Ontology highlight
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.
SUBMITTER: Snowden JM
PROVIDER: S-EPMC5862739 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
BJOG : an international journal of obstetrics and gynaecology 20171030 6
<h4>Objective</h4>Using a simple simulation, we illustrate why associations estimated from studies restricted to preterm births cannot be interpreted causally.<h4>Design, setting and population</h4>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.<h4>Methods</h4> ...[more]