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Causal inference in studies of preterm babies: a simulation study
J M Snowden1,2, O Basso3,4
1School of Public Health, Oregon Health and Science University/Portland State University, Portland, OR, USA.
Insights
Studies on preterm births are misleading due to selection bias. Associations between pathologies like pre-eclampsia and neonatal death are underestimated, appearing protective when they are not.
Area of Science:
- Perinatal epidemiology
- Biostatistics
- Clinical research methodology
Background:
- Studies focusing on preterm births often face challenges in causal interpretation.
- Pre-existing pathologies in fetuses can influence gestational length and neonatal outcomes.
- Understanding these biases is crucial for accurate risk assessment in neonatal care.
Purpose of the Study:
- To illustrate how associations in preterm birth studies can be causally misinterpreted.
- To demonstrate the impact of selection bias on estimating risks of neonatal death.
- To highlight the need for careful study design in perinatal research.
Main Methods:
- A simple data simulation of a hypothetical fetal cohort with varying pathological factors (A-D) was employed.
- The study focused on births at or before 32 weeks of gestation.
- Associations between specific pathologies (pre-eclampsia, chorioamnionitis) and neonatal death were analyzed using odds ratios.
Main Results:
- Simulated odds ratios for neonatal death were substantially biased, underestimating true risks.
- Factor A (pre-eclampsia) showed a protective effect (OR 0.39) in preterm births, despite a true causal OR of 1.50.
- Factor D (chorioamnionitis) also exhibited biased associations, complicating risk interpretation.
Conclusions:
- Selection bias is inherent in studies of very preterm births.
- Babies with one pathology are often compared to those with multiple, leading to biased associations.
- Findings underscore that associations in preterm birth studies may not reflect true causal risks.
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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