title: A predictive Bayesian approach to the design and analysis of bridging studies.
abstract: Pharmaceutical product development culminates in confirmatory trials whose evidence for the product's efficacy and safety supports regulatory approval for marketing. Regulatory agencies in countries whose patients were not included in the confirmatory trials often require confirmation of efficacy and safety in their patient populations, which may be accomplished by carrying out bridging studies to establish consistency for local patients of the effects demonstrated by the original trials. This article describes and illustrates an approach for designing and analyzing bridging studies that fully incorporates the information provided by the original trials. The approach determines probability contours or regions of joint predictive intervals for treatment effect and response variability, or endpoints of treatment effect confidence intervals, that are functions of the findings from the original trials, the sample sizes for the bridging studies, and possible deviations from complete consistency with the original trials. The bridging studies are judged consistent with the original trials if their findings fall within the probability contours or regions. Regulatory considerations determine the region definitions and appropriate probability levels. Producer and consumer risks provide a way to assess alternative region and probability choices.   [Supplemental materials are available for this article. Go to the Publisher's online edition of the Journal of Biopharmaceutical Statistics for the following free supplemental resource: Appendix 2: R code for Calculations.].
authors.affiliation: Merck Research Laboratories, Merck & Co., Inc., North Wales, PA 19454, USA. larry_gould@merck.com
authors.forename: A Lawrence
authors.initials: AL
authors.lastname: Gould
authors.forename: Tian
authors.initials: T
authors.lastname: Jin
authors.forename: Li Xin
authors.initials: LX
authors.lastname: Zhang
authors.forename: William W B
authors.initials: WW
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D004345:Drug Industry; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic; D012107:Research Design / Q000706:statistics & numerical data*; D012306:Risk; D018401:Sample Size