Shen Changyu, Hu Yang, Li Xiaochun, Wang Yadong, Chen Peng-Sheng, Buxton Alfred E
Department of Biostatistics, School of Medicine, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, 46202, USA.
School of Life Science and Technology, Harbin Institute of Technology, Harbin, HeiLongJiang, 150001, China.
Biom J. 2016 Nov;58(6):1357-1375. doi: 10.1002/bimj.201500180. Epub 2016 Jun 29.
Characterization of a subpopulation by the difference in marginal means of the outcome under the intervention and control may not be sufficient to provide informative guidance for individual decision and public policy making. Specifically, often we are interested in the treatment benefit rate (TBR), that is, the probability of benefitting an intervention in a meaningful way. For binary outcomes, TBR is the proportion that has "unfavorable" outcome under the control and "favorable" outcome under the intervention. Identification of subpopulations with distinct TBR by baseline characteristics will have significant implications in clinical setting where a medical intervention with potential negative health impact is under consideration for a given patient. In addition, these subpopulations with unique TBR set the basis for guidance in implementing the intervention toward a more personalized scheme of treatment. In this article, we propose a Bayesian tree based latent variable model to seek subpopulations with distinct TBR. Our method offers a nonparametric Bayesian framework that accounts for the uncertainty in estimating potential outcomes and allows more exhaustive search of the partitions of the baseline covariates space. The method is evaluated through a simulation study and applied to a randomized clinical trial of implantable cardioverter defibrillators to reduce mortality.
通过干预和对照下结果的边际均值差异来表征亚群，可能不足以提供有关个体决策和公共政策制定的有用指导。具体而言，我们通常对治疗受益率（TBR）感兴趣，即从有意义的角度受益于干预的概率。对于二元结果，TBR是在对照下有“不利”结果且在干预下有“有利”结果的比例。通过基线特征识别具有不同TBR的亚群，对于在考虑对特定患者进行可能对健康有负面影响的医学干预的临床环境中具有重要意义。此外，这些具有独特TBR的亚群为朝着更个性化的治疗方案实施干预提供了指导基础。在本文中，我们提出了一种基于贝叶斯树的潜在变量模型来寻找具有不同TBR的亚群。我们的方法提供了一个非参数贝叶斯框架，该框架考虑了估计潜在结果时的不确定性，并允许对基线协变量空间的划分进行更详尽的搜索。该方法通过模拟研究进行评估，并应用于一项旨在降低死亡率的植入式心脏复律除颤器随机临床试验。
