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Antiandrogens for COVID-19: real-time meta analysis of 45 studies
Covid Analysis, December 2022
https://c19early.org/aameta.html
 
0 0.5 1 1.5+ All studies 31% 45 94,222 Improvement, Studies, Patients Relative Risk Mortality 40% 31 87,965 Ventilation 49% 13 3,329 ICU admission 32% 10 7,769 Hospitalization 30% 15 8,854 Progression 49% 3 221 Recovery 42% 11 2,063 Cases 9% 11 92,725 Viral clearance 49% 4 560 RCTs 58% 16 2,863 RCT mortality 62% 13 2,590 Peer-reviewed 28% 38 91,131 Prophylaxis 5% 22 63,938 Early 44% 6 28,040 Late 64% 17 2,244 Antiandrogens for COVID-19 c19early.org/aa Dec 2022 Favorsantiandrogen Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ventilation, ICU admission, hospitalization, recovery, cases, and viral clearance. 28 studies from 23 independent teams in 12 different countries show statistically significant improvements in isolation (16 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 31% [21‑40%] improvement. Results are better for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
Results are robust — in exclusion sensitivity analysis 20 of 45 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 31% 45 94,222 Improvement, Studies, Patients Relative Risk Mortality 40% 31 87,965 Ventilation 49% 13 3,329 ICU admission 32% 10 7,769 Hospitalization 30% 15 8,854 Progression 49% 3 221 Recovery 42% 11 2,063 Cases 9% 11 92,725 Viral clearance 49% 4 560 RCTs 58% 16 2,863 RCT mortality 62% 13 2,590 Peer-reviewed 28% 38 91,131 Prophylaxis 5% 22 63,938 Early 44% 6 28,040 Late 64% 17 2,244 Antiandrogens for COVID-19 c19early.org/aa Dec 2022 Favorsantiandrogen Favorscontrol after exclusions
This analysis combines the results of several different antiandrogens. Results for individual treatments may vary.
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 18% of antiandrogen studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix. [Kotani] present another meta analysis for antiandrogens, showing significant improvements for mortality and progression.
Highlights
Antiandrogens reduce risk for COVID-19 with very high confidence for mortality, ventilation, hospitalization, recovery, viral clearance, and in pooled analysis, high confidence for ICU admission and cases, and very low confidence for progression. This analysis combines the results of several different antiandrogens.
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+ Cadegiani 77% 0.23 [0.08-0.66] recov. time 8 (n) 262 (n) Improvement, RR [CI] Treatment Control McCoy (DB RCT) 80% 0.20 [0.01-4.13] death 0/134 2/134 censored, see notes Cadegiani (DB RCT) 62% 0.38 [0.18-0.82] no recov. 7/44 18/43 Cadegiani (DB RCT) 63% 0.37 [0.02-8.85] death 0/75 1/102 Kintor (DB RCT) 67% 0.33 [0.01-8.16] death 0/365 1/365 Hunt 39% 0.61 [0.51-0.73] death 167/1,788 1,445/24,720 Tau​2 = 0.01, I​2 = 3.6%, p < 0.0001 Early treatment 44% 0.56 [0.45-0.69] 174/2,414 1,467/25,626 44% improvement Vicenzi 93% 0.07 [0.04-0.53] death 30 (n) 39 (n) OT​1 Improvement, RR [CI] Treatment Control Goren 81% 0.19 [0.03-1.28] ICU 1/12 17/36 Mareev (RCT) 11% 0.89 [0.65-1.22] no recov. 33 (n) 33 (n) CT​2 Zarehoseinz.. (RCT) 75% 0.25 [0.03-2.14] death 1/40 4/40 Ghandehari (RCT) -22% 1.22 [0.08-18.2] death 1/18 1/22 Ersoy (ICU) 46% 0.54 [0.36-0.81] death 14/30 26/30 ICU patients Welén (RCT) 80% 0.20 [0.01-4.65] death 0/29 1/10 Cadegiani (DB RCT) 78% 0.22 [0.16-0.30] death 45/423 171/355 Davarpanah 78% 0.22 [0.08-0.55] hosp. 6/103 23/103 CT​2 Kotfis (RCT) 17% 0.83 [0.25-2.74] death 4/24 5/25 Abbasi (SB RCT) 55% 0.45 [0.18-1.13] death 5/51 19/87 Gomaa (DB RCT) 91% 0.09 [0.01-1.56] death 0/25 5/25 CT​2 Hsieh 88% 0.12 [0.01-2.22] death 0/117 4/143 CT​2 Nickols (DB RCT) 18% 0.82 [0.32-1.82] death 11/62 7/34 Gordon (DB RCT) 82% 0.18 [0.03-0.94] death n/a n/a Wadhwa (RCT) 72% 0.28 [0.09-0.85] progression 4/74 9/46 Barnette (DB RCT) 55% 0.45 [0.27-0.74] death 19/94 23/51 Tau​2 = 0.36, I​2 = 73.2%, p < 0.0001 Late treatment 64% 0.36 [0.24-0.55] 111/1,165 315/1,079 64% improvement Montopoli 95% 0.05 [0.00-12.3] death 0/5,273 18/37,161 Improvement, RR [CI] Treatment Control Holt -129% 2.29 [1.59-3.32] death/ICU 16/31 148/658 Koskinen 46% 0.54 [0.06-5.16] death 1/134 3/218 Patel 55% 0.45 [0.11-1.47] death 4/22 10/36 Bennani 95% 0.05 [0.00-2063] death 0/4 18/114 Lazzeri -23% 1.23 [0.81-1.87] death/ICU Kwon 21% 0.79 [0.10-6.40] death 1/799 7/4,412 Klein -124% 2.24 [0.86-5.85] death 6/304 13/1,475 Jeon 77% 0.23 [0.08-0.64] cases case control Shaw (PSM) 6% 0.94 [0.90-0.98] cases 47 (n) 97 (n) Israel 38% 0.62 [0.41-0.91] hosp. case control Jiménez-Alcaide 33% 0.67 [0.26-1.74] death 3/11 17/50 Kazan -229% 3.29 [0.61-17.7] hosp. 4/138 2/227 Schmidt (PSM) 20% 0.80 [0.46-1.34] death 25/169 44/308 Duarte 11% 0.89 [0.59-1.11] death 100/156 32/43 Welén 2% 0.98 [0.61-1.59] death 21/358 167/4,980 Gedeborg -25% 1.25 [0.95-1.65] death case control Lyon 17% 0.83 [0.42-1.63] death 15/944 19/994 Lee (PSW) 21% 0.79 [0.62-0.97] severe case 76/295 727/2,427 MacFadden 7% 0.93 [0.88-0.98] cases n/a n/a Shah -16% 1.16 [0.68-1.98] death 148 (n) 317 (n) Cousins (PSM) 69% 0.31 [0.07-1.00] ventilation 794 (n) 794 (n) Tau​2 = 0.02, I​2 = 70.6%, p = 0.36 Prophylaxis 5% 0.95 [0.85-1.06] 272/9,627 1,225/54,311 5% improvement All studies 31% 0.69 [0.60-0.79] 557/13,206 3,007/81,016 31% improvement 45 antiandrogen COVID-19 studies c19early.org/aa Dec 2022 Tau​2 = 0.08, I​2 = 83.2%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment2 CT: study uses combined treatment Favors antiandrogen Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Cadegiani 77% recovery Relative Risk [CI] McCoy (DB RCT) 80% death censored, see notes Cadegiani (DB RCT) 62% recovery Cadegiani (DB RCT) 63% death Kintor (DB RCT) 67% death Hunt 39% death Tau​2 = 0.01, I​2 = 3.6%, p < 0.0001 Early treatment 44% 44% improvement Vicenzi 93% death OT​1 Goren 81% ICU admission Mareev (RCT) 11% recovery CT​2 Zarehosein.. (RCT) 75% death Ghandehari (RCT) -22% death Ersoy (ICU) 46% death ICU patients Welén (RCT) 80% death Cadegiani (DB RCT) 78% death Davarpanah 78% hospitalization CT​2 Kotfis (RCT) 17% death Abbasi (SB RCT) 55% death Gomaa (DB RCT) 91% death CT​2 Hsieh 88% death CT​2 Nickols (DB RCT) 18% death Gordon (DB RCT) 82% death Wadhwa (RCT) 72% progression Barnette (DB RCT) 55% death Tau​2 = 0.36, I​2 = 73.2%, p < 0.0001 Late treatment 64% 64% improvement Montopoli 95% death Holt -129% death/ICU Koskinen 46% death Patel 55% death Bennani 95% death Lazzeri -23% death/ICU Kwon 21% death Klein -124% death Jeon 77% case Shaw (PSM) 6% case Israel 38% hospitalization Jiménez-Alcaide 33% death Kazan -229% hospitalization Schmidt (PSM) 20% death Duarte 11% death Welén 2% death Gedeborg -25% death Lyon 17% death Lee (PSW) 21% severe case MacFadden 7% case Shah -16% death Cousins (PSM) 69% ventilation Tau​2 = 0.02, I​2 = 70.6%, p = 0.36 Prophylaxis 5% 5% improvement All studies 31% 31% improvement 45 antiandrogen COVID-19 studies c19early.org/aa Dec 2022 Tau​2 = 0.08, I​2 = 83.2%, p < 0.0001 Effect extraction pre-specifiedRotate device for footnotes/details Favors antiandrogen 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 antiandrogen studies.
We analyze all significant studies concerning the use of antiandrogens for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
An In Silico study supports the efficacy of antiandrogens [Saih].
An In Vivo animal study supports the efficacy of antiandrogens [Leach].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Improvement Studies Patients Authors
All studies31% [21‑40%]45 94,222 498
After exclusions33% [23‑42%]41 92,811 476
Peer-reviewed studiesPeer-reviewed28% [18‑38%]38 91,131 440
Randomized Controlled TrialsRCTs58% [35‑73%]16 2,863 199
Mortality40% [21‑54%]31 87,965 364
VentilationVent.49% [27‑64%]13 3,329 170
ICU admissionICU32% [1‑53%]10 7,769 98
HospitalizationHosp.30% [9‑47%]15 8,854 218
Recovery42% [27‑55%]11 2,063 130
Cases9% [1‑15%]11 92,725 96
Viral49% [24‑66%]4 560 32
RCT mortality62% [44‑75%]13 2,590 157
RCT hospitalizationRCT hosp.32% [3‑53%]8 2,304 131
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.
Early treatment Late treatment Prophylaxis
All studies44% [31‑55%] 664% [45‑76%] 175% [-6‑15%] 22
After exclusions39% [29‑48%] 464% [45‑76%] 1710% [0‑18%] 20
Peer-reviewed studiesPeer-reviewed40% [31‑49%] 361% [40‑75%] 156% [-6‑16%] 20
Randomized Controlled TrialsRCTs64% [26‑82%] 457% [28‑74%] 12-
Mortality39% [29‑48%] 463% [43‑75%] 143% [-18‑21%] 13
VentilationVent.95% [60‑99%] 244% [23‑59%] 853% [2‑78%] 3
ICU admissionICU-40% [22‑55%] 79% [-132‑65%] 3
HospitalizationHosp.81% [46‑93%] 321% [-10‑43%] 616% [-33‑47%] 6
Recovery68% [41‑83%] 238% [21‑52%] 9-
Cases--9% [1‑15%] 11
Viral58% [2‑82%] 245% [-38‑78%] 2-
RCT mortality71% [-75‑95%] 361% [39‑75%] 10-
RCT hospitalizationRCT hosp.81% [46‑93%] 310% [-20‑33%] 5-
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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 viral clearance.
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Figure 12. 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 13 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 65% improvement, compared to 33% for other studies. Figure 14, 15, and 16 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results. RCT results are included in Table 1 and Table 2.
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Figure 13. Results for RCTs and non-RCT studies.
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Figure 14. 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 15. Random effects meta-analysis for RCT mortality results.
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Figure 16. 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 17 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Cadegiani], potential randomization failure.
[Cadegiani (B)], significant unadjusted differences between groups.
[Holt], unadjusted results with no group details.
[Jiménez-Alcaide], excessive unadjusted differences between groups. Excluded results: case.
[Kazan], excessive unadjusted differences between groups.
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Figure 17. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and [Kumar] report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Figure 18 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 18. 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 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, 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 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.
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.
48% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 80% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 21% improvement, compared to 74% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 20 shows a scatter plot of results for prospective and retrospective studies.
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Figure 20. 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 21 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 21. Example funnel plot analysis for simulated perfect trials.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
1 of the 45 studies compare against other treatments, which may reduce the effect seen. 4 of 45 studies combine treatments. The results of antiandrogens alone may differ. 2 of 16 RCTs use combined treatment. [Kotani] present another meta analysis for antiandrogens, showing significant improvements for mortality and progression.
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.
Antiandrogens are an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ventilation, ICU admission, hospitalization, recovery, cases, and viral clearance. 28 studies from 23 independent teams in 12 different countries show statistically significant improvements in isolation (16 for the most serious outcome). Meta analysis using the most serious outcome reported shows 31% [21‑40%] improvement. Results are better for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Results are robust — in exclusion sensitivity analysis 20 of 45 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
This analysis combines the results of several different antiandrogens. Results for individual treatments may vary.
0 0.5 1 1.5 2+ Mortality 55% Improvement Relative Risk Ventilation 34% ICU admission 19% Recovery 47% c19early.org/aa Abbasi et al. Spironolactone for COVID-19 RCT LATE Favors spironolactone Favors control
[Abbasi] RCT including 51 spironolactone patients and 87 control patients in Iran, showing improved recovery with spironolactone, sitagliptin, and the combination of both.
0 0.5 1 1.5 2+ Mortality 55% Improvement Relative Risk Ventilation time 49% ICU time 44% Hospitalization time 26% c19early.org/aa Barnette et al. Sabizabulin for COVID-19 RCT LATE Favors sabizabulin Favors control
[Barnette] RCT with 98 hospitalized moderate/severe patients treated with sabizabulin and 52 control patients, showing lower mortality with treatment. Sabizabulin 9mg for up to 21 days. For more discussion see [twitter.com, twitter.com (B), twitter.com (C)].
0 0.5 1 1.5 2+ Mortality 95% Improvement Relative Risk ICU admission -119% Hospitalization 25% Severe case 8% c19early.org/aa Bennani et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Bennani] Retrospective 118 prostate cancer patients, 4 on androgren deprivation therapy, not showing significant differences (as expected with only 4 patients in the treatment group).
0 0.5 1 1.5 2+ Mortality 63% Improvement Relative Risk Ventilation 90% Hospitalization 86% c19early.org/aa Cadegiani et al. Proxalutamide for COVID-19 RCT EARLY Favors proxalutamide Favors control
[Cadegiani (C)] RCT 177 women in Brazil, 75 treated with proxalutamide, showing significantly lower hospitalization with treatment.
0 0.5 1 1.5 2+ Recovery 62% Improvement Relative Risk Recovery time 44% Recovery time (b) 40% c19early.org/aa Cadegiani et al. Dutasteride for COVID-19 RCT EARLY Favors dutasteride Favors control
[Cadegiani] RCT 130 outpatients in Brazil, 54 treated with dutasteride, showing faster recovery with treatment. All patients received nitazoxanide. There were no hospitalizations, mechanical ventilation, or deaths. Some percentages for viral clearance in Table 3 do not match the group sizes, and a third-party analysis suggests possible randomization failure. 34110420.2.0000.0008.
0 0.5 1 1.5 2+ Recovery time 77% Improvement Relative Risk Recovery time (b) 83% Time to viral- 38% c19early.org/aa Cadegiani et al. Spironolactone for COVID-19 EARLY Favors spironolactone Favors control
[Cadegiani (B)] Prospective study of 270 female COVID-19 patients in Brazil, 75 with hyperandrogenism, of which 8 were on spironolactone. Results suggest that HA patients may be at increased risk, and that spironolactone use may reduce the risk compared to both other HA patients and non-HA patients. SOC included other treatments and there was no mortality or hospitalization.
0 0.5 1 1.5 2+ Mortality 78% Improvement Relative Risk Mortality (b) 79% Recovery rate 45% Recovery rate (b) 55% primary Hospitalization time 33% c19early.org/aa Cadegiani et al. NCT04728802 Proxalutamide RCT LATE Favors proxalutamide Favors control
[Cadegiani (D)] RCT 778 hospitalized patients in Brazil, 423 treated with proxalutamide, showing significantly lower mortality and improved recovery with treatment. NCT04728802 and NCT05126628. Authors note that cases in this trial were predominantly the P.1 Gamma variant, for which proxalutamide may be more effective compared to other variants.
0 0.5 1 1.5 2+ Ventilation 69% Improvement Relative Risk ICU admission 58% c19early.org/aa Cousins et al. Spironolactone for COVID-19 Prophylaxis Favors spironolactone Favors control
[Cousins] PSM retrospective 64,349 COVID-19 patients in the USA, showing spironolactone associated with lower ICU admission.

Authors also present In Vitro research showing dose-dependent inhibition in a human lung epithelial cell line.
0 0.5 1 1.5 2+ Hospitalization 78% Improvement Relative Risk Recovery time 64% c19early.org/aa Davarpanah et al. Spironolactone for COVID-19 LATE Favors spironolactone Favors control
[Davarpanah] Prospective study of 206 outpatients in Iran, 103 treated with spironolactone and sitagliptin, showing lower hospitalization and faster recovery with treatment. spironolactone 100mg and sitagliptin 100mg daily.
0 0.5 1 1.5 2+ Mortality 11% Improvement Relative Risk c19early.org/aa Duarte et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Duarte] Retrospective 199 prostate cancer patients hospitalized with COVID-19 in Brazil, showing no significant difference in mortality with active ADT.
0 0.5 1 1.5 2+ Mortality 46% Improvement Relative Risk c19early.org/aa Ersoy et al. Spironolactone for COVID-19 ICU Favors spironolactone Favors control
[Ersoy] Retrospective 30 COVID-19 ARDS ICU patients and 30 control patients, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality -25% Improvement Relative Risk c19early.org/aa Gedeborg et al. Antiandrogens for COVID-19 Prophylaxis Favors antiandrogen Favors control
[Gedeborg] Case control study with 474 patients that died of COVID-19 in Sweden, showing higher risk with ADT, without statistical significance.
0 0.5 1 1.5 2+ Mortality -22% Improvement Relative Risk Ventilation 85% Progression, day 15 76% Progression, day 7 39% Recovery 100% primary c19early.org/aa Ghandehari et al. NCT04365127 Antiandrogens RCT LATE Favors antiandrogen Favors control
[Ghandehari] RCT 42 hospitalized patients in the USA, showing improved recovery and lower progression with progesterone treatment.
0 0.5 1 1.5 2+ Mortality 91% Improvement Relative Risk Ventilation 91% Recovery time 44% Recovery 33% c19early.org/aa Gomaa et al. NCT04487964 Glycyrrhizin RCT LATE TREATMENT Favors glycyrrhizin Favors control
[Gomaa] RCT with 50 hospitalized COVID+ patients in Egypt, 25 treated with glycyrrhizin and boswellic acid, showing improved recovery with treatment. Glycyrrhizin 60mg and boswellic acid 200mg bid for 2 weeks. NCT04487964.
0 0.5 1 1.5 2+ Mortality, ITT 82% Improvement Relative Risk Ventilation time 76% ICU time 73% c19early.org/aa Gordon et al. Sabizabulin for COVID-19 RCT LATE TREATMENT Favors sabizabulin Favors control
[Gordon] Phase 2 RCT of sabizabulin showing lower mortality with treatment. For more discussion see [twitter.com (D)].
0 0.5 1 1.5 2+ ICU admission 81% Improvement Relative Risk ICU admission (b) 86% Mortality -50% Mortality (b) -35% c19early.org/aa Goren et al. NCT04368897 Antiandrogens LATE TREATMENT Favors various Favors control
[Goren] Prospective study of 77 men hospitalized with COVID-19, 12 taking antiandrogens (9 dutasteride, 2 finasteride, 1 spironolactone), showing lower ICU admission with treatment (statistically significant with age-matched controls only when excluding the spironolactone patient). NCT04368897.
0 0.5 1 1.5 2+ Death/ICU -129% Improvement Relative Risk c19early.org/aa Holt et al. Spironolactone for COVID-19 Prophylaxis Favors spironolactone Favors control
[Holt] Retrospective 689 hospitalized COVID-19 patients in Denmark, showing higher risk of ICU/death with spironolactone use in unadjusted results subject to confounding by indication.
0 0.5 1 1.5 2+ Mortality 88% Improvement Relative Risk Ventilation 51% ICU admission 30% Recovery 88% Increase in Ct score 36% c19early.org/aa Hsieh et al. Antiandrogens for COVID-19 LATE Favors antiandrogen Favors control
[Hsieh] Prospective study of 260 hospitalized patients in Taiwan, 117 treated with herbal formula Jing Si Herbal Tea which includes antiandrogen glycyrrhiza glabra, showing improved recovery with treatment, with statistical significance for SpO2, Ct score, CRP, and Brixia score.
0 0.5 1 1.5 2+ Mortality 39% Improvement Relative Risk c19early.org/aa Hunt et al. Antiandrogens for COVID-19 EARLY TREATMENT Favors antiandrogen Favors control
[Hunt] Retrospective 26,508 consecutive COVID+ veterans in the USA, showing lower mortality with multiple treatments including anti-androgens. 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 38% Improvement Relative Risk c19early.org/aa Israel et al. Dutasteride for COVID-19 Prophylaxis Favors dutasteride Favors control
[Israel] Case control study examining medication usage with a healthcare database in Israel, showing lower risk of hospitalization with dutasteride.
0 0.5 1 1.5 2+ Case 77% Improvement Relative Risk c19early.org/aa Jeon et al. Spironolactone for COVID-19 Prophylaxis Favors spironolactone Favors control
[Jeon] Retrospective 6,462 liver cirrhosis patients in South Korea, with 67 COVID+ cases, showing significantly lower cases with spironolactone treatment. Death and ICU results per group are not provided.
0 0.5 1 1.5 2+ Mortality 33% Improvement Relative Risk Progression -8% Case -68% c19early.org/aa Jiménez-Alcaide et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Jiménez-Alcaide] Retrospective 1,349 prostate cancer patients in Spain, 156 on ADT, showing no significant differences in COVID-19 outcomes with treatment.
0 0.5 1 1.5 2+ Hospitalization -229% Improvement Relative Risk Case 29% c19early.org/aa Kazan et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Kazan] Retrospective 365 prostate cancer patients in Turkey, 138 treated with ADT, showing no significant differences with treatment.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk Hospitalization 50% Mortality (b) 67% Hospitalization (b) 71% Mortality (c) 67% Hospitalization (c) 92% Viral clearance 74% c19early.org/aa Kintor et al. NCT04870606 Proxalutamide RCT EARLY Favors proxalutamide Favors control
[Kintor] RCT 733 outpatients, 99% in the USA, showing lower hospitalization/death, and significantly reduced viral load with proxalutamide treatment. The viral clearance result is from [Ma].
0 0.5 1 1.5 2+ Mortality -124% Improvement Relative Risk Case 7% c19early.org/aa Klein et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Klein] Retrospective 1,779 prostate cancer patients, showing no significant differences in COVID-19 outcomes with ADT.
0 0.5 1 1.5 2+ Mortality 46% Improvement Relative Risk Death/ICU 46% Case 11% c19early.org/aa Koskinen et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Koskinen] Retrospective 352 prostate cancer patients in Finland, showing no significant differences in COVID-19 with ADT.
0 0.5 1 1.5 2+ Mortality 17% Improvement Relative Risk ICU admission 11% TFS score 30% c19early.org/aa Kotfis et al. NCT04912011 Potassium canrenoate RCT LATE Favors potassium canrenoate Favors control
[Kotfis] RCT with 24 patients treated with potassium canrenoate and 25 placebo patients in Poland, showing no significant differences.
0 0.5 1 1.5 2+ Mortality 21% Improvement Relative Risk Case -18% c19early.org/aa Kwon et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Kwon] Retrospective 5,211 prostate cancer patients, 799 on ADT, showing no significant differences in COVID-19 outcomes with treatment.
0 0.5 1 1.5 2+ Death/ICU -23% Improvement Relative Risk c19early.org/aa Lazzeri et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Lazzeri] Retrospective case-control study in Italy with 943 male COVID-19 patients, 45 on chronic 5ARI treatment (finasteride/dutasteride). There was significantly fewer COVID-19 patients >55 on 5ARI treatment compared to age-matched controls (5.57 vs. 8.14%, p=0.0083). The difference was greater for men aged >65 (7.14 vs. 12.31%, p=0.0001). There was no significant difference for ICU admission or death.
0 0.5 1 1.5 2+ Severe case 21% Improvement Relative Risk Case 11% c19early.org/aa Lee et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Lee] Retrospective 3,057 androgen deprivation therapy patients in the USA, and 36,096 control patients with cancer, showing lower risk of cases and severity with ADT.
0 0.5 1 1.5 2+ Mortality 17% Improvement Relative Risk Case 7% c19early.org/aa Lyon et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Lyon] Retrospective 944 5ARI users in the USA and 944 matched controls, showing lower risk of COVID-19 cases with treatment.
0 0.5 1 1.5 2+ Case 7% Improvement Relative Risk c19early.org/aa MacFadden et al. Spironolactone for COVID-19 Prophylaxis Favors spironolactone Favors control
[MacFadden] Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing lower cases with chronic use of spironolactone.
0 0.5 1 1.5 2+ SHOKS-COVID score 11% Improvement Relative Risk PCR+ on day 10 or hospi.. 39% Hospitalization time 8% Viral clearance 87% c19early.org/aa Mareev et al. Spironolactone for COVID-19 RCT LATE Favors spironolactone Favors control
[Mareev] Prospective 103 PCR+ patients in Russia, 33 treated with bromexhine+spironolactone, showing lower PCR+ at day 10 or hospitalization >10 days with treatment. Bromhexine 8mg 4 times daily, spironolactone 25-50 mg/day for 10 days.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk Ventilation 97% Hospitalization 91% c19early.org/aa McCoy et al. NCT04446429 Proxalutamide RCT EARLY TREATMENT Favors proxalutamide Favors control
[McCoy] RCT 268 male patients in Brazil, 134 treated with proxalutamide, showing significantly lower hospitalization and mechanical ventilation. NCT04446429.

This paper was censored without details or author response, and the editors have ignored the authors, see [twitter.com (E)].
0 0.5 1 1.5 2+ Mortality 95% Improvement Relative Risk Severe case 75% Case 75% c19early.org/aa Montopoli et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Montopoli] Retrospective 5,273 prostate cancer patients on androgen-deprivation therapy (ADT), and 37,161 not on ADT, showing lower risk of cases with treatment.
0 0.5 1 1.5 2+ Mortality 18% Improvement Relative Risk Ventilation -19% Ongoing hospitalization.. -17% primary Hospitalization time -20% c19early.org/aa Nickols et al. NCT04397718 HITCH Degarelix RCT LATE Favors degarelix Favors control
[Nickols] Early terminated RCT with 62 very late stage (79% on oxygen) degarelix patients and 34 placebo patients, showing no significant differences with treatment.

For discussion of many issues with this study see [twitter.com (F)].
0 0.5 1 1.5 2+ Mortality 55% Improvement Relative Risk Ventilation 69% Hospitalization 77% c19early.org/aa Patel et al. Antiandrogens for COVID-19 Prophylaxis Favors various Favors control
[Patel] Retrospective 58 prostate cancer patients in the USA, showing lower risk of hospitalization with ADT.
0 0.5 1 1.5 2+ Mortality