考慮區域性差異之多區域藥物臨床試驗之評估與設計 Design and Evaluation of Multi-regional Clinical Trials With Heterogeneous Treatment Effect Across Regions Chi-Tian Chen Advisor ： Chin-Fu Hsiao, Ph.D. Yun-Ming Pong, Ph.D. Abstrat To speed up drug development to allow faster access to medicines for patients globally, conducting multi- regional clinical trials incorporating subjects from many countries around the world under the same protocol may be desired. Several statistical methods have been purposed for the design and evaluation of multi-regional clinical trials. However, in most of recent approaches for sample size determination in multi-regional clinical trials, a common treatment effect of the primary endpoint (continuous or binary endpoint) across regions is usually assumed. In practice, it might be expected that there is a difference in treatment effect due to regional difference (e.g., ethnic difference). For continuous endpoint, a random effect model for heterogeneous treatment effect across regions is proposed for the design and evaluation of multi-regional clinical trials in this dissertation. For binary endpoint, a power-function distribution is used to describe heterogeneous treatment effect across regions for the design and evaluation of multi-regional clinical trials. We also address consideration on the determination of the number of subjects in a specific region to establish the consistency of treatment effects between the specific region and the entire group. Material and result 1. Continuous case For continuous endpoints, a random effect model is used to describe the heterogeneity of the treatment results from the participated regions in a multi- regional clinical trial. An estimate of the overall treatment difference, the formulas for the sample size determination, and a test statistic for testing nonpositive effect between a test product and a placebo control are included. 2. Binary case In clinical trials, a binary study endpoint such as response rate is often considered as the primary endpoint for evaluation of the efficacy of a test treatment. In this chapter, we will establish statistical methodology by considering heterogeneity among regions in a multi-regional clinical trial for binary endpoint. A power-function distribution will be used to describe the heterogeneity among regions. 3. Examples In order to illustrate our approaches, suppose that a multi-regional clinical trial will be conducted in 3 regions (for example, Taiwan, European Union (EU), and United States (USA)). let p 1 be the proportion of patients in the smallest region, p 2 the proportion of patients in the second smallest region, and p 3 the proportion of patients in the largest region. For continuous case, the first line in Table 2.1 corresponds to a design with (p 1, p 2, p 3 )=(0.1, 0.1, 0.8), (θ 1, θ 2, θ 3 )=(10, 15, 15), and τ 2 =1. In this case, the total sample size required per group in the multi-regional clinical trial is 64, and the assurance probability for the first region is This indicates that the sample size for the smallest region has to be around 10% of the overall sample size to maintain the assurance probability at 80% level. For binary case, the first panel in Table 3.1 corresponds to a design with (r 1 T, r 2 T, r 3 T )=(35%,40%,40%), r 1 C = r 2 C = r 3 C =20%, and p 1 = p 2 < p 3. That is, (π 1, π 2, π 3 )=(15%, 20%, 20%). When (p 1, p 2, p 3 )=(0.1, 0.1, 0.8), the total sample size required per group in the multi-regional clinical trial is 179, and the assurance probability for the first region is As seen in Table 3.1, the sample size for the smallest region has to be around 20% of the overall sample size to maintain the assurance probability at 80% level. Reference 1.Chow, S. C., Shao, J., and Wang, H. (2002). A note on sample size calculation for mean comparisons based on noncentral t-statistics. Journal of Biopharmaceutical Statistics 12(4): 441– Caraco Y. (2004). Genes and the response to drugs. The New England Journal of Medicine 351(27):2867– DerSimonia R. and Laird N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials 7: Eichebaum M., Spannbrucker N., Steincke B., et al. 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