Causal inference in studies of preterm babies: a simulation study.
Causal inference in studies of preterm babies: a simulation study.
复制标题
早产儿研究中的因果推断：模拟研究。
DOI：
10.1111/1471-0528.14942
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Basso,O
中科院分区：
文献类型：
--
作者：
Snowden,JM;Basso,O
目的：通过一个简单的模拟，我们说明了为什么从仅限于早产的研究中估计的关联不能被解释为因果关系。设计、环境和人群数据模拟涉及一个假设的胎儿队列，这些胎儿可能是健康的，或具有四种已知影响妊娠期长短和死亡风险的病理因素（称为a至D，严重程度逐渐增加）中的一种或多种。我们关注的是妊娠≤32周出生的婴儿。方法我们直观地表示模拟人群，并比较A（可能代表先兆子痫）与新生儿死亡之间的关系。然后我们用D（代表绒毛膜羊膜炎）作为兴趣暴露重复这个练习。主要结局测量模拟数据中新生儿死亡的比率。结果在大多数周内，对于A组和D组，计算出的优势比存在显著偏差，低估了与每种病理相关的新生儿死亡的真实风险。例如，因子A的真正因果优势比为1.50，但它在出生≤32周的人群中似乎具有保护作用（估计粗优势比0.39；胎龄调整优势比0.71）。结论：在非常早产的婴儿中，几乎所有的婴儿出生时都有增加不良后果风险的病理。因此，暴露于一种因素（如先兆子痫）的婴儿与具有其他混合病理的婴儿进行比较。这种选择偏倚影响了在极早产儿中进行的研究（例如，先兆子痫似乎降低了新生儿不良结局的风险）。摘要选择偏差影响早产研究，使解释复杂化。
ObjectiveUsing a simple simulation, we illustrate why associations estimated from studies restricted to preterm births cannot be interpreted causally.Design, setting and populationData 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.MethodsWe 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 measuresOdds ratios of neonatal death in the simulated data.ResultsIn 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).ConclusionsAmong 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 abstractSelection bias affects studies of preterm births, complicating interpretation.