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Stratified randomization for platform trials with differing experimental arm eligibility
Clinical Trials ( IF 2.7 ) Pub Date : 2021-08-21 , DOI: 10.1177/17407745211028872
Subodh Selukar 1 , Susanne May 1 , Dave Law 2 , Megan Othus 3
Affiliation  

Background:

Platform trials facilitate efficient use of resources by comparing multiple experimental agents to a common standard of care arm. They can accommodate a changing scientific paradigm within a single trial protocol by adding or dropping experimental arms—critical features for trials in rapidly developing disease areas such as COVID-19 or cancer therapeutics. However, in these trials, efficacy and safety issues may render certain participant subgroups ineligible to some experimental arms, and methods for stratified randomization do not readily apply to this setting.

Methods:

We propose extensions for conventional methods of stratified randomization for platform trials whose experimental arms may differ in eligibility criteria. These methods balance on a prespecified set of stratification variables observable prior to arm assignment that remains the same across experimental arms. To do so, we suggest modifying block randomization by including experimental arm eligibility as a stratifying variable, and we suggest modifying the imbalance score calculation in dynamic balancing by performing pairwise comparisons between each eligible experimental arm and standard of care arm participants eligible to that experimental arm.

Results:

We provide worked examples to illustrate the proposed extensions. In addition, we also provide a formula to quantify the relative efficiency loss of platform trials with varying eligibility compared with trials with non-varying eligibility as measured by the size of the common standard of care arm.

Conclusions:

This article presents important extensions to conventional methods for stratified randomization in order to facilitate the implementation of platform trials with differing experimental arm eligibility.



中文翻译:

具有不同实验组资格的平台试验的分层随机化

背景:

平台试验通过将多种实验药物与通用标准的护理臂进行比较来促进资源的有效利用。他们可以通过添加或删除实验臂来在单个试验方案中适应不断变化的科学范式——这是在快速发展的疾病领域(如 COVID-19 或癌症疗法)进行试验的关键特征。然而,在这些试验中,有效性和安全性问题可能会使某些参与者亚组不符合某些实验组的条件,并且分层随机化方法并不容易适用于这种情况。

方法:

我们建议扩展平台试验的分层随机化传统方法,其实验组的资格标准可能不同。这些方法在手臂分配之前可观察到的一组预先指定的分层变量上进行平衡,这些变量在整个实验组中保持不变。为此,我们建议通过将实验组资格作为分层变量来修改区块随机化,并且我们建议通过在每个符合条件的实验组和符合该实验组资格的护理标准组参与者之间进行成对比较来修改动态平衡中的不平衡分数计算.

结果:

我们提供了工作示例来说明提议的扩展。此外,我们还提供了一个公式来量化具有不同资格的平台试验与具有非变化资格的试验相比的相对效率损失,以通用标准护理臂的规模衡量。

结论:

本文介绍了对分层随机化传统方法的重要扩展,以促进具有不同实验组资格的平台试验的实施。

更新日期:2021-08-21
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