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

Abstract

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.

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

Figure 1

DAGs representing two scenarios for confounding. The left panels show the framework for confounding, and the right panels provide illustrations of confounding of the preeclampsia (PE) and cerebral palsy (CP) association with maternal age (Age) as a potential confounder, and sub-fertility as an unmeasured confounder We denote sub-fertility as an unmeasured confounder in the broadest sense when, in fact, sub-fertility may serve as a marker for an underlying condition that results in both conception delay and preeclampsia, should a conception occur

Figure 2

DAGs representing three scenarios for variables acting as intermediates. The left panels show the framework for an intermediate variable, and the right panels show illustrations of how an intermediate variable, gestational age (GA), may affect the preeclampsia (PE) and cerebral palsy (CP) association, with placental abruption as an unmeasured intermediate (U). V is another unmeasured confounder, for example, birth defects

Figure 3

DAGs representing collider stratification bias with three illustrations for each scenario. The left panels show the theoretical framework for an intermediate variable, the middle panels show illustrations of how adjusting for an intermediate variable, I, depicted in a box and labeled a “collider” with gestational age (GA) as the intermediate, will induce bias of the preeclampsia (PE) and cerebral palsy (CP) association. The right panels show the risks of cerebral palsy on the y-axis by gestational age on the x-axis, among women with (solid line) and without preeclampsia (dashed line). Placental abruption is assumed to be an unmeasured variable.

Figure 3

DAGs representing collider stratification bias with three illustrations for each scenario. The left panels show the theoretical framework for an intermediate variable, the middle panels show illustrations of how adjusting for an intermediate variable, I, depicted in a box and labeled a “collider” with gestational age (GA) as the intermediate, will induce bias of the preeclampsia (PE) and cerebral palsy (CP) association. The right panels show the risks of cerebral palsy on the y-axis by gestational age on the x-axis, among women with (solid line) and without preeclampsia (dashed line). Placental abruption is assumed to be an unmeasured variable.