In recent years, precision treatment strategy have gained significant attention in medical research, particularly for patient care. We propose a novel framework for estimating conditional average treatment effects (CATE) in time-to-event data with competing risks, using ICU patients with sepsis as an illustrative example. Our approach, based on cumulative incidence functions and targeted maximum likelihood estimation (TMLE), achieves both asymptotic efficiency and double robustness. The primary contribution of this work lies in our derivation of the efficient influence function for the targeted causal parameter, CATE. We established the theoretical proofs for these properties, and subsequently confirmed them through simulations. Our TMLE framework is flexible, accommodating various regression and machine learning models, making it applicable in diverse scenarios. In order to identify variables contributing to treatment effect heterogeneity and to facilitate accurate estimation of CATE, we developed two distinct variable importance measures (VIMs). This work provides a powerful tool for optimizing personalized treatment strategies, furthering the pursuit of precision medicine.

翻译：近年来，精准治疗策略在医学研究中受到广泛关注，尤其在患者护理领域。本文提出一种新颖的框架，用于估计竞争风险下生存数据的条件平均处理效应（CATE），并以脓毒症ICU患者作为示例。该方法基于累积发生率函数与目标最大似然估计（TMLE），同时实现了渐近有效性与双重稳健性。本工作的主要贡献在于推导了目标因果参数CATE的有效影响函数。我们为这些性质建立了理论证明，并通过仿真实验予以验证。所提出的TMLE框架具有灵活性，可兼容多种回归与机器学习模型，适用于多样化场景。为识别导致处理效应异质性的变量并促进CATE的准确估计，我们开发了两种不同的变量重要性度量指标。本研究为优化个性化治疗策略、推进精准医学实践提供了有力工具。