Epidemiology Research Today is a free monthly online journal that collates and summarizes the latest research about Epidemiology, including details on twin studies, statistics, environmental and genetic factors.

Potential Confounding by Exposure History and Prior Outcomes: An Example From Perinatal Epidemiology.

Howards PP, Schisterman EF, Heagerty PJ

From the *Division of Epidemiology, Statistics, and Prevention Research, National Institute of Child Health and Human Development, Bethesda, Maryland; †Department of Biostatistics, University of Washington, Seattle, Washington.

Prior pregnancy outcomes, such as spontaneous abortion and preterm birth, are often predictive of future pregnancy outcomes. Therefore, many researchers adjust for reproductive history. Although this adjustment may be appropriate for a predictive model, it is not necessarily appropriate when the goal is to obtain an unbiased estimate of the effect of exposure on disease. Reproductive history may seem to meet the conventional criteria for confounding because it is unlikely to be on the causal pathway between exposure and current outcome, is often associated with current outcome, and may be associated with exposure as well. However, whether reproductive history is a confounder or not depends on the underlying reason for its associations with exposure and current outcome. Thus, conventional methods for assessing confounding are often inadequate. Directed acyclic graphs (DAGs) can be used to evaluate complex scenarios for confounding when the research question is clearly defined with respect to the exposure, the outcome, and the effect estimate of interest. Special care is required when reproductive history affects future exposure. We use 5 DAGs to illustrate possible relations between reproductive history and current outcome. We assess each DAG for confounding, and identify the appropriate analytic technique. We provide a numeric example using data from the Collaborative Perinatal Project. There is no single answer as to whether reproductive history should be included in the model; the decision depends on the research question and the underlying DAG.

Published 10 August 2007 in Epidemiology.
Full-text of this article is available online (may require subscription).

© 2005-2008 Epidemiology Research Today. All Rights Reserved.