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Am J Obstet Gynecol. 2017 Aug;217(2):167-175. doi: 10.1016/j.ajog.2017.04.016. Epub 2017 Apr 17.

Confounding, causality, and confusion: the role of intermediate variables in interpreting observational studies in obstetrics.

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Department of Obstetrics and Gynecology, College of Physicians and Surgeons, Columbia University, New York, NY; Department of Epidemiology, Joseph L. Mailman School of Public Health, Columbia University, New York, NY. Electronic address:
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD.


Prospective and retrospective cohorts and case-control studies are some of the most important study designs in epidemiology because, under certain assumptions, they can mimic a randomized trial when done well. These assumptions include, but are not limited to, properly accounting for 2 important sources of bias: confounding and selection bias. While not adjusting the causal association for an intermediate variable will yield an unbiased estimate of the exposure-outcome's total causal effect, it is often that obstetricians will want to adjust for an intermediate variable to assess if the intermediate is the underlying driver of the association. Such a practice must be weighed in light of the underlying research question and whether such an adjustment is necessary should be carefully considered. Gestational age is, by far, the most commonly encountered variable in obstetrics that is often mislabeled as a confounder when, in fact, it may be an intermediate. If, indeed, gestational age is an intermediate but if mistakenly labeled as a confounding variable and consequently adjusted in an analysis, the conclusions can be unexpected. The implications of this overadjustment of an intermediate as though it were a confounder can render an otherwise persuasive study downright meaningless. This commentary provides an exposition of confounding bias, collider stratification, and selection biases, with applications in obstetrics and perinatal epidemiology.


causal pathway; collider stratification bias; confounder; descending proxy; inappropriate adjustment; intermediate variable; overadjustment; perinatal paradox; selection bias; unmeasured confounding

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