Overadjustment is defined inconsistently. This term is meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias or decreases precision without affecting bias. We define overadjustment bias as control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome. We define unnecessary adjustment as control for a variable that does not affect bias of the causal relation between exposure and outcome but may affect its precision. We use causal diagrams and an empirical example (the effect of maternal smoking on neonatal mortality) to illustrate and clarify the definition of overadjustment bias, and to distinguish overadjustment bias from unnecessary adjustment. Using simulations, we quantify the amount of bias associated with overadjustment. Moreover, we show that this bias is based on a different causal structure from confounding or selection biases. Overadjustment bias is not a finite sample bias, while inefficiencies due to control for unnecessary variables are a function of sample size.
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From the aEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland; bDepartment of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, North Carolina; and cDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
Submitted 8 January 2008; accepted 28 October 2008.
Supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institutes of Health (to E.F.S.). The NIH, NIAID through R03-AI 071763 (to S.C.). Chercheur-boursier award, and by core support to the Montreal Children's Hospital Research Institute, from the Fonds de Recherche en Santé du Quebec (to R.P.).
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Editors' note: A commentary on this article appears on page 494.
Correspondence: Enrique F. Schisterman, Epidemiology Branch, Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6100 Executive Boulevard, Room 7B03, Rockville, MD 20852. E-mail: firstname.lastname@example.org.