In survival studies, treatments can benefit patients through different mechanisms: a treatment may increase the probability of being cured or delay failure among patients who are not cured. Quantifying which mechanism is dominant, and whether it varies across subpopulations, is clinically important, yet there is limited work in the causal machine learning literature addressing this problem. Standard causal survival learners target finite-horizon survival or restricted mean survival time, while many cure models capture cure structures without estimating causal effects. In this work, we define meaningful causal effects in the presence of a cured subpopulation and introduce BartCure, a Bayesian causal machine learning approach for estimating them. The causal effects we recommend decompose the causal effect on restricted mean survival time into a stochastic cure and stochastic latency component, and we relate these new effects to both stochastic intervention effects and causal effects in principal strata. In simulations, BartCure is competitive for estimating average effects and is especially effective at conservatively detecting the direction of treatment-effect heterogeneity. We apply BartCure to estimate average and subgroup causal effects and to identify treatment effect heterogeneity in the CALGB 40101 breast cancer trial.

翻译：在生存分析研究中，不同治疗机制可使患者获益：治疗可能提高治愈概率，或延迟未治愈患者的失效时间。量化哪种机制占主导地位及其是否在亚群间存在差异，具有重要临床意义，但现有因果机器学习文献对此问题的研究十分有限。标准因果生存分析模型针对有限时间生存或受限平均生存时间进行建模，而多数治愈模型虽能捕捉治愈结构却未估计因果效应。本研究定义了存在治愈亚群时的有意义的因果效应，并引入BartCure——一种用于估计这些效应的贝叶斯因果机器学习方法。我们推荐的因果效应将受限平均生存时间的因果效应分解为随机治愈成分与随机潜伏期成分，并将这些新效应与随机干预效应及主分层因果效应建立关联。模拟实验表明，BartCure在估计平均效应方面具有竞争力，尤其在保守检测治疗效果异质性方向方面表现优异。我们将BartCure应用于CALGB 40101乳腺癌试验，估计了平均效应与亚组因果效应，并识别了治疗效果的异质性。