title: Assessing the consistency of the treatment effect under the discrete random effects model in multiregional clinical trials.
abstract: In recent years, developing pharmaceutical products via multiregional clinical trials (MRCTs) has become standard. Traditionally, an MRCT would assume that a treatment effect is uniform across regions. However, heterogeneity among regions may have impact upon the evaluation of a medicine's effect. In this study, we consider a random effects model using discrete distribution (DREM) to account for heterogeneous treatment effects across regions for the design and evaluation of MRCTs. We derive an power function for a treatment that is beneficial under DREM and illustrate determination of the overall sample size in an MRCT. We use the concept of consistency based on Method 2 of the Japanese Ministry of Health, Labour, and Welfare's guidance to evaluate the probability for treatment benefit and consistency under DREM. We further derive an optimal sample size allocation over regions to maximize the power for consistency. Moreover, we provide three algorithms for deriving sample size at the desired level of power for benefit and consistency. In practice, regional treatment effects are unknown. Thus, we provide some guidelines on the design of MRCTs with consistency when the regional treatment effect are assumed to fall into a specified interval. Numerical examples are given to illustrate applications of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.
authors.forename: Jung-Tzu
authors.initials: JT
authors.lastname: Liu
authors.affiliation: Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.|Graduate Institute of Biostatistics, College of Public Health, China Medical University, Taichung, Taiwan.
authors.forename: Hsiao-Hui
authors.initials: HH
authors.lastname: Tsou
authors.affiliation: Janssen R & D, Pharmaceutical Companies of Johnson & Johnson, Raritan, NJ, U.S.A.
authors.forename: K K
authors.initials: KK
authors.lastname: Gordon Lan
authors.affiliation: Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
authors.forename: Chi-Tian
authors.initials: CT
authors.lastname: Chen
authors.affiliation: Software Design Center, Cloud Systems Dept. FIH Mobile Limited, New Taipei City, Taiwan.
authors.forename: Yi-Hsuan
authors.initials: YH
authors.lastname: Lai
authors.affiliation: Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
authors.forename: Wan-Jung
authors.initials: WJ
authors.lastname: Chang
authors.affiliation: Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.
authors.forename: Chyng-Shyan
authors.initials: CS
authors.lastname: Tzeng
authors.affiliation: Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
authors.forename: Chin-Fu
authors.initials: CF
authors.lastname: Hsiao
mesh_terms: D000465:Algorithms; D056808:Biostatistics; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data; D011336:Probability; D018401:Sample Size; D016896:Treatment Outcome
keywords: consistency; discrete random effects model; multiregional clinical trial; optimal sample size allocation; power for benefit and consistency