Decision-making with multiple correlated binary outcomes in clinical trials.

ABSTRACT: Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all outcome(s); and lacks a compensatory mechanism that allows large positive effects on one or multiple outcome(s) to outweigh small negative effects on other outcomes. To address these shortcomings, th

SUBMITTER: Kavelaars X 

PROVIDER: S-EPMC7682528 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature