Covid Analysis, June 2023
•Statistically significant improvements are seen for mortality, ventilation, ICU admission, hospitalization, recovery, cases, and viral clearance. 30 studies from 24 independent teams in 12 different countries show statistically significant improvements in isolation (17 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows 30% [20‑38%] improvement. Results are better for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
•Results are robust — in exclusion sensitivity analysis 24 of 49 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.
•No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are more effective. Only 18% of antiandrogen studies show zero events with treatment.
•All data to reproduce this paper and sources are in the appendix. Other meta analyses for antiandrogen can be found in [Cheema, Kotani], showing significant improvements for mortality, hospitalization, recovery, and progression.
|All studies||Early treatment||Late treatment||Studies||Patients||Authors|
|All studies||30% [20‑38%]|
|Randomized Controlled TrialsRCTs||58% [37‑73%]|
|RCT mortality||63% [46‑75%]|
|71% [-75‑95%]||62% [41‑75%]
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 51 treatments.
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.
An In Silico study supports the efficacy of antiandrogens [Saih].
An In Vitro study supports the efficacy of antiandrogens [Majidipur].
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.
|All studies||30% [20‑38%]|
|After exclusions||31% [22‑39%]|
|Peer-reviewed studiesPeer-reviewed||27% [17‑36%]|
|Randomized Controlled TrialsRCTs||58% [37‑73%]|
|ICU admissionICU||34% [5‑54%]|
|RCT mortality||63% [46‑75%]|
|RCT hospitalizationRCT hosp.||32% [3‑53%]|
|Early treatment||Late treatment||Prophylaxis|
|All studies||44% [31‑55%]|
|After exclusions||39% [29‑48%]|
|Peer-reviewed studiesPeer-reviewed||40% [31‑49%]|
|Randomized Controlled TrialsRCTs||64% [26‑82%]|
|ICU admissionICU||-||42% [24‑55%]|
|21% [-10‑43%]||16% [-33‑47%]|
|RCT mortality||71% [-75‑95%]||62% [41‑75%]|
|RCT hospitalizationRCT hosp.||81% [46‑93%]|
Figure 13 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 65% improvement, compared to 21% 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.
[Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
[Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
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.
Heterogeneity in COVID-19 studies arises from many factors including:
[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.
|Post exposure prophylaxis||86% 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 51 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 51 treatments.
[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].
[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.
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.
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.
[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.
Figure 20 shows a scatter plot of results for prospective and retrospective studies. 48% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 77% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 20% improvement, compared to 74% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.
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.
heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, 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.
[Cheema, Kotani], showing significant improvements for one or more of mortality, hospitalization, recovery, and progression.
Antiandrogens are an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ventilation, ICU admission, hospitalization, recovery, cases, and viral clearance. 30 studies from 24 independent teams in 12 different countries show statistically significant improvements in isolation (17 for the most serious outcome). Meta analysis using the most serious outcome reported shows 30% [20‑38%] improvement. Results are better for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Results are robust — in exclusion sensitivity analysis 24 of 49 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.
[Abbasi] RCT including 51 spironolactone patients and 87 control patients in Iran, showing improved recovery with spironolactone, sitagliptin, and the combination of both.
[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)].
[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).
[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.
[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.
[Cousins] PSM retrospective 898,303 hospitalized COVID-19 patients in the USA, 16,324 on spironolactone, showing lower mortality and ventilation with spironolactone use.
[Cousins (B)] 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.
Authors also present In Vitro research showing dose-dependent inhibition in a human lung epithelial cell line.
[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.
[Davidsson] Retrospective 655 prostate cancer patients in Sweden, showing no significant difference in seropositivity with ADT.
[Duarte] Retrospective 199 prostate cancer patients hospitalized with COVID-19 in Brazil, showing no significant difference in mortality with active ADT.
[Elkazzaz] RCT with 20 13-cis-retinoic acid patients and 20 control patients, showing faster recovery and viral clearance with treatment. Aerosolized 13-cis-retinoic acid with increasing dose from 0.2 mg/kg/day to 4 mg/kg/day for 14 days, plus oral 13-cis-retinoic acid 20 mg/day. 13-cis retinoic acid, also known as isotretinoin, is a synthetic vitamin A derivative that has been shown to have antiandrogenic effects .
[Ersoy] Retrospective 30 COVID-19 ARDS ICU patients and 30 control patients, showing lower mortality with treatment.
[Gedeborg] Case control study with 474 patients that died of COVID-19 in Sweden, showing higher risk with ADT, without statistical significance.
[Ghandehari] RCT 42 hospitalized patients in the USA, showing improved recovery and lower progression with progesterone treatment.
[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.
[Gordon] Phase 2 RCT of sabizabulin showing lower mortality with treatment. For more discussion see [twitter.com (D)].
[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.
[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.
[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.
[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.
[Israel] Case control study examining medication usage with a healthcare database in Israel, showing lower risk of hospitalization with dutasteride.
[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.