Statistical Considerations on Subgroup Analysis in Clinical Trials
Statistical Considerations on Subgroup Analysis in Clinical Trials
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DOI：
10.1080/19466315.2015.1077726
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发表时间：
2015-01-01
影响因子：
1.8
通讯作者：
Yue, Lilly
中科院分区：
文献类型：
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作者：
Alosh, Mohamed;Fritsch, Kathleen;Yue, Lilly
临床试验亚组分析的目的是研究基于背景特征定义的亚组间治疗效果的一致性或异质性。因此，亚组分析在临床试验结果的解释中起着至关重要的作用。各试验亚组治疗效果的一致性表明，无论特定背景特征如何，平均治疗效果一般适用。治疗效果的巨大异质性可能表明治疗益处仅适用于一小部分人群。然而，观察到的治疗效果在亚组之间的异质性可能是由于将人群划分为几个亚组的偶然结果。此外，众所周知，临床试验通常没有检测异质性的功率，因此统计检验可能由于功率低而无法检测到现有的异质性。在本文中，我们的目标是：(i)概述临床试验中亚组分析的主要问题，并为其结果的解释提供一般统计指导；（ii）提供旨在在目标亚组及其总体人群中建立疗效的临床试验的设计和分析的统计观点；（iii）强调与贝叶斯亚组分析相关的一些潜在假设和问题，非劣效性试验的亚组考虑因素；个体化用药，亚组误分类，最后进行亚组分析进行安全性评价。
The objective of subgroup analysis of a clinical trial is to investigate consistency or heterogeneity of the treatment effect across subgroups, defined based on background characteristics. As such, subgroup analysis plays an essential role in the interpretation of the clinical trial findings. Consistency of treatment effect across trial subgroups indicates that the average treatment effect is in general applicable regardless of the specific background characteristics. Substantial heterogeneity in treatment effect may be indicative that treatment benefit pertains only to a subset of the population. However, heterogeneity in the observed treatment effect across subgroups can arise due to chance as a result of partitioning the population into several subgroups. Furthermore, as it is known, clinical trials are generally not powered for detecting heterogeneity, thus statistical tests may miss detection of existing heterogeneity due to low power. In this article, we aim to: (i) outline the major issues underlying subgroup analysis in clinical trials and provide general statistical guidance for interpretation of its findings, (ii) provide statistical perspectives on the design and analysis of a clinical trial that aims for establishing efficacy in a targeted subgroup along with that of its overall population, and (iii) highlight some of the underlying assumptions and issues relevant to Bayesian subgroup analysis, subgroup considerations for noninferiority trials, personalized medicine, subgroup misclassification, and finally, subgroup analysis for safety assessment.