Empirical shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials
| Autor: | Ji Zhang, Weichung Joe Shih, Joshua Chen, Mingyu Li, Hui Quan, Soo Peter Ouyang, Peng-Liang Zhao |
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| Rok vydání: | 2012 |
| Předmět: |
Statistics and Probability
Shrinkage estimator Time Factors Epidemiology Biostatistics Minimum-variance unbiased estimator Bias of an estimator Consistency (statistics) Drug Discovery Statistics Econometrics Humans Multicenter Studies as Topic Probability Randomized Controlled Trials as Topic Mathematics Interpretability Heart Failure Clinical Trials as Topic Models Statistical Estimator Bayes Theorem Random effects model Sample size determination Data Interpretation Statistical Delayed-Action Preparations Sample Size Metoprolol |
| Zdroj: | Statistics in Medicine. 32:1691-1706 |
| ISSN: | 0277-6715 |
| DOI: | 10.1002/sim.5543 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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