Power/sample size calculations for assessing correlates of risk in clinical efficacy trials.
Power/sample size calculations for assessing correlates of risk in clinical efficacy trials.
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DOI：
10.1002/sim.6952
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发表时间：
2016-09-20
影响因子：
2
通讯作者：
Huang Y
中科院分区：
文献类型：
--
作者：
Gilbert PB;Janes HE;Huang Y
在评估治疗效果的随机对照临床试验中，共同目标是使用病例队列、病例对照或两阶段抽样设计，评估测量的生物标志物反应终点与主动治疗组主要研究终点的关联。这种生物标志物关联分析的功效和样本量计算方法通常不考虑治疗功效的水平，排除了根据治疗功效的生物标志物效应修改来解释生物标志物关联结果，其不利之处在于功效计算可能默认且无意地假设治疗伤害了一些研究参与者。我们开发了解决这个问题的功效和样本量方法，并且这些方法还考虑了与生物学不相关的生物标志物的个体间变异性（例如，由于技术测量误差）。我们专注于二元研究终点和受测量误差影响的生物标志物，该误差呈正态分布或具有两个或三个水平的分类。我们通过预防性 HIV 疫苗功效试验来说明这些方法，并包括实施这些方法的 R 包。
In a randomized controlled clinical trial that assesses treatment efficacy, a common objective is to assess the association of a measured biomarker response endpoint with the primary study endpoint in the active treatment group, using a case-cohort, case-control, or two-phase sampling design. Methods for power and sample size calculations for such biomarker association analyses typically do not account for the level of treatment efficacy, precluding interpretation of the biomarker association results in terms of biomarker effect modification of treatment efficacy, with detriment that the power calculations may tacitly and inadvertently assume that the treatment harms some study participants. We develop power and sample size methods accounting for this issue, and the methods also account for inter-individual variability of the biomarker that is not biologically relevant (e.g., due to technical measurement error). We focus on a binary study endpoint and on a biomarker subject to measurement error that is normally distributed or categorical with two or three levels. We illustrate the methods with preventive HIV vaccine efficacy trials, and include an R package implementing the methods.