J M Snowden1,2, O Basso3,4. 1. School of Public Health, Oregon Health and Science University/Portland State University, Portland, OR, USA. 2. Department of Obstetrics & Gynecology, Oregon Health and Science University, Portland, OR, USA. 3. Department of Obstetrics & Gynecology, Research Institute of the McGill University Health Centre, Montreal, QC, Canada. 4. Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, QC, Canada.
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.
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.
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