Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies
Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies
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DOI：
10.1111/biom.13285
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发表时间：
2020-05-12
期刊：
影响因子：
1.9
通讯作者：
Platt, Robert W.
中科院分区：
文献类型：
--
作者：
Coulombe, Janie;Moodie, Erica E. M.;Platt, Robert W.
我们在使用电子健康记录的观察性研究的背景下，当利益关系被信息丰富的访问过程混淆、调解并进一步扭曲时，我们解决了对连续纵向结果的时变二元治疗的边际影响的估计。我们只允许零星记录纵向结果，并假定其监测时间由患者的特征决定。我们提出了两种新的基于线性模型的平均结果估计器，分别通过处理权重的广义逆概率和比例强度模型来调整混杂和信息监测过程。我们允许作为时间函数的拦截函数的灵活建模。我们的估计量具有闭式解，并且可以得到它们的渐近分布。广泛的模拟研究表明，这两种估计器都优于标准方法，如普通的最小二乘估计器或只考虑信息性监测或混杂因素的估计器。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。
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 modeling 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.