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分组序贯富集设计结合亚组选择。

Group sequential enrichment design incorporating subgroup selection.

机构信息

Novartis Pharma AG, Basel, Switzerland.

出版信息

Stat Med. 2013 Jul 20;32(16):2695-714. doi: 10.1002/sim.5738. Epub 2013 Jan 13.

DOI:10.1002/sim.5738
PMID:23315698
Abstract

An important component of clinical trials in drug development is the analysis of treatment efficacy in patient subgroups (subpopulations). Because of concerns of multiplicity and of the small sample sizes often involved, such analyses can present substantial statistical challenges and may lead to misleading conclusions. As a confirmatory seamless phase II/III design, we propose an adaptive enrichment group sequential procedure whereby resources can be concentrated on subgroups most likely to respond to treatment. Stopping boundaries are defined through upper and lower spending functions. The procedure is presented in terms of the efficient score, enabling the analysis of normal, binary, or time-to-event data. It addresses the dilution effect by eliminating populations at the first stage that appear likely to derive no therapeutic benefit. It subsequently proceeds with the definitive assessment of treatment efficacy among the remaining pooled populations using a group sequential design. The procedure provides strong protection of familywise type I error rate, and we employ a bootstrap algorithm to obtain point and interval estimates that are adjusted for the selection bias. We give examples to demonstrate how the design is used. We make comparisons with adaptive two-stage combination test procedures and with a group sequential test that does not account for the presence of subgroups. Numerical results show that the procedure has high power to detect subgroup-specific effects and the use of multiple interim analysis points can lead to substantial sample size savings.

摘要

药物开发临床试验的一个重要组成部分是分析患者亚组(亚群)的治疗效果。由于多重性和涉及的小样本量的担忧,这种分析可能会带来重大的统计挑战,并可能导致误导性的结论。作为一种确证性无缝的 II/III 期设计,我们提出了一种适应性富集组序贯程序,通过该程序可以将资源集中在最有可能对治疗有反应的亚组上。停止边界通过上限和下限支出函数来定义。该程序是根据有效评分提出的,能够分析正态、二项式或生存数据。它通过在第一阶段消除那些似乎没有治疗益处的人群来消除稀释效应。随后,使用组序贯设计对剩余的合并人群进行治疗效果的确定性评估。该程序为总体 I 型错误率提供了强有力的保护,我们使用自举算法来获得经过选择偏差调整的点估计和区间估计。我们给出了示例来说明如何使用该设计。我们将其与适应性两阶段组合检验程序以及不考虑亚组存在的组序贯检验进行了比较。数值结果表明,该程序具有检测亚组特异性效应的高功效,并且使用多个中期分析点可以导致样本量的大幅节省。

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