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一种多臂多阶段平台设计,允许在预计划中增加手臂,同时仍能控制家族错误率。

A multi-arm multi-stage platform design that allows preplanned addition of arms while still controlling the family-wise error.

机构信息

Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.

Exeter Clinical Trials Unit, University of Exeter, Exeter, UK.

出版信息

Stat Med. 2024 Aug 30;43(19):3613-3632. doi: 10.1002/sim.10135. Epub 2024 Jun 16.

Abstract

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.

摘要

人们对平台试验越来越感兴趣,这些试验允许在试验进行过程中增加新的治疗组,并且可以在试验中途停止治疗,无论是因为缺乏益处/无效还是因为优越性。在某些情况下,平台试验需要保证错误率得到控制。本文提出了一种多阶段设计,允许在平台试验中以预先计划的方式添加额外的治疗组,同时仍然控制总体错误率,前提是已知要添加的治疗组的数量和时间,并且没有时间趋势。给出了一种计算所需样本量以达到预期功效水平的方法,并展示了如何找到样本量分布和预期样本量。我们专注于最不利配置下的功效,即从一组治疗中找到具有临床相关效果的治疗的功效,而其余治疗的效果则没有意义。提出了一个激励性试验,重点关注两种情况,第一种是每个活跃治疗组的固定阶段数,第二种是总阶段数固定,后来添加的治疗组的阶段数较少。与 Bonferroni 相比,在有三个治疗组的试验中,总最大样本量的节省幅度较小,不到总样本量的 1%。然而,在有更多治疗组的试验中,节省幅度更大。

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