Improving Medication Safety in Pregnancy and Infancy: Target Trial Emulation with Real-World Data.

Abstract

Objective: The exclusion of pregnant women and infants from many randomized controlled trials (RCTs) has left critical gaps in medication safety, complicating clinical decision-making during these sensitive life stages. This commentary explores target trial emulation using real-world data as a robust alternative for advancing medication safety research when RCTs are not feasible.

Methods: Target trial emulation replicates the design principles of RCTs within observational data, accounting for the dynamic nature of medication exposure across gestational stages and adjusting for time-varying confounders. While challenges such as unmeasured confounding, selection bias, and violations of positivity assumptions remain, this method provides crucial insights to address current evidence gaps.

Outputs and implication: Information on medication exposure effects will be obtained, which will inform safer medicine guidelines in pregnancy and infancy. Future research integrating AI-driven tools, open science practices, and robust data governance frameworks will further strengthen the reliability and impact of target trial emulation. Multinational collaboration, the sharing of data across diverse sources will accelerate the generation of evidence, ultimately advancing medication safety.

Conclusion: Target trial emulation, leveraging real-world data, offers a promising alternative when traditional clinical trials are not feasible, promoting safer medication use and improving health outcomes for mothers and infants.