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Multi‐regional clinical trial design and consistency assessment of treatment effects
Author(s) -
Quan Hui,
Mao Xuezhou,
Chen Joshua,
Shih Weichung Joe,
Ouyang Soo Peter,
Zhang Ji,
Zhao PengLiang,
Binkowitz Bruce
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6108
Subject(s) - estimator , sample size determination , consistency (knowledge bases) , computer science , econometrics , statistics , sample (material) , computation , random effects model , covariate , mathematics , algorithm , artificial intelligence , medicine , meta analysis , chemistry , chromatography
We can apply both fixed and random effects models to multi‐regional clinical trial (MRCT) design and data analysis. Thoroughly, understanding the features of these models in an MRCT setting will help assessing their applicability to an MRCT. In this paper, we discuss the interpretations of trial results from these models. We also evaluate the impact of the number of regions and the sample size configuration across the regions on the required total sample size for the overall treatment effect assessment. For quantifying treatment effects of individual regions, the empirical shrinkage estimator and the James–Stein type shrinkage estimator associate with smaller variability compared with the regular sample estimator. We conduct computation and simulation to compare the performance of these estimators when they are applied to assess consistency of treatment effects across regions. We use a multinational trial example to illustrate the application of these methods. Copyright © 2014 John Wiley & Sons, Ltd.