Literature DB >> 22855311

Empirical shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials.

Hui Quan1, Mingyu Li, Weichung Joe Shih, Soo Peter Ouyang, Joshua Chen, Ji Zhang, Peng-Liang Zhao.   

Abstract

Multi-regional clinical trials have been widely used for efficient global new drug developments. Both a fixed-effect model and a random-effect model can be used for trial design and data analysis of a multi-regional clinical trial. In this paper, we first compare these two models in terms of the required sample size, type I error rate control, and the interpretability of trial results. We then apply the empirical shrinkage estimation approach based on the random-effect model to two criteria of consistency assessment of treatment effects across regions. As demonstrated in our computations, compared with the sample estimator, the shrinkage estimator of the treatment effect of an individual region borrowing information from the other regions is much closer to the estimator of the overall treatment effect, has smaller variability, and therefore provides much higher probability for demonstrating consistency. We use a multinational trial example with time to event endpoint to illustrate the application of the method.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 22855311     DOI: 10.1002/sim.5543

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  2 in total

1.  Statistical design of noninferiority multiple region clinical trials to assess global and consistent treatment effects.

Authors:  Guoqing Diao; Donglin Zeng; Joseph G Ibrahim; Alan Rong; Oliver Lee; Kathy Zhang; Qingxia Chen
Journal:  J Biopharm Stat       Date:  2017-03-15       Impact factor: 1.051

2.  Challenges from Variation across Regions in Cost Effectiveness Analysis in Multi-Regional Clinical Trials.

Authors:  Yunbo Chu; Luyan Dai; Sheng Qi; Matthew Lee Smith; Hui Huang; Yang Li; Ye Shen
Journal:  Front Pharmacol       Date:  2016-10-28       Impact factor: 5.810

  2 in total

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