Causal inference in studies of preterm babies: A simulation study

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Abstract

Using a simple simulation, we illustrate why associations estimated from studies restricted to preterm births cannot be interpreted causally. 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. 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. Odds ratios of neonatal death in the simulated data. 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). 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). Selection bias affects studies of preterm births, complicating interpretation.

Original languageEnglish (US)
JournalBJOG: An International Journal of Obstetrics and Gynaecology
DOIs
StateAccepted/In press - 2017

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Keywords

  • Causal inference
  • Neonatal networks
  • Perinatal epidemiology
  • Preterm birth

ASJC Scopus subject areas

  • Obstetrics and Gynecology

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