Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling of these effect differences is important for etiological goals and for purposes of optimizing treatment. Structural nested mean models (SNMMs) are useful causal models for estimating the potentially heterogeneous effect of a time-varying exposure on the mean of an outcome in the presence of time-varying confounding. A data-driven approach for selecting the effect modifiers of an exposure may be necessary if these effect modifiers are a priori unknown and need to be identified. Although variable selection techniques are available in the context of estimating conditional average treatment effects using marginal structural models, or in the context of estimating optimal dynamic treatment regimens, all of these methods consider an outcome measured at a single point in time. In the context of an SNMM for repeated outcomes, we propose a doubly robust penalized G-estimator for the causal effect of a time-varying exposure with a simultaneous selection of effect modifiers and use this estimator to analyze the effect modification in a study of hemodiafiltration. We prove the oracle property of our estimator, and conduct a simulation study for evaluation of its performance in finite samples and for verification of its double-robustness property. Our work is motivated by and applied to the study of hemodiafiltration for treating patients with end-stage renal disease at the Centre Hospitalier de l'Universit\'e de Montr\'eal. We apply the proposed method to investigate the effect heterogeneity of dialysis facility on the repeated session-specific hemodiafiltration outcomes.

翻译：当处理效应随其他协变量（即效应修饰因子）水平变化时，即产生效应修饰。对这些效应差异进行建模对于病因学研究和治疗优化具有重要意义。结构化嵌套均值模型（SNMMs）是一种有用的因果模型，可在存在时依混杂的情况下估计时变暴露对结局均值的潜在异质性效应。若效应修饰因子先验未知且需识别，则需采用数据驱动方法选择暴露的效应修饰因子。尽管在利用边际结构模型估计条件平均处理效应或估计最优动态治疗方案时已有变量选择技术，但这些方法均考虑单时间点测量的结局。针对重复结局的SNMM框架，本文提出一种双重稳健的惩罚G估计量，用于同时进行效应修饰因子选择并估计时变暴露的因果效应，并将其应用于血液透析滤过研究的效应修饰分析。我们证明了该估计量的神谕性质，并通过模拟研究评估其在有限样本下的表现及双重稳健性。本研究受蒙特利尔大学医院中心（Centre Hospitalier de l'Université de Montréal）的终末期肾病血液透析滤过治疗研究启发，并将该方法应用于探讨透析机构对重复疗程特异性血液透析滤过结局的效应异质性。