Robins J
Occupational Health Program Harvard School of Public Health, Boston, MA 02115.
Stat Med. 1989 Jun;8(6):679-701. doi: 10.1002/sim.4780080608.
In epidemiologic studies of the effect of an exposure on disease, the crude association of exposure with disease may fail to reflect a causal association due to confounding by one or more covariates. Most previous discussions of confounding in the epidemiologic literature have considered only point exposure studies, that is, studies that measure exposure and covariate status only once, at start of follow-up. In this paper we offer definitions of confounding suitable for longitudinal studies that obtain data on exposure, covariate, and vital status at several points in time. An important difference between longitudinal studies and point exposure studies is that, in longitudinal studies, a time-dependent covariate can be simultaneously a confounder and an intermediate variable on the causal pathway from exposure to disease. In this paper I propose an estimator, the extended standardized risk difference, that provides control for confounding by a covariate that is simultaneously a confounder and an intermediate variable.
在关于暴露因素对疾病影响的流行病学研究中，由于一个或多个协变量的混杂作用，暴露因素与疾病之间的粗略关联可能无法反映因果关联。流行病学文献中先前关于混杂的大多数讨论仅考虑了时点暴露研究，即仅在随访开始时测量一次暴露和协变量状态的研究。在本文中，我们给出了适用于纵向研究的混杂定义，这类研究在多个时间点获取暴露、协变量和生命状态的数据。纵向研究与时点暴露研究的一个重要区别在于，在纵向研究中，一个随时间变化的协变量可以同时是混杂因素以及暴露到疾病因果路径上的中间变量。在本文中，我提出了一种估计方法，即扩展标准化风险差，它可以控制同时作为混杂因素和中间变量的协变量的混杂作用。
