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International Journal of Public Health

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01-12-2010 | Hints & Kinks

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

Authors: Erica E. M. Moodie, D. A. Stephens

Published in: International Journal of Public Health | Issue 6/2010

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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Title: Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies
Authors: Erica E. M. Moodie
D. A. Stephens
Publication date: 01-12-2010
Publisher: SP Birkhäuser Verlag Basel
Published in: International Journal of Public Health / Issue 6/2010
Print ISSN: 1661-8556
Electronic ISSN: 1661-8564
DOI: https://doi.org/10.1007/s00038-010-0184-x

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Introduction

Objectives

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Introduction

Objectives

Conclusions

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