Using Statistics, Data Science and AI to Enrich the Assessment of Treatment Effect Heterogeneity in Drug Development

This session discusses a wide range of topics on assessing treatment effect heterogeneity (TEH) in drug development. By leveraging advanced statistical methods, data science techniques, and AI-driven approaches, we aim to enhance the understanding of treatment effect heterogeneity through post-hoc analysis in the real world application to drug development. Speakers from variety of career levels (from graduate student to senior Statistical consultant) from both academia and industry will discuss the following topics: overall statistical assessment of treatment effect heterogeneity, to subgroup identification and selection using ML models, to investigating the Bayesian Shrinkage estimation of subgroup effect. In addition, we will cover topics on how to performance these analysis in drug development, what should one consider when applying these post-hoc analysis in practice. The variety methods with the goal of enriching drug development embodies the 2025 ENAR theme of "ENAR is interdisciplinary" and as well as showcasing a real world contribution to methodological development. Attendees will gain insights into how these innovative Statistical and AI approaches can help to improve the understanding of treatment effect heterogeneity, drug development processes and outcomes, ultimately enriching society through better healthcare solutions.