Department of Health and Human Services, Office of Biostatistics, Center for Drug Evaluation and Research, FDA, Silver Spring, Maryland, USA.
Pharm Stat. 2024 Nov-Dec;23(6):1065-1083. doi: 10.1002/pst.2424. Epub 2024 Jul 15.
In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population. BHM assumes exchangeability in treatment effect across subgroups after adjusting for effect modifiers and other relevant covariates. In this article, we will discuss the technical details for applying one-way and multi-way BHM using summary-level statistics, and patient-level data for subgroup analysis. Four case studies based on four new drug applications are used to illustrate the application of these models in subgroup analyses for continuous, dichotomous, time-to-event, and count endpoints.
在传统的亚组分析中，亚组治疗效果是使用来自每个亚组的单独数据来估计的，而不考虑同一研究中的其他亚组的数据。以这种方式估计的亚组治疗效果可能由于某些亚组的样本量较小而存在高度异质性和较大的变异性，与总体人群中的治疗效果有很大不同。贝叶斯层次模型（BHM）可用于得出更精确、异质性更小的亚组治疗效果估计值，这些估计值更接近总体人群中的治疗效果。BHM 假设在调整了效应修饰剂和其他相关协变量后，亚组间的治疗效果具有可交换性。在本文中，我们将讨论使用汇总水平统计数据和患者水平数据进行单向和多向 BHM 分析的技术细节，并用于亚组分析。基于四个新药申请的四个案例研究用于说明这些模型在连续、二分类、生存时间和计数终点的亚组分析中的应用。
