Early Pregnancy Treatment Decisions: Designing Perinatal Pharmacoepidemiology Studies using Real-World Data

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.