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Int J Public Health. 2010 Dec;55(6):701-3. doi: 10.1007/s00038-010-0184-x. Epub 2010 Sep 14.

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

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  • 1Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, QC, H3A 1A2, Canada. erica.moodie@mcgill.ca

Abstract

INTRODUCTION:

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.

OBJECTIVES:

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.

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