Going multivariate in clinical trial studies: A Bayesian framework for multiple binary outcomes

Xynthia Kavelaars*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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Abstract

In an era where medicine is increasingly personalized, clinical trials often suffer from small samples. As a consequence, treatment comparison based on the data of these trials may result in inconclusive decisions. Efficient decision-making strategies are highly needed so decisions can be made with smaller samples without increasing the risk of errors. The current chapter centers around one such strategy: Including information from multiple outcomes in the decision, thereby focusing on data from binary outcomes. Key elements of the approach are (1) criteria for treatment comparison that are suitable for two outcomes, and (2) a multivariate Bayesian technique to analyze multiple binary outcomes simultaneously. The conceptual discussion of these elements is complemented with software to implement the approach. To further facilitate trials with small samples, the chapter also outlines how interim analyses may result in more efficient decisions compared to the traditional sample size estimation before data collection.
Original languageEnglish
Title of host publicationSmall sample size solutions
Subtitle of host publicationA guide for applied researchers and practitioners
EditorsR. van de Schoot, M. Miočević
PublisherRoutledge
Chapter10
Number of pages16
ISBN (Print)9780367222222
Publication statusPublished - 2020

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    Kavelaars, X. (2020). Going multivariate in clinical trial studies: A Bayesian framework for multiple binary outcomes. In R. van de Schoot, & M. Miočević (Eds.), Small sample size solutions: A guide for applied researchers and practitioners Routledge.