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Decision-making with multiple correlated binary outcomes in clinical trials

Kavelaars,Xynthia
; 
Mulder,Joris
; 
Kaptein,Maurits
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.
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Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Dutch Research Council (NWO) [grant no. 406.18.505].
Date
2020
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Tilburg University Research Output Collection
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Keywords
Bayesian analysis, CONSTRUCTIONS, END-POINTS, Multiple outcomes, SAMPLE-SIZE DETERMINATION, compensatory decision rules, efficiency, multivariate Bernoulli model
Citation
Kavelaars, X, Mulder, J & Kaptein, M 2020, 'Decision-making with multiple correlated binary outcomes in clinical trials', Statistical Methods in Medical Research, vol. 29, no. 11, pp. 3265-3277. https://doi.org/10.1177/0962280220922256
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https://hdl.handle.net/20.500.14602/73590
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