•Statistically significant improvements are seen for mortality, ventilation, recovery, cases, and viral clearance. 22 studies from 20 independent teams in 12 different countries show statistically significant
improvements in isolation (14 for the most serious outcome).
•Results are robust — in exclusion sensitivity analysis 20 of 39
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. Proxalutamide is analyzed separately.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
Only 13% of antiandrogen
studies show zero events in the treatment arm.
Multiple treatments are typically used
in combination, and other treatments
are more effective.
•No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used.
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•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.
We show traditional outcome specific analyses and combined
evidence from all studies, incorporating treatment delay, a primary
confounding factor in COVID-19 studies.
Figure 1.A. Random effects
meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
B. Scatter plot showing the
distribution of effects reported in studies. C. History of all reported
effects (chronological within treatment stages).
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, for studies within each treatment stage, for individual outcomes, for
peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after
exclusions.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
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.
Figure 3 shows a visual overview of the results, with details in
Table 1 and Table 2.
Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13
show forest plots for a 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.
Figure 4. Random effects meta-analysis for all studies with pooled effects.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
