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\begin{document}

\title{Drug repurposing for COVID-19: the problem of excessive hypothesis
testing}



\author[1]{Mariusz Maziarz}%
\author[1]{Adrian Stencel}%
\affil[1]{Jagiellonian University}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
Rationale, aims, and objectives The current strategy of searching for an
effective drug to treat COVID-19 relies mainly on repurposing existing
therapies developed to target other diseases. There are currently more
than four thousand active studies assessing the efficacy of existing
drugs as therapies for COVID-19. The number of ongoing trials and the
urgent need for a treatment poses the risk that false-positive results
will be incorrectly interpreted as evidence for treatments' efficacy and
a ground for drug approval. Our purpose is to assess the risk of
false-positive outcomes by analyzing the mechanistic evidence for the
efficacy of exemplary candidates for repurposing, estimate false
discovery rate, and discuss solutions to the problem of excessive
hypothesis testing. Methods We estimate the expected number of
false-positive results and probability of at least one false-positive
result under the assumption that all tested compounds have no effect on
the course of the disease. Later, we relax this assumption and analyze
the sensitivity of the expected number of true-positive results to
changes in the prior probability (\selectlanguage{greek}π\selectlanguage{english}) that tested compounds are
effective. Finally, we calculate False Positive Report Probability and
expected numbers of false-positive and true-positive results for
different thresholds of statistical significance, power of studies, and
ratios of effective to non-effective compounds. We also review
mechanistic evidence for the efficacy of two exemplary repurposing
candidates (hydroxychloroquine and ACE2 inhibitors) and assess its
quality to choose the plausible values of the prior probability (\selectlanguage{greek}π\selectlanguage{english}) that
tested compounds are effective against COVID-19. Results Our analysis
shows that, due to the excessive number of statistical tests in the
field of drug repurposing for COVID-19 and low prior probability (\selectlanguage{greek}π\selectlanguage{english}) of
the efficacy of tested compounds, positive results are far more likely
to result from type-I error than reflect the effects of pharmaceutical
interventions.%
\end{abstract}%



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