Decision-making with multiple correlated binary outcomes in clinical trials

Xynthia Kavelaars*, Joris Mulder, Maurits Kaptein

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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, this paper proposes (1) a Bayesian model for the analysis of correlated binary outcomes based on the multivariate Bernoulli distribution; and (2) a flexible decision criterion with a compensatory mechanism that captures the relative importance of the outcomes. A simulation study demonstrates that efficient and unbiased decisions can be made while Type I error rates are properly controlled. The performance of the framework is illustrated for (1) fixed, group sequential, and adaptive designs; and (2) non-informative and informative prior distributions.
Original languageEnglish
Pages (from-to)3265-3277
JournalStatistical Methods in Medical Research
Volume29
Issue number11
DOIs
Publication statusPublished - 2020

Keywords

  • Bayesian analysis
  • CONSTRUCTIONS
  • END-POINTS
  • Multiple outcomes
  • SAMPLE-SIZE DETERMINATION
  • compensatory decision rules
  • efficiency
  • multivariate Bernoulli model

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