Causal inference methods have been developed for longitudinal observational study designs where confounding is thought to occur over time. In particular, one may estimate and contrast the population mean counterfactual outcome under specific exposure patterns. In such contexts, confounders of the longitudinal treatment-outcome association are generally identified using domain-specific knowledge. However, this may leave an analyst with a large set of potential confounders that may hinder estimation. Previous approaches to data-adaptive model selection for this type of causal parameter were limited to the single time-point setting. We develop a longitudinal extension of a collaborative targeted minimum loss-based estimation (C-TMLE) algorithm that can be applied to perform variable selection in the models for the probability of treatment with the goal of improving the estimation of the population mean counterfactual outcome under a fixed exposure pattern. We investigate the properties of this method through a simulation study, comparing it to G-Computation and inverse probability of treatment weighting. We then apply the method in a real data example to evaluate the safety of trimester-specific exposure to inhaled corticosteroids during pregnancy in women with mild asthma. The data for this study were obtained from the linkage of electronic health databases in the province of Quebec, Canada. The C-TMLE covariate selection approach allowed for a reduction of the set of potential confounders, which included baseline and longitudinal variables. This article is protected by copyright. All rights reserved.

中文翻译：

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基于协同目标最小损失估计的因果推理中的数据自适应纵向模型选择

已经为纵向观察研究设计开发了因果推断方法，其中认为随着时间的推移会发生混淆。特别是，人们可以估计和对比特定暴露模式下的总体平均反事实结果。在这种情况下，纵向治疗-结果关联的混杂因素通常使用特定领域的知识进行识别。然而，这可能会给分析师留下大量可能阻碍估计的潜在混杂因素。以前为此类因果参数选择数据自适应模型的方法仅限于单个时间点设置。我们开发了一种协作目标最小损失估计 (C-TMLE) 算法的纵向扩展，该算法可用于在模型中执行变量选择以获取治疗概率，目的是改进对总体平均反事实结果的估计固定的曝光模式。我们通过模拟研究来研究这种方法的特性，将其与 G 计算和治疗加权的逆概率进行比较。然后，我们在真实数据示例中应用该方法，以评估轻度哮喘女性在怀孕期间特定三个月暴露于吸入性皮质类固醇的安全性。本研究的数据来自加拿大魁北克省电子健康数据库的链接。C-TMLE 协变量选择方法允许减少潜在混杂因素，其中包括基线和纵向变量。本文受版权保护。版权所有。