Design and estimation in clinical trials with subpopulation selection.
Design and estimation in clinical trials with subpopulation selection.
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DOI：
10.1002/sim.7925
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发表时间：
2018-12-20
影响因子：
2
通讯作者：
Jaki T
中科院分区：
文献类型：
--
作者：
Chiu YD;Koenig F;Posch M;Jaki T
由于临床背景、环境和遗传因素等多种因素，在临床试验中经常观察到患者治疗反应的群体异质性。由这些基线因素定义的不同亚群可能导致治疗干预的获益或安全性特征存在差异。忽视亚群之间的异质性可能会对医疗实践产生重大影响。解决异质性的一种方法是需要设计和分析具有亚群选择的临床试验。已经针对不同的情况提出了几种类型的设计。在这项工作中，我们讨论了一类设计，允许选择一个预定义的子群。使用基于最大检验统计量的选择作为最坏情况，然后我们通过模拟在研究结束时研究最大似然估计量的精度和准确度。我们发现，所需的样本量主要是由亚组患病率，并在模拟中显示，这些设计的最大似然估计可以大大偏置。
Population heterogeneity is frequently observed among patients' treatment responses in clinical trials because of various factors such as clinical background, environmental, and genetic factors. Different subpopulations defined by those baseline factors can lead to differences in the benefit or safety profile of a therapeutic intervention. Ignoring heterogeneity between subpopulations can substantially impact on medical practice. One approach to address heterogeneity necessitates designs and analysis of clinical trials with subpopulation selection. Several types of designs have been proposed for different circumstances. In this work, we discuss a class of designs that allow selection of a predefined subgroup. Using the selection based on the maximum test statistics as the worst‐case scenario, we then investigate the precision and accuracy of the maximum likelihood estimator at the end of the study via simulations. We find that the required sample size is chiefly determined by the subgroup prevalence and show in simulations that the maximum likelihood estimator for these designs can be substantially biased.
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