🤖 AI Summary
This study addresses methodological challenges in perinatal pharmacologic research—such as left and right censoring, competing events, and gestational age heterogeneity—that often introduce bias in causal effect estimation, particularly when evaluating decisions to modify preconception treatment regimens during early pregnancy. Leveraging real-world data, this work extends the target trial emulation framework to the context of pre-pregnancy medication changes and innovatively proposes a gestational age–anchored definition of time zero tailored to early-pregnancy therapeutic decisions. The approach systematically corrects for selection bias and immortal time bias. By establishing a generalizable methodological paradigm, this research enhances the scientific rigor and feasibility of pharmacoepidemiologic studies assessing the safety and effectiveness of medications—such as those for type 2 diabetes—during the periconceptional and prenatal periods.
📝 Abstract
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.