Reducing the false discovery rate of preclinical animal research with Bayesian statistical decision criteria.

ABSTRACT: The success of preclinical research hinges on exploratory and confirmatory animal studies. Traditional null hypothesis significance testing is a common approach to eliminate the chaff from a collection of drugs, so that only the most promising treatments are funneled through to clinical research phases. Balancing the number of false discoveries and false omissions is an important aspect to consider during this process. In this paper, we compare several preclinical research pipelines, either based on null hypothesis significance testing or based on Bayesian statistical decision criteria. We build on a recently published large-scale meta-analysis of reported effect sizes in preclinical animal research and elicit a non-informative prior distribution under which both approaches are compared. A

SUBMITTER: Kelter R 

PROVIDER: S-EPMC10563376 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature