Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies

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Abstract
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.

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