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


OBJECTIVE
We review confounding and mediation in a longitudinal setting and introduce causal graphs to explain the bias that arises from conventional analyses.


CONCLUSIONS
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.
Moodie
Erica E M
EE
Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, QC, H3A 1A2, Canada. erica.moodie@mcgill.ca
Stephens
D A
DA
D015982:Bias; D015984:Causality; D003196:Computer Graphics; D015986:Confounding Factors, Epidemiologic; D003430:Cross-Sectional Studies; D008137:Longitudinal Studies; D015233:Models, Statistical; D011634:Public Health