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联合主要终点和次要终点分析的多重比较程序。

Multiple Comparisons Procedures for Analyses of Joint Primary Endpoints and Secondary Endpoints.

作者信息

Luo Xiaolong, Li Lerong, Savenkov Oleksandr, Liu Weijian, Ni Xiao, Tang Weihua, Guo Wenge

机构信息

Biometrics, Sarepta Therapeutics, Cambridge, Massachusetts, USA.

Department of Mathematical Sciences, New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA.

出版信息

Pharm Stat. 2025 May-Jun;24(3):e70010. doi: 10.1002/pst.70010.

Abstract

One of the main challenges in drug development for rare diseases is selecting the appropriate primary endpoints for pivotal studies. Although many endpoints can effectively reflect clinical benefit, their sensitivity often varies, making it difficult to determine the required sample size for study design and to interpret final results, which may be underpowered for some or all endpoints. This complexity is further compounded when there is a desire to support regulatory claims for multiple clinical endpoints and dose regimens due to the issues of multiplicity and sample size constraints. Joint Primary Endpoints (JPEs) offer a compelling strategy to address these challenges; however, their analysis in conjunction with component endpoints presents additional complexities, particularly in managing multiplicity concerns for regulatory claims. To address these issues, this paper introduces a robust two-stage gatekeeping framework designed to test two hierarchically ordered families of hypotheses. A novel truncated closed testing procedure is employed in the first stage, enhancing flexibility and adaptability in the evaluation of primary endpoints. This approach strategically propagates a controlled fraction of the error rate to the second stage for assessing secondary endpoints, ensuring rigorous control of the global family-wise Type I error rate across both stages. Through extensive numerical simulations and real-world clinical trial applications, we demonstrate the efficiency, adaptability, and practical utility of this approach in advancing drug development for rare diseases while meeting stringent regulatory requirements.

摘要

罕见病药物研发的主要挑战之一是为关键研究选择合适的主要终点。尽管许多终点可以有效反映临床获益,但其敏感性往往各不相同,这使得难以确定研究设计所需的样本量以及解读最终结果,因为对于某些或所有终点而言,研究可能缺乏足够的效力。当由于多重性问题和样本量限制而希望支持针对多个临床终点和剂量方案的监管声明时,这种复杂性会进一步加剧。联合主要终点(JPEs)提供了一种应对这些挑战的有力策略;然而,将其与组成终点一起进行分析会带来更多复杂性,尤其是在处理监管声明的多重性问题时。为了解决这些问题,本文引入了一个稳健的两阶段把关框架,旨在检验两个层次有序的假设族。在第一阶段采用了一种新颖的截断封闭检验程序,增强了在评估主要终点时的灵活性和适应性。这种方法将一定比例的错误率有策略地传递到第二阶段以评估次要终点,确保在两个阶段对全局家族性I型错误率进行严格控制。通过广泛的数值模拟和实际临床试验应用,我们证明了该方法在推进罕见病药物研发、同时满足严格监管要求方面的效率、适应性和实际效用。

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