title: Potential confounding by exposure history and prior outcomes: an example from perinatal epidemiology.
abstract: 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.
authors: Division of Epidemiology, Statistics, and Prevention Research, National Institute of Child Health and Human Development, Bethesda, Maryland 20892, USA. howardsp@mail.nih.gov
authors: Penelope P
authors: PP
authors: Howards
authors: Enrique F
authors: EF
authors: Schisterman
authors: Patrick J
authors: PJ
authors: Heagerty
mesh_terms: D003196:Computer Graphics; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D016015:Logistic Models; D018811:Maternal Exposure / Q000009:adverse effects*; D011247:Pregnancy; D011256:Pregnancy Outcome / Q000453:epidemiology*; D012907:Smoking / Q000009:adverse effects / Q000453:epidemiology