Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies
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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.
KeywordsConfounding Mediation Directed Acyclic Graphs Longitudinal data
Both authors acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC). Moodie also acknowledges funding from the Canadian Institutes of Health Research (CIHR).
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