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适应性富集设计中的样本量再估计。

Sample size re-estimation in adaptive enrichment design.

机构信息

Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA.

Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China.

出版信息

Contemp Clin Trials. 2021 Jan;100:106216. doi: 10.1016/j.cct.2020.106216. Epub 2020 Nov 24.

Abstract

Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.

摘要

临床试验参与者通常具有异质性,这是精准医学这一快速发展领域的一个基本问题。参与者的异质性给当前的 III 期试验设计带来了相当大的困难。适应性富集设计提供了一种灵活直观的解决方案。在中期分析时,我们富集那些更有可能从新治疗中获益的试验参与者亚组。然而,在富集某些特定的参与者亚组后,控制检验效能和保持足够的功效是至关重要的。我们提出了两种具有样本量重估的自适应富集策略,并通过广泛的模拟和敏感性分析验证了它们的可行性和实用性。模拟研究表明,所提出的方法可以控制总体Ⅰ类错误率,并在统计功效和预期样本量方面具有竞争性的改进。通过实际的试验应用说明了所提出的设计。

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