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

Statistical Methods in Medical Research
Xynthia KavelaarsMaurits Kaptein

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) ...Continue Reading

References

Aug 6, 2010·Statistics in Medicine·Takashi SozuToshimitsu Hamasaki
Jul 26, 2012·Biometrical Journal. Biometrische Zeitschrift·Takashi SozuToshimitsu Hamasaki
Jun 23, 2015·Journal of Biopharmaceutical Statistics·Takashi SozuToshimitsu Hamasaki
May 18, 2016·Statistics in Medicine·Thomas A MurrayYing Yuan
May 26, 2018·Educational and Psychological Measurement·Maarten Marsman, Eric-Jan Wagenmakers
Jan 6, 2019·Proceedings of the National Academy of Sciences of the United States of America·Daniel J Wilson

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