January 9, 2025 Webinar
Precision Medicine: Subgroup Identification in Clinical Trials
Lei Liu, PhD
Abstract:
In randomized controlled trials, individual subjects often exhibit heterogeneous treatment effects; that is, while some subjects might benefit significantly, others may see little to no improvement, or even detrimental effects. These variations can lead to an overall treatment effect that appears negligible. To address this issue in what might be considered failed trials, we employ an interaction tree framework to identify subgroups that exhibit heterogeneous treatment effects. We utilize the Classification and Regression Tree (CART) methodology, which recursively partitions the data into subsets demonstrating the most significant interaction with the treatment. The variability in treatment effects is evaluated using different models tailored to various outcome types. We have applied our methods to three randomized controlled trials, showcasing their potential to guide the direction of future clinical studies.
Short Bio:
Dr. Lei Liu is a Professor in the Center Biostatistics and Data Science, Institute of Informatics, Data Science, and Biostatistics (I2DB) at Washington University in St. Louis. He is the Deputy Director of Faculty Affairs of I2DB. He has diverse research interests in biostatistical and data science methods, including survival analysis, longitudinal data analysis, personalized medicine, and machine learning and deep learning. His research is focused on the analysis of high dimensional omics (epigenetics and microbiome) data, medical cost data, and joint models of multi-outcome data. He collaborates with clinicians in various medical fields, e.g., cancer, cardiovascular, addiction, ophthalmology, nephrology, infectious disease, asthma, and diabetes. Dr. Liu is a Fellow of the American Statistical Association. He is an associated editor of Biometrics and Statistics in Medicine, and an editorial board member of the Journal of the National Cancer Institute and Frontiers in Psychiatry. He is a standing member of NIH Biostatistical Methods and Research Design Study Section (2016-22), the only study section focusing on biostatistical methodology development. He also reviews grants frequently for other NIH study sections and other funding agencies around the world.