title: A cautionary note about estimating effects of secondary exposures in cohort studies.
abstract: Cohort studies are often enriched for a primary exposure of interest to improve cost-effectiveness, which presents analytical challenges not commonly discussed in epidemiology. In this paper, we use causal diagrams to represent exposure-enriched cohort studies, illustrate a scenario wherein the risk ratio for the effect of a secondary exposure on an outcome is biased, and propose an analytical method for correcting for such bias. In our motivating example, maternal smoking (Z) is a cause of fetal growth restriction (X), which subsequently affects preterm birth (Y) (i.e., Z → X → Y); strong positive associations exist between both Z, X and X, Y; and enrichment for X increases its prevalence from 10% to 50%. In the X-enriched cohort, unadjusted and X-adjusted analyses lead to bias in the risk ratio for the total effect of Z on Y. After application of inverse probability weights, the bias is corrected, with a small loss of efficiency in comparison with a same-sized study without X-enrichment. With increasing interest in conducting secondary analyses to reduce research costs, caution should be employed when analyzing studies that have already been enriched, intentionally or unintentionally, for a primary exposure of interest. Causal diagrams can help identify scenarios in which secondary analyses may be biased. Inverse probability weights can be used to remove the bias.
authors.forename: K A
authors.initials: KA
authors.lastname: Ahrens
authors.forename: S R
authors.initials: SR
authors.lastname: Cole
authors.forename: D
authors.initials: D
authors.lastname: Westreich
authors.forename: R W
authors.initials: RW
authors.lastname: Platt
authors.forename: E F
authors.initials: EF
authors.lastname: Schisterman
mesh_terms: D015331:Cohort Studies; D005260:Female; D005317:Fetal Growth Retardation / Q000209:etiology; D006801:Humans; D011247:Pregnancy; D047928:Premature Birth / Q000209:etiology; D012907:Smoking / Q000009:adverse effects; D013223:Statistics as Topic
keywords: bias (epidemiology); cohort studies; directed acyclic graph; epidemiologic methods; oversampling