Biostatistics Weekly Seminar

An important aspect of precision medicine focuses on characterizing differential responses to treatment due to unique patient characteristics, also known as heterogeneous treatment effects (HTE), and identifying beneficial subgroups with enhanced treatment effects. Estimating HTE with right-censored data in observational studies remains challenging. In this paper, we propose a meta-learner-based procedure with pseudo-outcomes for analyzing HTE in survival data, which includes a pseudo-outcome-based meta-learner framework for estimating HTE, a variable importance metric for identifying predictive variables to HTE, and a data-adaptive procedure to select subgroups with enhanced treatment effects. We evaluate the finite sample performance of the framework under various settings of observational studies and randomized clinical trials. Furthermore, we applied the proposed procedure to analyze subgroup treatment heterogeneity of a written asthma action plan (WAAP) on time-to-ED (Emergency Department) return due to asthma exacerbation, using a large EHR dataset with visit records expanded from pre- to post-COVID-19 pandemic. We identified vulnerable subgroups of patients with poorer asthma outcomes but enhanced benefits from WAAP and characterized patient profiles. Our research offers valuable insights for healthcare policymakers and providers in advocating influenza vaccination and strategic WAAP distribution to particularly vulnerable groups during a disruptive public health event, ultimately enhancing pediatric asthma control.

Virtual: Zoom Link to Follow
20 March 2024
1:30pm