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



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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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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Correspondence to Erica E. M. Moodie.

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Moodie, E.E.M., Stephens, D.A. Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies. Int J Public Health 55, 701–703 (2010).

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  • Confounding
  • Mediation
  • Directed Acyclic Graphs
  • Longitudinal data