Causal inference in studies of preterm babies: a simulation study. (30th October 2017)

• Record Type: Journal Article
• Authors: Snowden, JM; Basso, O
• Is Part Of: BJOG. Volume 125:Number 6(2018)
• Page Start: 686
• Page End: 692
• Publication Date: 2017-10-30
• Subjects: Causal inference -- neonatal networks -- perinatal epidemiology -- preterm birth; Obstetrics -- Periodicals; Gynecology -- Periodicals; 618
• DOI: 10.1111/1471-0528.14942
• Languages: English
• ISSNs: 1470-0328

Abstract

Abstract : Objective: Using a simple simulation, we illustrate why associations estimated from studies restricted to preterm births cannot be interpreted causally. Design, setting and population: Data simulation involving a hypothetical cohort of fetuses who may be healthy or have one or more of four pathological factors (termed A through D, increasing in severity) with known effects on gestational length and risk of mortality. We focus on babies born at ≤32 weeks of gestation. Methods: We visually represent the simulated population and compare the association between A (which may represent pre‐eclampsia) and neonatal death. We then repeat the exercise with D (standing in for chorioamnionitis) as the exposure of interest. Main outcome measures: Odds ratios of neonatal death in the simulated data. Results: In most weeks, and for both A and D, the calculated odds ratios are substantially biased and underestimate the true risk of neonatal death associated with each pathology. For example, factor A has a true causal odds ratio of 1.50, yet it appears protective among births ≤32 weeks (estimated crude odds ratio 0.39; gestational age‐adjusted odds ratio 0.71). Conclusions: Among very preterm births, virtually all babies are born with pathologies that increase the risk of adverse outcomes. Hence, babies exposed to one factor (e.g. pre‐eclampsia) are compared with babies who have a mix of other pathologies. Such selection bias affects studies carried out among very preterm births (e.g. where pre‐eclampsia appears to reduce risk of adverse neonatal outcomes). Tweetable abstract: Selection bias affects studies of preterm births, complicating interpretation. Abstract : Tweetable abstract Selection bias affects studies of preterm births, complicating interpretation.