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释维度。在衰老研究中，当干预效果因死亡事件而截断时，评估干预对临床意义结局的影响面临分析挑战。例如，在评估老年人远程照护的大型整群随机试验“全系统示范试验”中，干预对生活质量的总体效应为零。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，尤其是在随访结束时仍存活的群体中。为探究这种异质性，我们采用基于主分层的因果推断框架，以“幸存者平均因果效应”为目标——即“始终幸存者”（无论接受何种治疗分配均会存活的个体）中的治疗效果。我们利用贝叶斯加性回归树这一非参数机器学习方法扩展该框架，灵活建模潜在主分层及分层特异性潜在结果。这使得能够估计条件性幸存者平均因果效应，从而揭示基于基线特征定义的亚组间治疗效果的变异。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显著的生活质量改善（或未获益），这凸显了实施更个性化干预策略的机遇。本研究论证了将BART等机器学习方法嵌入原则性因果推断框架，如何能够为包含死亡截断和整群设计等复杂特征的试验数据提供更深入的洞见——这些正是分析实用老年学试验的关键考量。