From the CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Epidemiology. 2023 Mar 1;34(2):238-246. doi: 10.1097/EDE.0000000000001562. Epub 2022 Nov 11.
Observational studies are often the only option to estimate effects of interventions during pregnancy. Causal inference from observational data can be conceptualized as an attempt to emulate a hypothetical pragmatic randomized trial: the target trial.
To provide a step-by-step description of how to use healthcare databases to estimate the effects of interventions initiated during pregnancy. As an example, we describe how to specify and emulate a target trial of COVID-19 vaccination during pregnancy, but the framework can be generally applied to point and sustained strategies involving both pharmacologic and non-pharmacologic interventions.
First, we specify the protocol of a target trial to evaluate the safety and effectiveness of vaccination during pregnancy. Second, we describe how to use observational data to emulate each component of the protocol of the target trial. We propose different target trials for different gestational periods because the outcomes of interest vary by gestational age at exposure. We identify challenges that affect (i) the target trial and thus its observational emulation (censoring and competing events), and (ii) mostly the observational emulation (confounding, immortal time, and measurement biases).
Some biases may be unavoidable in observational emulations, but others are avoidable. For instance, immortal time bias can be avoided by aligning the start of follow-up with the gestational age at the time of the intervention, as we would do in the target trial. Explicitly emulating target trials at different gestational ages can help reduce bias and improve the interpretability of effect estimates for interventions during pregnancy.
观察性研究通常是评估妊娠期间干预措施效果的唯一选择。从观察性数据中进行因果推断可以被概念化为尝试模拟一个假设的实用随机试验：目标试验。
提供一个逐步描述如何使用医疗保健数据库来估计妊娠期间开始的干预措施的效果。作为一个例子，我们描述了如何指定和模拟妊娠期间 COVID-19 疫苗接种的目标试验，但该框架可以普遍应用于涉及药物和非药物干预的点和持续策略。
首先，我们指定一个目标试验的方案来评估妊娠期间疫苗接种的安全性和有效性。其次，我们描述如何使用观察性数据来模拟目标试验方案的每个组成部分。我们为不同的妊娠时期提出不同的目标试验，因为感兴趣的结局因暴露时的妊娠年龄而异。我们确定了影响（i）目标试验及其观察性模拟（删失和竞争事件）的挑战，以及（ii）主要是观察性模拟（混杂、不朽时间和测量偏差）的挑战。
在观察性模拟中，有些偏差可能是不可避免的，但有些是可以避免的。例如，可以通过将随访的开始与干预时的妊娠年龄对齐来避免不朽时间偏差，就像我们在目标试验中所做的那样。明确地在不同的妊娠时期模拟目标试验可以帮助减少偏差，并提高对妊娠期间干预措施效果估计的解释性。
