Detecting heterogeneity in treatment response enriches the interpretation of gerontologic trials. In aging research, estimating the effect of the intervention on clinically meaningful outcomes faces analytical challenges when it is truncated by death. For example, in the Whole Systems Demonstrator trial, a large cluster-randomized study evaluating telecare among older adults, the overall effect of the intervention on quality of life was found to be null. However, this marginal intervention estimate obscures potential heterogeneity of individuals responding to the intervention, particularly among those who survive to the end of follow-up. To explore this heterogeneity, we adopt a causal framework grounded in principal stratification, targeting the Survivor Average Causal Effect (SACE)-the treatment effect among "always-survivors," or those who would survive regardless of treatment assignment. We extend this framework using Bayesian Additive Regression Trees (BART), a nonparametric machine learning method, to flexibly model both latent principal strata and stratum-specific potential outcomes. This enables the estimation of the Conditional SACE (CSACE), allowing us to uncover variation in treatment effects across subgroups defined by baseline characteristics. Our analysis reveals that despite the null average effect, some subgroups experience distinct quality of life benefits (or lack thereof) from telecare, highlighting opportunities for more personalized intervention strategies. This study demonstrates how embedding machine learning methods, such as BART, within a principled causal inference framework can offer deeper insights into trial data with complex features including truncation by death and clustering-key considerations in analyzing pragmatic gerontology trials.

翻译：检测治疗反应的异质性能够丰富老年学试验的解读。在衰老研究中，当干预效果因死亡事件而被截断时，评估干预对具有临床意义结局的影响面临着分析上的挑战。例如，在"全系统示范"试验中——一项评估老年人远程照护的大型整群随机研究——干预对生活质量的总体效应被证实为零。然而，这种边际干预估计掩盖了个体对干预反应的潜在异质性，尤其是在那些存活至随访结束的个体中。为探索这种异质性，我们采用了一个基于主分层的因果推断框架，以"幸存者平均因果效应"（SACE）——即"总是幸存者"（无论被分配何种治疗都会存活的个体）中的治疗效果——为目标。我们利用贝叶斯加性回归树（BART），一种非参数机器学习方法，扩展了这一框架，以灵活地对潜在的"主分层"和"分层特异性潜在结局"进行建模。这使得我们能够估计"条件性SACE"（CSACE），从而揭示基于基线特征定义的亚组之间治疗效果的差异。我们的分析表明，尽管平均效应为零，但某些亚组从远程照护中获得了（或未获得）显著的生活质量改善，这突显了制定更个性化干预策略的机遇。本研究展示了将BART等机器学习方法嵌入到原则性的因果推断框架中，如何能够为具有复杂特征（包括死亡截断和整群设计）的试验数据提供更深入的见解——这些是分析实用老年学试验时的关键考量。