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

  • Cande V. Ananth
    Corresponding author: Cande V. Ananth, PhD, MPH.
    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
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  • Enrique F. Schisterman
    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
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Published:April 17, 2017DOI:
      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.

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        American Journal of Obstetrics & GynecologyVol. 218Issue 3
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          We thank Dr Lisonkova for her thoughtful letter,1 in which she raises 3 important issues regarding our article on confounding and intermediate variables.2 First, Dr Lisonkova extols the importance of a properly formulated research question and clinical insights to guide research. We agree and wholeheartedly support her contention. Second, as stated in our article, the directed acyclic graphs (DAG) that we presented are oversimplified for illustration purposes, and Dr Lisonkova takes an issue with this.
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