Abstract

Real-world evidence (RWE) refers to clinical and healthcare insights derived from the analysis of real-world data (RWD), such as electronic health records, insurance claims, registries, and patient-generated data. While RWE offers valuable opportunities to assess treatment effectiveness, safety, and health outcomes in routine clinical practice, it often faces challenges related to confounding and bias. Causal inference methods—such as propensity score techniques and target trial emulation—are essential to address these limitations and approximate the rigor of randomized controlled trials. The integration of RWE and robust causal inference frameworks is transforming healthcare research and policy by enabling more timely, generalizable, and ethically feasible evidence generation.

Bio.

Edward is a Professor at the School of Pharmacy, National Cheng Kung University (NCKU), Taiwan.

He is also the leader of the Institutional Research Division, R&D Office at NCKU.

He has expertise in the use of a variety of healthcare databases across Asia, Oceania, North America, and Europe in the conduct of international multi-database pharmacoepidemiologic research.

His research interests include the safety and effectiveness of medications, as well as the methods for real-world evidence (RWE) studies.

He has published 140+ research articles in top journals such as BMJ, Nature Medicine, JAMA Internal Medicine, and Annals of Internal Medicine.

He has served as an Associate Editor of Pharmacoepidemiology and Drug Safety since 2013.

He served as the chair of the Asian Pharmacoepidemiology Network (AsPEN) (2016-2018) and the Educational leader since 2024.

He is an active member of the Neurological and Mental Health Global Epidemiology Network (NeuroGEN).

He was awarded the Fellow of the International Society for Pharmacoepidemiology (FISPE) designation in 2023 and the Outstanding Research Award of the National Science and Technology Council of Taiwan.

All faculty and students are welcome to join.