Decision-making with multiple correlated binary outcomes in clinical trials

TL;DR

This paper introduces a Bayesian framework for analyzing multiple correlated binary outcomes in clinical trials, addressing limitations of existing methods by modeling outcomes jointly and incorporating a compensatory decision mechanism.

Contribution

It proposes a multivariate Bernoulli Bayesian model and a flexible decision criterion to improve superiority decisions in multi-outcome clinical trials.

Findings

Efficient and unbiased decision-making demonstrated in simulations

Proper control of Type I error rates achieved

Framework applicable to various trial designs and priors

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 outcomes(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.