Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies
Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies
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DOI：
10.1007/s00038-010-0184-x
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发表时间：
2010-12-01
影响因子：
4.6
通讯作者：
Stephens, D. A.
中科院分区：
文献类型：
--
作者：
Moodie, Erica E. M.;Stephens, D. A.
卫生研究人员越来越多地获得纵向数据；这些挑战在横断面数据中没有遇到，其中最重要的是时变混杂变量和中间效应的存在。我们回顾了纵向设置的混淆和中介，并引入因果图来解释传统分析产生的偏差。当数据中同时存在时变混淆和中介时，传统的回归模型会导致对效应系数的估计系统不正确或有偏差。在另一篇论文中（穆迪和斯蒂芬斯在《国际公共卫生杂志》，2010年b期），我们描述了一类在纵向设置中产生无偏估计的模型。
Longitudinal data are increasingly available to health researchers; these present challenges not encountered in cross-sectional data, not the least of which is the presence of time-varying confounding variables and intermediate effects.We review confounding and mediation in a longitudinal setting and introduce causal graphs to explain the bias that arises from conventional analyses.When both time-varying confounding and mediation are present in the data, traditional regression models result in estimates of effect coefficients that are systematically incorrect, or biased. In a companion paper (Moodie and Stephens in Int J Publ Health, 2010b, this issue), we describe a class of models that yield unbiased estimates in a longitudinal setting.