19 — Highlights
Meta-analysis of studies to date shows no significant improvements with dexamethasone.
Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
c19early.org
October 2026

Dexamethasone COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Kocks (DB RCT)−300%4.00 · 0.27–58.22hosp.2/40/2COPPER
Early treatment−300%4.00 · 0.27–58.222/40/2300% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.31
Tomazini (RCT)3%0.97 · 0.72–1.31death85/15191/148CoDEX Horby (RCT)13%0.87 · 0.79–0.96death596/2,1041,297/4,321RECOVERY Jamaati (RCT)−7%1.07 · 0.69–1.65death16/2515/25 Munch (DB RCT)13%0.87 · 0.70–1.07death157/490180/478LD1 Crothers (PSW)−59%1.59 · 1.39–1.81death7,507 (n)7,443 (n) Maskin (RCT)10%0.90 · 0.79–1.02death49 (n)49 (n)LD1 Wu (RCT)−133%2.33 · 0.87–6.24death11/525/55LD1 Mourad10%0.90 · 0.86–0.95death Horby (RCT)−15%1.15 · 0.95–1.39death241/905200/844RECOVERY high-doseLD1 Franco-Mor.. (RCT)−134%2.34 · 0.45–12.34ventilation4/582/68EARLY-DEX Yen−103%2.03 · 1.40–2.94death572 (n)1,624 (n) Bhat (PSM)−35%1.35 · 0.88–2.07death46/52934/529 Garneau−31%1.31 · 0.09–19.00death1/131/17 Bepouka−104%2.04 · 0.20–25.00death70 (n)340 (n)
Late treatment−11%1.11 · 0.96–1.271,157/12,5251,825/15,94111% higher risk
Tau² = 0.04, I² = 86.5%, p = 0.16
All studies−11%1.11 · 0.96–1.281,159/12,5291,825/15,94311% higher risk
Tau² = 0.04, I² = 85.7%, p = 0.1500.511.52+
1 LD: comparison with low dose treatment
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
B
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Fig. 1. A. Random-effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix. B. Timeline of results in dexamethasone studies.
Introduction
Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from3.
Immediate treatment recommended
SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury4-20 and cognitive deficits7,12, cardiovascular complications21-27, DNA damage28-31, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits32—the spike protein binds to fibrin leading to fibrinolysis-resistant blood clots, thromboinflammation, and neuropathology. Minimizing replication as early as possible is recommended.
Many treatments are expected to modulate infection
SARS-CoV-2 infection and replication involves the complex interplay of 500+ host and viral proteins and other factorsA,33-40, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk41, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
Analysis
We analyze all significant controlled studies of dexamethasone 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, and Randomized Controlled Trials (RCTs).
Treatment timing
Fig. 3 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.
Prophylaxis pre-exposure post-exposure Early treatment Late treatment Treatment delay viral load disease severity exposed symptom onset hospitalized ICU Expected benefit of antiviral therapy −2 0 2 4 6 8 10 Time relative to symptom onset (days) trajectories vary widely immediately or soon after symptoms after disease progression
Fig. 3. Treatment stages.
Results
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, and for specific outcomes. Table 2 shows results by treatment stage. Fig. 4 plots individual results by treatment stage. Fig. 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, recovery, and high vs. low-dose studies.
Table 1. Random-effects meta-analysis for all stages combined, for Randomized Controlled Trials, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval. ** p<0.01.
Relative Risk Studies Patients
All studies1.11 [0.96‑1.28]1520K
RCTsRCTs0.96 [0.86‑1.06]99,828
Mortality1.10 [0.96‑1.27]1320K
VentilationVent.1.05 [0.71‑1.56]46,866
ICU admissionICU4.51 [0.80‑25.54]2156
HospitalizationHosp.1.51 [0.66‑3.44]3162
Recovery1.67 [0.27‑10.39]26,431
RCT mortality0.95 [0.86‑1.05]79,696
Table 2. Random-effects meta-analysis results by treatment stage. Results show the relative risk with treatment and the 95% confidence interval.treatment and the 95% confidence interval. ** p<0.01.
Early treatment Late treatment
All studies4.00 [0.27‑58.22]4.00
[0.27‑58.22]		1.11 [0.96‑1.27]1.11
[0.96‑1.27]
RCTsRCTs4.00 [0.27‑58.22]4.00
[0.27‑58.22]		0.96 [0.86‑1.06]0.96
[0.86‑1.06]
Mortality1.10 [0.96‑1.27]1.10
[0.96‑1.27]
VentilationVent.1.05 [0.71‑1.56]1.05
[0.71‑1.56]
ICU admissionICU4.51 [0.80‑25.54]4.51
[0.80‑25.54]
HospitalizationHosp.4.00 [0.27‑58.22]4.00
[0.27‑58.22]		1.40 [0.56‑3.52]1.40
[0.56‑3.52]
Recovery7.00 [0.55‑89.40]7.00
[0.55‑89.40]		0.91 [0.85‑0.97]**0.91**
[0.85‑0.97]
RCT mortality0.95 [0.86‑1.05]0.95
[0.86‑1.05]
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Fig. 4. 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.
c19early.org
October 2026

Dexamethasone COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Kocks (DB RCT)−300%4.00 · 0.27–58.22hosp.2/40/2COPPER
Early treatment−300%4.00 · 0.27–58.222/40/2300% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.31
Tomazini (RCT)3%0.97 · 0.72–1.31death85/15191/148CoDEX Horby (RCT)13%0.87 · 0.79–0.96death596/2,1041,297/4,321RECOVERY Jamaati (RCT)−7%1.07 · 0.69–1.65death16/2515/25 Munch (DB RCT)13%0.87 · 0.70–1.07death157/490180/478LD1 Crothers (PSW)−59%1.59 · 1.39–1.81death7,507 (n)7,443 (n) Maskin (RCT)10%0.90 · 0.79–1.02death49 (n)49 (n)LD1 Wu (RCT)−133%2.33 · 0.87–6.24death11/525/55LD1 Mourad10%0.90 · 0.86–0.95death Horby (RCT)−15%1.15 · 0.95–1.39death241/905200/844RECOVERY high-doseLD1 Franco-Mor.. (RCT)−134%2.34 · 0.45–12.34ventilation4/582/68EARLY-DEX Yen−103%2.03 · 1.40–2.94death572 (n)1,624 (n) Bhat (PSM)−35%1.35 · 0.88–2.07death46/52934/529 Garneau−31%1.31 · 0.09–19.00death1/131/17 Bepouka−104%2.04 · 0.20–25.00death70 (n)340 (n)
Late treatment−11%1.11 · 0.96–1.271,157/12,5251,825/15,94111% higher risk
Tau² = 0.04, I² = 86.5%, p = 0.16
All studies−11%1.11 · 0.96–1.281,159/12,5291,825/15,94311% higher risk
Tau² = 0.04, I² = 85.7%, p = 0.1500.511.52+
1 LD: comparison with low dose treatment
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 5. Random-effects meta-analysis for all studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
c19early.org
October 2026

Dexamethasone COVID-19 mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk
Tomazini (RCT)3%0.97 · 0.72–1.3185/15191/148CoDEX Horby (RCT)13%0.87 · 0.79–0.96596/2,1041,297/4,321RECOVERY Jamaati (RCT)−7%1.07 · 0.69–1.6516/2515/25 Munch (DB RCT)13%0.87 · 0.70–1.07157/490180/478LD1 Crothers (PSW)−59%1.59 · 1.39–1.817,507 (n)7,443 (n) Maskin (RCT)10%0.90 · 0.79–1.0249 (n)49 (n)LD1 Wu (RCT)−133%2.33 · 0.87–6.2411/525/55LD1 Mourad10%0.90 · 0.86–0.95 Horby (RCT)−15%1.15 · 0.95–1.39241/905200/844RECOVERY high-doseLD1 Yen−103%2.03 · 1.40–2.94572 (n)1,624 (n) Bhat (PSM)−35%1.35 · 0.88–2.0746/52934/529 Garneau−31%1.31 · 0.09–19.001/131/17 Bepouka−104%2.04 · 0.20–25.0070 (n)340 (n)
Late treatment−10%1.10 · 0.96–1.271,153/12,4671,823/15,87310% higher risk
Tau² = 0.04, I² = 87.4%, p = 0.18
All studies−10%1.10 · 0.96–1.271,153/12,4671,823/15,87310% higher risk
Tau² = 0.04, I² = 87.4%, p = 0.1800.511.52+
1 LD: comparison with low dose treatment
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 6. Random-effects meta-analysis for mortality results.
c19early.org
October 2026

Dexamethasone COVID-19 mechanical ventilation results

StudyImprovementRR · 95% CITreatmentControlRelative Risk
Horby (RCT)21%0.79 · 0.64–0.97110/1,780298/3,638RECOVERY Jamaati (RCT)−18%1.18 · 0.66–2.1113/2511/25 Horby (RCT)−46%1.46 · 0.75–2.8322/65914/613RECOVERY high-doseLD1 Franco-Mor.. (RCT)−134%2.34 · 0.45–12.344/582/68EARLY-DEX
Late treatment−5%1.05 · 0.71–1.56149/2,522325/4,3445% higher risk
Tau² = 0.07, I² = 46.3%, p = 0.81
All studies−5%1.05 · 0.71–1.56149/2,522325/4,3445% higher risk
Tau² = 0.07, I² = 46.3%, p = 0.8100.511.52+
1 LD: comparison with low dose treatment
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 7. Random-effects meta-analysis for ventilation.
c19early.org
October 2026

Dexamethasone COVID-19 ICU results

StudyImprovementRR · 95% CITreatmentControlRelative Risk
Franco-Mor.. (RCT)−217%3.17 · 0.13–76.421/580/68EARLY-DEX Garneau−423%5.23 · 0.66–41.414/131/17
Late treatment−351%4.51 · 0.80–25.545/711/85351% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.088
All studies−351%4.51 · 0.80–25.545/711/85351% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.08800.511.52+
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 8. Random-effects meta-analysis for ICU admission.
c19early.org
October 2026

Dexamethasone COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Kocks (DB RCT)−300%4.00 · 0.27–58.22hosp.2/40/2COPPER
Early treatment−300%4.00 · 0.27–58.222/40/2300% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.31
Franco-Mor.. (RCT)3%0.97 · 0.67–1.39hosp. time58 (n)68 (n)EARLY-DEX Garneau−155%2.55 · 0.95–6.83hosp. time13 (n)17 (n)
Late treatment−40%1.40 · 0.56–3.5271 (n)85 (n)40% higher risk
Tau² = 0.32, I² = 69.1%, p = 0.48
All studies−51%1.51 · 0.66–3.442/750/8751% higher risk
Tau² = 0.27, I² = 51.6%, p = 0.3300.511.52+
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 9. Random-effects meta-analysis for hospitalization.
c19early.org
October 2026

Dexamethasone COVID-19 recovery results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Kocks (DB RCT)−600%7.00 · 0.55–89.40no recov.4/40/2COPPER
Early treatment−600%7.00 · 0.55–89.404/40/2600% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.13
Horby (RCT)9%0.91 · 0.85–0.97no disch.2,104 (n)4,321 (n)RECOVERY
Late treatment9%0.91 · 0.85–0.972,104 (n)4,321 (n)9% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.0034
All studies−67%1.67 · 0.27–10.394/2,1080/4,32367% higher risk
Tau² = 1.24, I² = 59.4%, p = 0.600.511.52+
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 10. Random-effects meta-analysis for recovery.
c19early.org
October 2026

Dexamethasone COVID-19 studies comparing high vs. low doses

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Munch (DB RCT)13%0.87 · 0.70–1.07death157/490180/478LD1 Maskin (RCT)10%0.90 · 0.79–1.02death49 (n)49 (n)LD1 Wu (RCT)−133%2.33 · 0.87–6.24death11/525/55LD1 Horby (RCT)−15%1.15 · 0.95–1.39death241/905200/844RECOVERY high-doseLD1
Late treatment1%0.99 · 0.83–1.19409/1,496385/1,4261% lower risk
Tau² = 0.02, I² = 70.3%, p = 0.94
All studies1%0.99 · 0.83–1.19409/1,496385/1,4261% lower risk
Tau² = 0.02, I² = 70.3%, p = 0.9400.511.52+
1 LD: comparison with low dose treatment
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 11. Random-effects meta-analysis for high vs. low-dose studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below.
Randomized Controlled Trials (RCTs)
Fig. 12 shows a comparison of results for RCTs and observational studies. Fig. 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.
Loading..
Fig. 12. Results for RCTs and observational studies.
RCTs have many potential biases
RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases42, and analysis of double-blind RCTs has identified extreme levels of bias43. 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, reporting, 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.
Conflicts of interest for COVID-19 RCTs
RCTs are expensive and many RCTs are funded by pharmaceutical companies or other organizations with conflicts of interest, for example governments that previously denied treatment with the study drug. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. Bekelman et al. and Lundh et al. show that industry-sponsored studies are more likely to be favorable. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.
RCTs for novel acute diseases requiring rapid treatment
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 227 treatments we have analyzed, 67% 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.
RCTs may be less reliable
Concato et al. report a paradoxical finding—RCT results had higher variability, and only RCTs were found to sometimes report significant results the opposite of the overall result. The same trend is seen for the most popular (most politicized) COVID-19 treatments—considering all statistically significant results reported in studies, RCTs are slightly more likely to report a result in the opposite direction. In other words, for these COVID-19 treatments and for the topics covered by Concato et al., assuming causality from a single study is more likely to result in an incorrect conclusion for RCTs.
Increased risk of inconsistent results for RCTs suggests higher prevalence of bias, which may arise due to many issues including design bias, conflicts of interest, treatment differences by physicians aware of allocation, attrition bias, ascertainment bias, randomization failures, errors, or fraud.
Using all studies identifies efficacy 8+ months faster (9+ months for low-cost treatments)
Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 54% have been confirmed in RCTs, with a mean delay of 7.8 months (62% with 8.7 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
All studies must be carefully analyzed
Neither observational studies nor RCTs prove causation—any study can be flawed or fraudulent. We need much more, for example a combination of results from many independent teams, detailed understanding of each study, knowledge of conflicts/team reliability, dose-response relationships, delay-response relationships, logical results across outcomes, or details consistent with preclinical expectations.
All studies must be evaluated individually. 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.
c19early.org
October 2026

Dexamethasone COVID-19 Randomized Controlled Trials

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Kocks (DB RCT)−300%4.00 · 0.27–58.22hosp.2/40/2COPPER
Early treatment−300%4.00 · 0.27–58.222/40/2300% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.31
Tomazini (RCT)3%0.97 · 0.72–1.31death85/15191/148CoDEX Horby (RCT)13%0.87 · 0.79–0.96death596/2,1041,297/4,321RECOVERY Jamaati (RCT)−7%1.07 · 0.69–1.65death16/2515/25 Munch (DB RCT)13%0.87 · 0.70–1.07death157/490180/478LD1 Maskin (RCT)10%0.90 · 0.79–1.02death49 (n)49 (n)LD1 Wu (RCT)−133%2.33 · 0.87–6.24death11/525/55LD1 Horby (RCT)−15%1.15 · 0.95–1.39death241/905200/844RECOVERY high-doseLD1 Franco-Mor.. (RCT)−134%2.34 · 0.45–12.34ventilation4/582/68EARLY-DEX
Late treatment4%0.96 · 0.86–1.061,110/3,8341,790/5,9884% lower risk
Tau² = 0.01, I² = 53.1%, p = 0.4
All studies4%0.96 · 0.86–1.061,112/3,8381,790/5,9904% lower risk
Tau² = 0.01, I² = 50.3%, p = 0.4300.511.52+
1 LD: comparison with low dose treatment
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 13. Random-effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
c19early.org
October 2026

Dexamethasone COVID-19 RCT mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk
Tomazini (RCT)3%0.97 · 0.72–1.3185/15191/148CoDEX Horby (RCT)13%0.87 · 0.79–0.96596/2,1041,297/4,321RECOVERY Jamaati (RCT)−7%1.07 · 0.69–1.6516/2515/25 Munch (DB RCT)13%0.87 · 0.70–1.07157/490180/478LD1 Maskin (RCT)10%0.90 · 0.79–1.0249 (n)49 (n)LD1 Wu (RCT)−133%2.33 · 0.87–6.2411/525/55LD1 Horby (RCT)−15%1.15 · 0.95–1.39241/905200/844RECOVERY high-doseLD1
Late treatment5%0.95 · 0.86–1.051,106/3,7761,788/5,9205% lower risk
Tau² = 0.01, I² = 56.2%, p = 0.36
All studies5%0.95 · 0.86–1.051,106/3,7761,788/5,9205% lower risk
Tau² = 0.01, I² = 56.2%, p = 0.3600.511.52+
1 LD: comparison with low dose treatment
Rotate screen for more detailsIncrease width for more details
← Dexamethasone
reduces riskDexamethasone
increases risk →
Fig. 14. Random-effects meta-analysis for RCT mortality results.
Severe/Critical Patients Only
Dexamethasone is a highly potent synthetic corticosteroid that broadly suppresses immune function and attenuates pro-inflammatory signaling. When administered during the late-stage immunopathological phase - when dysregulated, tissue-damaging inflammation dominates over active viral replication - this immunosuppression may be a vital rescue mechanism. The RECOVERY trial1, and the pre-pandemic DEXA-ARDS trial48, show that dexamethasone significantly lowers mortality risk for late-stage patients. However, if used earlier in the active viral replication phase, dexamethasone may impair the innate antiviral response, prolong viremia, and result in a higher risk of clinical deterioration and death.
Specifically, dexamethasone shows a significant mortality benefit in the RECOVERY trial low-dose arm1, however this benefit has not been replicated in any of the other 8 RCTs. Dexamethasone 6 mg daily for up to ten days reduced mortality in patients hospitalised with COVID-19 who require invasive mechanical ventilation1. This finding is consistent with prior evidence of benefit in mechanically ventilated ARDS patients from the pre-COVID-19 DEXA-ARDS trial48. In patients not requiring supplemental oxygen at randomization, dexamethasone showed a point estimate toward harm at both 28 days1 and 6 months49, consistent with observational data from a large VA cohort showing increased mortality with dexamethasone in non-hypoxic patients50. The higher-dose RECOVERY substudy51 further demonstrated that escalating corticosteroids in patients on simple oxygen significantly increased mortality, reinforcing that more immunosuppression is harmful in less severely ill patients.
The evidence for benefit in hypoxic patients not progressing to ventilation is relatively weak. RECOVERY reported a statistically significant mortality reduction in their "oxygen only" subgroup, RR 0.87 [0.77-0.99] at 6 months49, but this category combined patients on simple nasal cannula with those receiving high-flow nasal oxygen, CPAP, and other non-invasive ventilation. The randomization form used during the dexamethasone comparison made no distinction between these groups. Patients on NIV or high-flow oxygen are substantially sicker and physiologically closer to mechanically ventilated patients, and the observed benefit could plausibly be driven by this subpopulation. Further, subgroup classification was based on respiratory support at randomization, and authors do not provide details of potential rapid progression to more intensive forms. The VA observational cohort found no mortality benefit among patients on low-flow nasal cannula, HR 1.08 [0.86-1.36]50. Taken together, while there is evidence supporting dexamethasone in mechanically ventilated patients, this was already known for ARDS patients prior to COVID-19 from the DEXA-ARDS trial48. RECOVERY suggests some benefit for those on intensive respiratory support such as high-flow oxygen or NIV, however the case for benefit in patients requiring only modest supplemental oxygen is not clear from the published analyses. The RECOVERY team could provide a more definitive answer with more detailed analysis.
Depending on the point at which harm turns to benefit, patients may be classified as "severe" or "critical", however we note that patients requiring supplemental oxygen are already in a critical situation in the sense that if a patient has reached this stage then treatment has failed - there has already been significant progression, and even with recovery there is significant long-term morbidity risk.
c19early.org
Levels of respiratory support
Respiratory support ranges from minimal ward-level oxygen supplementation to mechanical ventilation. The RECOVERY trial's "oxygen only" subgroup combined all levels below invasive mechanical ventilation.
Delivery method Typical flow / pressure Usual setting Intensity
Low-flow nasal cannula Prongs in nostrils 1–6 L/min Ward Low
Simple face mask Mask over nose and mouth 5–10 L/min Ward Low–moderate
Non-rebreather mask Mask with reservoir bag 12–15 L/min Ward / step-down Moderate
Venturi mask Mask with fixed FiO2 adapter Variable, precise FiO2 Ward Moderate
High-flow nasal cannula (HFNC) Wide-bore nasal prongs, heated humidified circuit 20–60 L/min, up to 100% FiO2 Step-down / ICU Moderate–high
CPAP Tight-fitting mask, continuous positive pressure 5–15 cmH2O ICU / step-down High
BiPAP / bilevel NIV Tight-fitting mask, two pressure levels (inspiratory and expiratory) IPAP 10–25, EPAP 5–10 cmH2O ICU / step-down / respiratory ward High
Invasive mechanical ventilation Endotracheal tube connected to ventilator Fully controlled by ventilator ICU Very high
ECMO Cannulae in great vessels, external membrane oxygenator Bypass of lungs partially or fully ICU Maximal
Table 3. Levels of respiratory support.
Corticosteroids in COVID-19 and ARDS carry harms - broad immunosuppression that impairs viral clearance and host defenses (driving secondary bacterial and fungal infection), hyperglycemia, impaired healing, and myopathy, offset by an anti-inflammatory benefit that depends on how much steroid-suppressible inflammation is driving lung injury.
Sines et al. analyzed 429 mechanically ventilated patients with severe COVID-19 in the USA, showing that corticosteroids were associated with lower 28-day mortality in patients with a predicted Hyper-Inflammatory endotype, but showed higher risk, without statistical significance, in those with a predicted Metabolic Dysregulation endotype.
Studies show that there is a crossover point where treatment goes from harmful to beneficial. Guidelines typically tie the treatment decision to the level of oxygen support, however this is a crude proxy for the underlying inflammatory biology. Sines et al. show that even within a ventilated population, a predicted inflammatory endotype can classify patients into groups with predicted benefit or no benefit.
A natural refinement is to model each patient's treatment effect as a function of oxygen requirements and a continuous inflammatory score, where each patient has their own benefit-harm crossover point. For example, a strongly hyperinflammatory patient might warrant treatment at high-flow oxygen, while a metabolically-dysregulated patient may cross into net benefit only if ventilated, or not at all.
Note that there are more directly measured indicators of steroid-suppressible inflammation than the predicted endotype used by Sines et al., spanning single systemic biomarkers (CRP/NLR), multi-analyte plasma subphenotypes, and airway/lung molecular profiling. These may improve treatment decisions, though which is most predictive of steroid response remains to be established.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay
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 hours53,54. Baloxavir marboxil studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. 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 et al. report only 2.5 hours improvement for inpatient treatment.
Table 4. Studies of baloxavir marboxil for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases55
<24 hours-33 hours symptoms56
24-48 hours-13 hours symptoms56
Inpatients-2.5 hours to improvement57
Fig. 15 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 227 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Fig. 15. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.
Patient demographics
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, for example as in López-Medina et al.
SARS-CoV-2 variants
Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants59, for example the Gamma variant shows significantly different characteristics60-63. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants64,65.
Treatment regimen
Effectiveness may depend strongly on the dosage and treatment regimen.
Medication quality
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
Other treatments
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic68-92, therefore efficacy may depend strongly on combined treatments.
Effect measured
Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.
Meta-analysis
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. 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.
Pooled Effects
Combining studies is required
For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results. Pooling the results of studies reporting different outcomes allows us to use more of the available information. Logically we should, and do, use additional information when evaluating treatments—for example dose-response and treatment delay-response relationships provide additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
Specific outcome and pooled analyses
We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.
Ethical and practical issues limit high-risk trials
Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster and safer collection of evidence.
Validating pooled outcome analysis for COVID-19
For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from all 227 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Fig. 16 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 17 shows that improved recovery is very strongly associated with lower mortality (p < 0.0000000001). Considering the extremes, Singh et al. show an association between viral clearance and hospitalization or death, with p = 0.003 after excluding one large outlier from a mutagenic treatment, and based on 44 RCTs including 52,384 patients. Fig. 18 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.0000000074 to p = 0.00000000015.
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Fig. 16. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 17. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 16. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Pooled outcomes identify efficacy 5 months faster (7 months for RCTs)
Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months. Fig. 19 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Fig. 19. 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.
Limitations
Pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral clearance may show no efficacy if most studies only examine viral clearance. In practice, it is rare for a non-antiviral treatment to report viral clearance and to not report clinical outcomes; and in practice other sources of heterogeneity such as differences in treatment delay are more likely to hide efficacy.
Summary
Analysis validates the use of pooled effects and shows significantly faster detection of efficacy on average. However, as with all meta-analyses, it is important to review the different studies included. We also present individual outcome analyses, which may be more informative for specific use cases.
Discussion
Publication bias
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 results94-97. For dexamethasone, there is currently not enough data to evaluate publication bias with high confidence.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Fig. 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 < 0.0598-105. 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.
Log Risk Ratio Standard Error 1.406 1.055 0.703 0.352 0 -3 -2 -1 0 1 2 A: Simulated perfect trials p > 0.05 Log Risk Ratio Standard Error 1.433 1.074 0.716 0.358 0 -4 -3 -2 -1 0 1 2 B: Simulated perfect trials with varying treatment delay p < 0.0001
Fig. 20. Example funnel plot analysis for simulated perfect trials.
Conflicts of interest
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Dexamethasone for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 dexamethasone 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 dexamethasone trials represent the optimal conditions for efficacy.
Limitations
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 for 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 with conflicts of interest 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 alone68-92. 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 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.
Notes
Currently all studies are peer-reviewed. While overall meta-analysis shows poor results, dexamethasone shows lower risk for a subgroup of very late-stage patients with severe disease1,2. Dexamethasone is a highly potent synthetic corticosteroid that broadly suppresses immune function and attenuates pro-inflammatory signaling. It may also impair the innate antiviral response. Early use, when viral replication dominates, may therefore worsen outcomes. However, for late-stage dysregulated hyperinflammatory responses, beneficial immunosuppression may outweigh that cost. RECOVERY showed lower mortality in mechanically ventilated COVID-19 patients, consistent with pre-COVID-19 evidence from the DEXA-ARDS RCT that shows lower mortality in mechanically ventilated ARDS patients. However, RECOVERY and other studies show higher risk in non-hypoxic patients. The evidence for benefit in patients on less-intensive forms of respiratory support remains unclear. The RECOVERY "oxygen only" subgroup combines patients at many different levels including high-flow oxygen and NIV, and authors do not provide analysis for patients at different baseline levels of respiratory support, or provide details of potential rapid progression to more intensive forms. Corticosteroids for COVID-19 carry harms - broad immunosuppression that impairs viral clearance and host defenses, hyperglycemia, impaired healing, and myopathy, offset by an anti-inflammatory benefit that depends on how much steroid-suppressible inflammation is driving lung injury. While guidelines often tie treatment to the level of oxygen support, this is a crude proxy for the underlying inflammatory biology. Measures of steroid-suppressible inflammation may be used to more accurately predict the benefit-harm crossover point for each patient.
Reviews
Gordon et al. present a review covering dexamethasone for COVID-19.
Other studies
Additional preclinical or review papers suggesting potential benefits of dexamethasone for COVID-19 include118-169. We have not reviewed these studies in detail.
Perspective
Results compared with other treatments
SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors33-40, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk41, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 21 shows an overview of the results for dexamethasone in the context of multiple COVID-19 treatments, and Fig. 22 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Fig. 21. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random-effects meta-analysis. 0.5% of 12,000+ proposed treatments show efficacy170.
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Fig. 22. Efficacy vs. cost for COVID-19 treatments.
Conclusion
Meta-analysis using the most serious outcome reported shows 11% [-4‑28%] higher risk, without reaching statistical significance.
While overall meta-analysis shows poor results, dexamethasone shows lower risk for a subgroup of very late-stage patients with severe disease1,2. Dexamethasone is a highly potent synthetic corticosteroid that broadly suppresses immune function and attenuates pro-inflammatory signaling. It may also impair the innate antiviral response. Early use, when viral replication dominates, may therefore worsen outcomes. However, for late-stage dysregulated hyperinflammatory responses, beneficial immunosuppression may outweigh that cost. RECOVERY showed lower mortality in mechanically ventilated COVID-19 patients, consistent with pre-COVID-19 evidence from the DEXA-ARDS RCT that shows lower mortality in mechanically ventilated ARDS patients. However, RECOVERY and other studies show higher risk in non-hypoxic patients. The evidence for benefit in patients on less-intensive forms of respiratory support remains unclear. The RECOVERY "oxygen only" subgroup combines patients at many different levels including high-flow oxygen and NIV, and authors do not provide analysis for patients at different baseline levels of respiratory support, or provide details of potential rapid progression to more intensive forms.
Corticosteroids for COVID-19 carry harms - broad immunosuppression that impairs viral clearance and host defenses, hyperglycemia, impaired healing, and myopathy, offset by an anti-inflammatory benefit that depends on how much steroid-suppressible inflammation is driving lung injury. While guidelines often tie treatment to the level of oxygen support, this is a crude proxy for the underlying inflammatory biology. Measures of steroid-suppressible inflammation may be used to more accurately predict the benefit-harm crossover point for each patient.
 
Contact.
Contact us on X at @CovidAnalysis.
Funding.
We have received no funding or compensation in any form, and do not accept donations. This is entirely volunteer work.
Conflicts of interest.
We have no conflicts of interest. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.
AI.
We use AI models (Gemini, Grok, Claude, and ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.
Updates.
Our COVID-19 meta-analyses involve the extraction of over 228,000 datapoints from thousands of papers for 227 treatments. We thank the thousands of scientists, physicians, and other contributors that have provided updates, suggestions, feedback, and corrections. These are all welcome and can be submitted at https://c19early.org/dexmeta.html.
Dedication.
This work is dedicated to top evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce mortality and morbidity during the pandemic. In alphabetical order: Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty, Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr. Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch, Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.
Public domain.
This is a public domain work distributed in accordance with the Creative Commons CC0 1.0 Universal license, which dedicates the work to the public domain by waiving all rights worldwide under copyright law. You can distribute, remix, adapt, and build upon this work in any medium or format, including for commercial purposes, without asking permission. Referenced material and third-party images retain any original copyrights or restrictions. See: https://creativecommons.org/publicdomain/zero/1.0/.
Study Notes
Retrospective 410 hospitalized COVID-19 patients in the Democratic Republic of Congo showing significantly lower mortality with vitamin C treatment. Submit Corrections or Updates.
Late treatmentPSM retrospective · 1,058 patients · Mar 2020 – Jun 2022
Dexamethasone for COVID-19
Bhat et al. J. Clinical Endocrinology &amp; Metabolism · Oct 2024
Outcome
Improvement
Relative Risk · 95% CI
Mortality
-35%1.35 · 0.88–2.07
Improvement
-46%1.46 · 1.06–2.00
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Worse improvement with dexamethasone
p = 0.023
Retrospective propensity score matched study of 529 hospitalized diabetic COVID-19 patients showing no significant difference in mortality or clinical improvement with dexamethasone treatment. Submit Corrections or Updates.
Late treatmentRetrospective · 14,950 patients · USA · Jun 2020 – May 2021
Dexamethasone for COVID-19
Crothers et al. European Respiratory J. · November 2021
Outcome
Improvement
Relative Risk · 95% CI
Mortality, all
-59%1.59 · 1.39–1.81
Mortality, no oxygen
-76%1.76 · 1.47–2.12
Mortality, nasal cannula
-8%1.08 · 0.86–1.36
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Higher mortality with dexamethasone
p < 0.000001
IPTW retrospective 19,973 hospitalized COVID-19 patients showing increased 90-day mortality with dexamethasone in patients not on oxygen and no mortality benefit in patients on low-flow nasal cannula. Authors found consistent results across multiple sensitivity analyses. Authors suggest that widespread adoption of dexamethasone for less severely ill COVID-19 patients may cause unintended harm, hypothesizing that early corticosteroid use may impair viral clearance and immune responses important for infection resolution. Submit Corrections or Updates.
Late treatmentRCT · 126 patients · Spain · Jun 2021 – Jan 2022
Dexamethasone for COVID-19
EARLY-DEX Franco-Moreno et al. Frontiers in Medicine · July 2024
Outcome
Improvement
Relative Risk · 95% CI
Ventilation
-134%2.34 · 0.45 to >9
ICU admission
-217%3.17 · 0.13 to >9
ARDS
-17%1.17 · 0.52–2.62
Hospitalization time
3%0.97 · 0.67–1.39
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Higher ventilation and ICU admission
Not statistically significant · p = 0.41 (ventilation) and p = 0.46 (ICU admission)
RCT 126 hospitalized COVID-19 pneumonia patients not requiring oxygen at admission, showing no significant difference in outcomes with dexamethasone treatment. Submit Corrections or Updates.
Late treatmentRetrospective · 30 patients · USA · Jun 2020 – Jun 2022
Dexamethasone for COVID-19
Garneau et al. PLOS ONE · November 2024
Outcome
Improvement
Relative Risk · 95% CI
Mortality
-31%1.31 · 0.09 to >9
ICU admission
-423%5.23 · 0.66 to >9
Hospitalization time
-155%2.55 · 0.95–6.83
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Higher ICU admission and longer hospitalization
Not statistically significant · p = 0.14 (ICU admission) and p = 0.063 (hospitalization)
Retrospective 30 hospitalized patients with sickle cell disease (SCD) showing increased risk of venous thromboembolism (VTE) with dexamethasone treatment for COVID-19. There were also trends towards increased ICU admission and longer hospital stays with dexamethasone, without statistical significance. Submit Corrections or Updates.
Late treatmentRCT · 1,749 patients · multinational · May 2021 – May 2022
Dexamethasone for COVID-19
RECOVERY high-dose Horby et al. Lancet · May 2023 · NCT04381936
Outcome
Improvement
Relative Risk · 95% CI
Mortality, day 180
-15%1.15 · 0.95–1.39
Mortality, day 28, exc. ventilated
-59%1.59 · 1.20–2.10
Ventilation, day 28, exc. ventilated
-46%1.46 · 0.75–2.83
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Higher ventilation with dexamethasone
Not statistically significant · p = 0.27
RCT 1,272 hospitalized COVID-19 patients with hypoxia receiving no oxygen or simple oxygen only, showing significantly increased mortality with higher dose dexamethasone compared to usual care (which included low-dose dexamethasone). 6-month results are from Horby (B) et al. Submit Corrections or Updates.
Late treatmentRCT · NCT04381936 · 6,425 patients · UK · Mar – Jun 2020
Dexamethasone for COVID-19
RECOVERY Horby et al. New England J. Medicine · February 2021
Outcome
Improvement
Relative Risk · 95% CI
Mortality, day 180
13%0.87 · 0.79–0.96
Mortality, day 28
17%0.83 · 0.75–0.93
Ventilation
21%0.79 · 0.64–0.97
Discharge
9%0.91 · 0.85–0.97
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Lower mortality and ventilation
p = 0.0051 (mortality) and p = 0.026 (ventilation)
RCT 6,425 hospitalized COVID-19 patients showing lower 28-day mortality with dexamethasone treatment. The benefit was most pronounced in patients who had symptoms for more than 7 days at randomization, suggesting dexamethasone is most effective when the disease is dominated by inflammatory processes rather than viral replication. 6-month results are from Horby (B) et al. Submit Corrections or Updates.
RCT 50 hospitalized COVID-19 ARDS patients showing no clinical benefit with high-dose dexamethasone. Submit Corrections or Updates.
Early treatmentDouble-blind RCT · NCT04746430 · 6 patients · Netherlands
Dexamethasone for COVID-19
COPPER Kocks et al. ERJ Open Research · April 2022
Outcome
Improvement
Relative Risk · 95% CI
Hospitalization
-300%4.00 · 0.27 to >9
Severe case
-450%5.50 · 0.41 to >9
Recovery
-600%7.00 · 0.55 to >9
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Higher hospitalization and severe cases
Not statistically significant · p = 0.47 (hospitalization) and p = 0.4 (severe cases)
Pilot RCT of 7 outpatients with non-severe COVID-19 suggesting potential harmful effects of dexamethasone treatment. Time to recovery was significantly longer in the dexamethasone group compared to controls (p=0.03). Authors note that systemic corticosteroids, while beneficial for hospitalized COVID-19 patients requiring oxygen, may be harmful in non-severe cases by potentially inhibiting normal immune response when administered too early. Submit Corrections or Updates.
Late treatmentRCT · 98 patients · Argentina · Jun 2020 – Mar 2021
Dexamethasone for COVID-19
Maskin et al. J. Intensive Care Medicine · December 2021 · NCT04395105
Outcome
Improvement
Relative Risk · 95% CI
Mortality, day 90
10%0.90 · 0.79–1.02
Mortality, day 90 (b)
0%1.00 · 0.66–1.52
Mortality, day 28
-5%1.05 · 0.65–1.71
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
No significant difference in mortality
RCT 98 mechanically ventilated patients with COVID-19-related ARDS showing no significant difference in ventilator-free days (VFD) at 28 days with high-dose dexamethasone (16mg/day for 5 days then 8mg/day for 5 days) versus low-dose dexamethasone (6mg/day for 10 days). Submit Corrections or Updates.
Late treatmentRetrospective · 56,368 patients · USA · Jul 2020 – Oct 2021
Dexamethasone for COVID-19
Mourad et al. JAMA Network Open · April 2023
Outcome
Improvement
Relative Risk · 95% CI
Mortality, all patients
10%0.90 · 0.86–0.95
Mortality, no oxygen
10%
Mortality...
8%
Mortality, NIPPV
13%
Mortality, MV/ECMO
18%
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Lower mortality with dexamethasone
p = 0.00024
PSM retrospective 80,699 hospitalized COVID-19 patients showing reduced mortality or discharge to hospice with dexamethasone in patients requiring supplemental oxygen or mechanical ventilation/ECMO, but no significant difference in patients not requiring supplemental oxygen or on NIPPV. Submit Corrections or Updates.
Late treatmentRCT · 971 patients · Denmark · Aug 2020 – May 2021
Dexamethasone for COVID-19
Munch et al. JAMA · November 2021 · NCT04509973
Outcome
Improvement
Relative Risk · 95% CI
Mortality, day 90
13%0.87 · 0.70–1.07
Mortality, day 28
14%0.86 · 0.68–1.08
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Lower mortality with dexamethasone
Not statistically significant · p = 0.2
RCT 1,000 hospitalized patients (982 analyzed) with COVID-19 and severe hypoxemia showing no statistically significant difference with 12 mg/d vs 6 mg/d of intravenous dexamethasone. Submit Corrections or Updates.
RCT 299 patients with moderate or severe COVID-19-related ARDS showing increased ventilator-free days with dexamethasone treatment. There was no significant difference in 28-day mortality (56.3% vs 61.5%), ICU-free days, or mechanical ventilation duration.

The study protocol and the Statistical Analysis Plan (SAP) include reporting mortality at 3, 6, 9, and 12 months, however this was not done. Submit Corrections or Updates.
Late treatmentRCT · NCT04707534 · 107 patients · USA · Jan – Dec 2021
Dexamethasone for COVID-19
Wu et al. PLOS ONE · October 2022
Outcome
Improvement
Relative Risk · 95% CI
Mortality
-133%2.33 · 0.87–6.24
Clinical improvement ≥2 points...
-45%1.45 · 0.55–3.86
Clinical improvement ≥2 points...
-9%1.09 · 0.46–2.60
00.511.52+
← Dexamethasone
reduces riskDexamethasone
increases risk →
Higher mortality and worse improvement
Not statistically significant · p = 0.11 (mortality) and p = 0.4 (improvement)
RCT 107 hospitalized COVID-19 pneumonia patients showing no significant benefit and potential harm with high-dose dexamethasone (20 mg daily) compared to low-dose dexamethasone (6 mg daily). Submit Corrections or Updates.
Retrospective 2,196 COVID-19 patients in Taiwan (49% mild cases, 44% moderate, 7% severe) showing significantly higher mortality with dexamethasone. Submit Corrections or Updates.
Please submit updates and corrections at the bottom of this page.Please submit updates and corrections at https://c19early.org/dexmeta.html.
7/10/2026: Updated discussion of the benefit-harm crossover point.
6/20/2026: Inclusion of high vs. low-dose studies in a separate forest plot.
Appendix 1. Methods and Data
Search methods
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org, which regularly receives notification of studies upon publication. Search terms are dexamethasone and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of dexamethasone for COVID-19 that report a comparison with a control group are included in the main analysis. Studies with major unexplained data issues, for example major outcome data that is impossible to be correct with no response from the authors, are excluded.
Fig. 23. Mid-recovery results can more accurately reflect efficacy when almost all patients recover. Mateja et al. confirm that intermediate viral load results more accurately reflect hospitalization/death.
Effect extraction
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 reported then they are both used in specific outcome analyses, while mortality is used for pooled analysis. If symptomatic results are reported at multiple times, we use the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. 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 outcomes are considered more important than viral outcomes. 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 little or no room for an effective treatment to do better, however faster recovery is valuable. An IPD meta-analysis confirms that intermediate viral load reduction is more closely associated with hospitalization/death than later viral load reduction171. 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.
Statistical methods
Forest plots are computed using PythonMeta172 with the DerSimonian and Laird random-effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I2 statistic. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang et al. Reported confidence intervals and p-values are used when available, and adjusted values are used when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted 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 1176. 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 (3.14.8) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.3), statsmodels (0.15.0), and plotly (6.9.0). Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor (4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.2 is used to parse PDF documents.
When evaluating potential effect modification across groups, we use an interaction test as described by Altman (C) et al. We compared the log-transformed relative risks using a z-test, deriving the standard error of the difference from the 95% confidence intervals. A two-sided interaction p-value of < 0.05 was considered a statistically significant difference in treatment effect between the groups.
Quality evaluation
Cochrane RoB 2/ROBINS-I are often used to evaluate studies, and have the advantage of providing standardized rules that can be applied with minimal understanding of the domain and study. However, the rules do not account for many real-world issues, often overemphasize or underemphasize others, and studies show low inter-rater reliability184. Certain domains are more applicable for these tools, however the time-sensitive nature of a pandemic, with significant mortality for every day of delay in evidence assessment, and the characteristics of COVID-19 make them inappropriate for this domain. This can be demonstrated with examples where expert RoB 2/ROBINS-I ratings do not match reality for COVID-19. Popp et al. use RoB 2 to classify Reis et al. as low risk of bias, however this is the opposite of reality—the trial not only has very high risk of bias, but has very high actual known bias, refusing to release data despite pledging to, reporting multiple impossible numbers, having blinding and randomization failure, and many other issues186. Axfors et al. use RoB 2 to classify Horby (D) et al. as low risk of bias, however this is the opposite of reality—the very late treatment and excessive dosage used produces results with no relevance to recommended usage. HCQ shows poor results with late treatment and excessive dosage, and the combination shows harmB. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias182,183 are far from the most informative. Both involve very late treatment, providing no information on recommended usage, and ROBINS-I does a very poor job of accounting for the impact of confounding factorsC.
Our quality evaluation focuses on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias. The estimated potential impact of each confounding factor, and the direction of the impact is considered. For example, consider a study that shows significantly lower risk, the value of the study varies significantly if confounding points to an underestimate or an overestimate of efficacy. In one case, the real effect may be null, while the other case provides stronger evidence of efficacy (which may be greater than the study shows). Analysis focusing on the risk of bias, while simpler, may penalize studies for theoretical or technical issues that have no or minimal impact on outcomes. Analysis also depends on the outcome, for example certain issues are less relevant for objective outcomes such as mortality. Inaccurate penalization, and inaccurate high-quality evaluation in the face of known major issues affecting outcomes, increases in significance during a pandemic when immediate recognition of new evidence is critical, and when considering all global studies, as required during a pandemic. Investigators in other countries may have different customs for design, analysis, and reporting, and different English language skills, however they may not be less diligent or have greater bias. Investigators in lower-pharmaceutical-profit countries may have lower bias towards profitable interventions.
Treatment time
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 effective53,54.
Living analysis
This is a living analysis and is updated regularly. Submit updates or corrections with the form below. We received no funding, this research is done in our spare time. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.
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/dexmeta.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.
Kocks, 4/30/2022, Double Blind Randomized Controlled Trial, Netherlands, peer-reviewed, 9 authors, trial NCT04746430 (history) (COPPER).
Submit Corrections or Updates.
 risk of hospitalization, 300.0% higher, RR 4.00, p = 0.47, treatment 2 of 4 (50.0%), control 0 of 2 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), day 28.
risk of severe case, 450.0% higher, RR 5.50, p = 0.40, treatment 3 of 4 (75.0%), control 0 of 2 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
risk of no recovery, 600.0% higher, RR 7.00, p = 0.07, treatment 4 of 4 (100.0%), control 0 of 2 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
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.
Bepouka, 5/14/2025, retrospective, DR Congo, peer-reviewed, 14 authors, study period 20 March, 2020 - 2 January, 2022.
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 risk of death, 104.1% higher, OR 2.04, p = 0.55, treatment 70, control 340, adjusted per study, inverted to make OR<1 favor treatment, multivariable, RR approximated with OR.
Bhat, 10/17/2024, retrospective, USA, peer-reviewed, 7 authors, study period 4 March, 2020 - 25 June, 2022.
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 risk of death, 35.3% higher, RR 1.35, p = 0.20, treatment 46 of 529 (8.7%), control 34 of 529 (6.4%), propensity score matching, day 28.
risk of no improvement, 45.6% higher, RR 1.46, p = 0.02, treatment 83 of 529 (15.7%), control 57 of 529 (10.8%), propensity score matching, day 28.
Crothers, 11/25/2021, retrospective, USA, peer-reviewed, median age 71.0, 15 authors, study period 7 June, 2020 - 31 May, 2021.
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 risk of death, 59.0% higher, HR 1.59, p < 0.001, treatment 7,507, control 7,443, all patients, propensity score weighting, Cox proportional hazards, day 90.
risk of death, 76.0% higher, HR 1.76, p < 0.001, treatment 3,124, control 6,006, no oxygen, propensity score weighting, Cox proportional hazards, day 90.
risk of death, 8.0% higher, HR 1.08, p = 0.52, treatment 4,383, control 1,437, nasal cannula, propensity score weighting, Cox proportional hazards, day 90.
Franco-Moreno, 7/17/2024, Randomized Controlled Trial, Spain, peer-reviewed, mean age 48.8, 14 authors, study period June 2021 - January 2022, average treatment delay 9.0 days, trial NCT04836780 (history) (EARLY-DEX).
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 risk of mechanical ventilation, 134.5% higher, RR 2.34, p = 0.41, treatment 4 of 58 (6.9%), control 2 of 68 (2.9%), day 30.
risk of ICU admission, 217.2% higher, RR 3.17, p = 0.46, treatment 1 of 58 (1.7%), control 0 of 68 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), day 30.
risk of ARDS, 17.2% higher, RR 1.17, p = 0.81, treatment 10 of 58 (17.2%), control 10 of 68 (14.7%), day 30.
hospitalization time, 3.0% lower, relative time 0.97, p = 0.88, treatment mean 6.4 (±5.0) n=58, control mean 6.6 (±8.7) n=68.
Garneau, 11/26/2024, retrospective, USA, peer-reviewed, median age 33.7, 6 authors, study period 1 June, 2020 - 26 June, 2022.
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 risk of death, 30.8% higher, RR 1.31, p = 1.00, treatment 1 of 13 (7.7%), control 1 of 17 (5.9%).
risk of ICU admission, 423.1% higher, RR 5.23, p = 0.14, treatment 4 of 13 (30.8%), control 1 of 17 (5.9%).
hospitalization time, 154.5% higher, relative time 2.55, p = 0.06, treatment 13, control 17.
Horby (C), 5/31/2023, Randomized Controlled Trial, multiple countries, peer-reviewed, mean age 61.0, 37 authors, study period 25 May, 2021 - 13 May, 2022, average treatment delay 7.0 days, trial NCT04381936 (history) (RECOVERY high-dose).
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 risk of death, 15.0% higher, RR 1.15, p = 0.15, treatment 241 of 905 (26.6%), control 200 of 844 (23.7%), day 180.
risk of death, 59.0% higher, RR 1.59, p = 0.001, treatment 123 of 659 (18.7%), control 75 of 613 (12.2%), excluding ventilated patients, day 28.
risk of mechanical ventilation, 46.0% higher, RR 1.46, p = 0.27, treatment 22 of 659 (3.3%), control 14 of 613 (2.3%), excluding ventilated patients, day 28.
Horby, 2/25/2021, Randomized Controlled Trial, United Kingdom, peer-reviewed, mean age 66.1, 26 authors, study period 19 March, 2020 - 8 June, 2020, trial NCT04381936 (history) (RECOVERY).
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 risk of death, 13.0% lower, RR 0.87, p = 0.005, treatment 596 of 2,104 (28.3%), control 1,297 of 4,321 (30.0%), NNT 59, adjusted per study, day 180.
risk of death, 17.0% lower, RR 0.83, p < 0.001, treatment 482 of 2,104 (22.9%), control 1,110 of 4,321 (25.7%), NNT 36, adjusted per study, day 28.
risk of mechanical ventilation, 21.0% lower, RR 0.79, p = 0.03, treatment 110 of 1,780 (6.2%), control 298 of 3,638 (8.2%), NNT 50, adjusted per study, day 28.
risk of no hospital discharge, 9.1% lower, RR 0.91, p = 0.003, treatment 2,104, control 4,321, adjusted per study, inverted to make RR<1 favor treatment, day 28.
Jamaati, 4/30/2021, Randomized Controlled Trial, Iran, peer-reviewed, median age 62.0, 18 authors, average treatment delay 8.0 days, trial IRCT20151227025726N17.
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 risk of death, 6.7% higher, RR 1.07, p = 1.00, treatment 16 of 25 (64.0%), control 15 of 25 (60.0%), day 28.
risk of mechanical ventilation, 18.2% higher, RR 1.18, p = 0.78, treatment 13 of 25 (52.0%), control 11 of 25 (44.0%), day 28.
Maskin, 12/13/2021, Randomized Controlled Trial, Argentina, peer-reviewed, 13 authors, study period 17 June, 2020 - 27 March, 2021, trial NCT04395105 (history).
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 risk of death, 10.0% lower, HR 0.90, p = 0.10, treatment 49, control 49, Cox proportional hazards, day 90.
risk of death, no change, RR 1.00, p = 1.00, treatment 23 of 49 (46.9%), control 23 of 49 (46.9%), day 90.
risk of death, 5.3% higher, RR 1.05, p = 1.00, treatment 20 of 49 (40.8%), control 19 of 49 (38.8%), day 28.
Mourad, 4/17/2023, retrospective, USA, peer-reviewed, median age 64.0, 7 authors, study period 1 July, 2020 - 31 October, 2021.
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 risk of death, 9.6% lower, RR 0.90, p < 0.001, treatment 48,579, control 7,789, adjusted per study, all patients.
risk of death, 10.0% lower, OR 0.90, p = 0.14, treatment 7,537, control 5,503, adjusted per study, no oxygen, mortality or discharge to hospice, RR approximated with OR.
risk of death, 8.0% lower, OR 0.92, p = 0.01, treatment 48,579, control 7,789, adjusted per study, supplemental oxygen, mortality or discharge to hospice, RR approximated with OR.
risk of death, 13.0% lower, OR 0.87, p = 0.12, treatment 6,826, control 792, adjusted per study, NIPPV, mortality or discharge to hospice, RR approximated with OR.
risk of death, 18.0% lower, OR 0.82, p = 0.04, treatment 2,660, control 1,013, adjusted per study, MV/ECMO, mortality or discharge to hospice, RR approximated with OR.
Munch, 11/9/2021, Double Blind Randomized Controlled Trial, Denmark, peer-reviewed, 80 authors, study period 27 August, 2020 - 20 May, 2021, trial NCT04509973 (history).
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 risk of death, 13.0% lower, RR 0.87, p = 0.20, treatment 157 of 490 (32.0%), control 180 of 478 (37.7%), NNT 18, adjusted per study, day 90.
risk of death, 14.0% lower, RR 0.86, p = 0.20, treatment 133 of 491 (27.1%), control 155 of 480 (32.3%), NNT 19, adjusted per study, day 28.
Tomazini, 10/6/2020, Randomized Controlled Trial, Brazil, peer-reviewed, 34 authors, study period 17 April, 2020 - 23 June, 2020, trial NCT04327401 (history) (CoDEX).
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 risk of death, 3.0% lower, HR 0.97, p = 0.85, treatment 85 of 151 (56.3%), control 91 of 148 (61.5%), NNT 19, adjusted per study, day 28.
6-point scale, 34.0% lower, OR 0.66, p = 0.07, treatment 151, control 148, day 15, RR approximated with OR.
Wu (B), 10/3/2022, Randomized Controlled Trial, USA, peer-reviewed, 6 authors, study period January 2021 - December 2021, trial NCT04707534 (history).
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 risk of death, 132.7% higher, RR 2.33, p = 0.11, treatment 11 of 52 (21.2%), control 5 of 55 (9.1%), day 28.
clinical improvement ≥2 points WHO-OSCI, 45.3% higher, OR 1.45, p = 0.40, treatment 52, control 55, day 28, RR approximated with OR.
clinical improvement ≥2 points WHO-OSCI, 9.1% higher, OR 1.09, p = 0.83, treatment 52, control 55, day 14, RR approximated with OR.
Yen, 8/20/2024, retrospective, Taiwan, peer-reviewed, 6 authors, study period 1 January, 2022 - 31 July, 2022.
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 risk of death, 103.0% higher, HR 2.03, p < 0.001, treatment 572, control 1,624, adjusted per study, multivariable, Cox proportional hazards.
Footnotes
a.
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
b.
When administered late in infection, HCQ may enhance viral egress by further increasing lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of SARS-CoV-2 particles into the extracellular environment178,179. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage180. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.
c.
Peters (B) et al. is subject to confounding by calendar-time (SOC evolved rapidly early in the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital specific effects), hospital type (non-treatment hospitals were tertiary university centers), confounding by indication (4/7 hospitals initiated treatment on deterioration), immortal-time bias for as-treated (exposure assigned after baseline), significant differences for other experimental treatments, potential overadjustment from collider bias (steroid use and indication bias), limited baseline severity information, differences in hospice referral propensity across hospitals, unadjusted difference in time from onset to admission, difference in PCR positivity, and other factors. Mahévas et al. is subject to confounding by hospital (treatment highly dependent on the hospital, different SOC/ICU transfer practices, not included in PS), immortal time (only partly addressed in sensitivity analysis), co-treatment differences, calendar-time (SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep age-risk gradient), residual imbalance (variables dropped from PS), a composite outcome dependent on hospital triage/capacity, and other factors.
References