title: 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.
authors.affiliation: a Lilly Research Laboratories, Eli Lilly and Company , Indianapolis , Indiana , USA.
authors.forename: Brian A
authors.initials: BA
authors.lastname: Millen
authors.forename: Alex
authors.initials: A
authors.lastname: Dmitrienko
authors.forename: Guochen
authors.initials: G
authors.lastname: Song
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000188:drug therapy; D001499:Bayes Theorem; D015415:Biomarkers; D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy; D011153:Population; D057285:Precision Medicine; D012107:Research Design / Q000706:statistics & numerical data*
chemical_list: D015415:Biomarkers