Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicine. In this article, we describe a nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event. By utilizing Bayesian additive regression trees and a mean-constrained Dirichlet process mixture model, our approach offers a flexible model for the regression function while placing few restrictions on the baseline hazard. Our nonparametric method leads to natural estimates of individual treatment effect and has the flexibility to address many major goals of HTE assessment. Moreover, our method requires little user input in terms of model specification for treatment covariate interactions or for tuning parameter selection. Our procedure shows strong predictive performance while also exhibiting good frequentist properties in terms of parameter coverage and mitigation of spurious findings of HTE. We illustrate the merits of our proposed approach with a detailed analysis of two large clinical trials (N = 6769) for the prevention and treatment of congestive heart failure using an angiotensin-converting enzyme inhibitor. The analysis revealed considerable evidence for the presence of HTE in both trials as demonstrated by substantial estimated variation in treatment effect and by high proportions of patients exhibiting strong evidence of having treatment effects which differ from the overall treatment effect.

中文翻译：

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通过完全非参数贝叶斯加速失效时间模型使用删失数据实现个性化治疗效果。

个体通常对相同的治疗反应不同，并且表征治疗反应的这种可变性是个性化医疗实践的一个重要目标。在本文中，我们描述了一个非参数加速失效时间模型，该模型可用于在患者结果为事件发生时间时分析异质治疗效果 (HTE)。通过利用贝叶斯加性回归树和均值约束狄利克雷过程混合模型，我们的方法为回归函数提供了一个灵活的模型，同时对基线风险的限制很少。我们的非参数方法导致对个体治疗效果的自然估计，并且可以灵活地解决 HTE 评估的许多主要目标。而且，我们的方法在处理协变量交互或调整参数选择的模型规范方面几乎不需要用户输入。我们的程序显示出强大的预测性能，同时在参数覆盖和减轻 HTE 的虚假发现方面也表现出良好的频率特性。我们通过对使用血管紧张素转换酶抑制剂预防和治疗充血性心力衰竭的两项大型临床试验 (N = 6769) 的详细分析来说明我们提出的方法的优点。分析揭示了两项试验中存在 HTE 的大量证据，这可以通过治疗效果的大量估计变化和高比例的患者表现出具有与整体治疗效果不同的治疗效果的有力证据来证明。