We address estimation of the marginal effect of a time-varying binary treatment on a continuous longitudinal outcome in the context of observational studies using electronic health records, when the relationship of interest is confounded, mediated and further distorted by an informative visit process. We allow the longitudinal outcome to be recorded only sporadically and assume that its monitoring timing is informed by patients' characteristics. We propose two novel estimators based on linear models for the mean outcome that incorporate an adjustment for confounding and informative monitoring process through generalized inverse probability of treatment weights and a proportional intensity model respectively. We allow for a flexible modelling of the intercept function as a function of time. Our estimators have closed-form solutions, and their asymptotic distributions can be derived. Extensive simulation studies show that both estimators outperform standard methods such as the ordinary least squares estimator or estimators that only account for informative monitoring or confounders. We illustrate our methods using data from the Add Health study, assessing the effect of depressive mood on weight in adolescents. This article is protected by copyright. All rights reserved.

中文翻译：

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加权回归分析以纠正纵向研究中的信息监测时间和混杂因素

在使用电子健康记录的观察性研究背景下，当兴趣关系被信息访问过程混淆、调节和进一步扭曲时，我们解决了时变二元治疗对连续纵向结果的边际效应的估计。我们只允许偶尔记录纵向结果，并假设其监测时间由患者的特征决定。我们提出了两个基于平均结果线性模型的新估计器，它们分别通过治疗权重的广义逆概率和比例强度模型对混杂和信息监测过程进行了调整。我们允许将截距函数灵活建模为时间的函数。我们的估算器具有封闭形式的解决方案，并且可以导出它们的渐近分布。广泛的模拟研究表明，这两种估计量都优于标准方法，例如普通最小二乘估计量或仅考虑信息监测或混杂因素的估计量。我们使用 Add Health 研究的数据来说明我们的方法，评估抑郁情绪对青少年体重的影响。本文受版权保护。版权所有。