Bringing causal models into the mainstream.
Bringing causal models into the mainstream.
复制标题
将因果模型带入主流。
DOI：
10.1097/ede.0b013e3181a0997a
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Hogan,JosephW
中科院分区：
文献类型：
--
作者：
Hogan,JosephW
Principled, sensible, and durable inference from complex observational data in epidemiology requires the careful formulation, implementation, and interpretation of models that are parameterized explicitly in terms of causal effects. The paper by Bembom et al 1 in this issue of Epidemiology is an outstanding example; aside from being a thorough and rigorous application of causal modeling, it addresses directly the shortcomings of standard regression approaches to answer causal questions.The data analyzed by Bembom et al have characteristics that are frequently encountered in longitudinal epidemiologic studies: the primary exposure cannot be easily studied using a randomized trial; confounders are time-varying; and standard regression models cannot be used to estimate the causal parameter of interest. The paper is remarkable not only for its careful description of the effects of interest and the use of cutting-edge models, but for its in-depth examination of key assumptions and side-by-side comparison with more familiar models.
