Bayesian Assessment of the Influence and Interaction Conditions in Multipopulation Tailoring Clinical Trials

Abstract

Multipopulation tailoring trials provide a trial design option that supports the realization of tailored therapeutics or personalized medicine. Several recent publications have focused on statistical and clinical considerations that arise in these trials that are designed to study the overall treatment effect in a population of interest as well as one or more prospectively defined subpopulations. Millen et al. (2012) introduced the influence and interaction conditions as part of a general framework to facilitate decision making in multipopulation trials. This article provides Bayesian methods for assessing the influence and interaction conditions. The methods introduced are illustrated using case studies based on clinical trials with biomarker-driven designs.

Key Words:

• Decision making
• Influence condition
• Interaction condition
• Subgroup analysis
• Tailored therapeutics

Notes

Note. p-Values correspond to the primary hypotheses for the trial. The treatment effect in the biomarker-negative subgroup was not evaluated.

Note. p-Values correspond to the primary hypotheses for the trial. The treatment effect in the biomarker-negative subgroup was not evaluated.

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/lbps.