Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA.
Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Stat Med. 2021 May 20;40(11):2665-2691. doi: 10.1002/sim.8924. Epub 2021 Mar 9.
Precision medicine is an active area of research that could offer an analytic paradigm shift for clinical trials and the subsequent treatment decisions based on them. Clinical trials are typically analyzed with the intent of discovering beneficial treatments if the same treatment is applied to the entire population under study. But, such a treatment strategy could be suboptimal if subsets of the population exhibit varying treatment effects. Identifying subsets of the population experiencing differential treatment effect and forming individualized treatment rules is a task well-suited to modern machine learning methods such as tree-based ensemble predictive models. Specifically, Bayesian additive regression trees (BART) has shown promise in this regard because of its exceptional performance in out-of-sample prediction. Due to the unique inferential needs of precision medicine for clinical trials, we have proposed the BART extensions explicated here. We incorporate random effects for longitudinal repeated measures and subject clustering within medical centers. The addition of a novel interaction detection prior to identify treatment heterogeneity among clinical trial patients and its association with patient characteristics. These extensions are unified under a framework that we call mixedBART. Simulation studies and applications of precision medicine based on real randomized clinical trials data examples are presented.
精准医学是一个活跃的研究领域，它可能为临床试验及其后续治疗决策提供分析范式的转变。临床试验通常是为了发现有益的治疗方法而进行分析，如果对研究人群中的所有个体都应用相同的治疗方法。但是，如果人群中的某些亚组表现出不同的治疗效果，这种治疗策略可能不是最优的。确定经历不同治疗效果的人群亚组并制定个体化的治疗规则是一个非常适合现代机器学习方法的任务，例如基于树的集成预测模型。具体来说，由于贝叶斯加性回归树（BART）在样本外预测方面的出色表现，它在这方面显示出了很大的潜力。由于精准医学对临床试验的独特推理需求，我们提出了这里阐述的 BART 扩展。我们为纵向重复测量和医学中心内的个体聚类纳入了随机效应。在识别临床试验患者之间的治疗异质性及其与患者特征的关联之前，添加了一种新的交互检测先验。这些扩展在我们称之为混合 BART 的框架下得到了统一。我们展示了基于真实随机临床试验数据示例的模拟研究和精准医学应用。
