早期妊娠治疗决策：使用真实世界数据设计围产期药物流行病学研究
Early Pregnancy Treatment Decisions: Designing Perinatal Pharmacoepidemiology Studies using Real-World Data
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中文总结 AI 辅助
本文针对早期妊娠治疗决策问题，采用目标试验模拟方法，以2型糖尿病治疗为例，探讨了利用真实世界医疗数据设计围产期药物流行病学研究的相关方法，以应对妊娠特有的方法学挑战。
中文摘要 AI 辅助
关于妊娠期间药物使用的研究有两个主要目标：一是检测药物可能对孕妇或胎儿有害的信号，二是为需要药物治疗的孕妇提供更好的治疗方案。目标试验模拟已被提出作为在真实世界数据中估计干预效果的方法，最近已扩展至妊娠场景。该方法聚焦于将 eligibility（ eligibility 译为 eligibility ，此处保留专业术语，指研究对象的合格性）与治疗启动时间和随访开始时间对齐，这一特性尤为重要，因为妊娠存在特定的方法学挑战，包括右删失、左删失、截尾、竞争风险事件、妊娠时长差异以及不同的病因易感期。以往关于妊娠目标试验模拟的研究聚焦于点治疗（如疫苗或抗生素）的启动与非启动，而本文聚焦于关于孕前治疗方案改变的研究问题，旨在强调设计与临床实践中早期妊娠治疗决策相关时间点对齐的研究的机会与方法。以2型糖尿病的治疗为例，我们回顾了在常规收集的医疗数据中识别妊娠 episode（ episode 译为 episode ，此处保留专业术语，指妊娠阶段）的方法，介绍了可能的时间零点候选，并讨论了可最小化选择偏倚和 immortal person time（ immortal person time 译为 immortal person time ，此处保留专业术语，指 immortal 人时）潜在偏倚的分析方法。
英文摘要
Research on the use of medications during pregnancy has two primary goals: to detect signals that medications may be harmful to a pregnant individual or fetus, and to support better treatment of pregnant people who require pharmacotherapy. Target trial emulation has been proposed as an approach to estimate the effects of interventions in real world data, with recent extensions to the pregnancy setting. This approach focuses on aligning eligibility and treatment initiation with start of follow up, which is particularly desirable given methodological challenges specific to pregnancy, such as right and left censoring and truncation, competing events, differences in gestational length, and varying etiologically susceptible periods. While previous work on target trial emulation in pregnancy has focused on initiation versus non-initiation of point treatments such as vaccines or antibiotics, this paper focuses on research questions regarding changes to pregestational treatment regimes, and aims to highlight opportunities and approaches to designing studies that align with relevant time points during early pregnancy at which treatment decisions occur in clinical practice. Using the example of treatment for type 2 diabetes mellitus, we review methods for identifying pregnancy episodes in routinely collected healthcare data, introduce possible time zero candidates, and discuss analytic approaches that minimize potential bias due to selection and immortal person time.
