Sharp Inference on Selected Subgroups in Observational Studies

• Activity Number: 438 - Modern Statistical Learning Methods for High-Dimensional Biomedical Data: Treatment Heterogeneity and Data Integration
• Type: Topic-Contributed
• Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Xinzhou Guo* and Waverly Wei and Chong Wu and Jingshen Wang
• Companies: Harvard University and University of California, Berkeley and Florida State University and UC Berkeley
• Keywords: subgroup analysis; resampling method; bias correction; sharp inference

Abstract

In modern drug development, the broader availability of high-dimensional observational data provides opportunities for scientist to explore subgroup heterogeneity, especially when randomized clinical trials are unavailable due to cost and ethical constraints. However, a common practice that naively searches the subgroup with a high treatment level is often misleading due to the "subgroup selection bias". More importantly, the nature of high-dimensional observational data has further exacerbated the challenge of accurately estimating the subgroup treatment effects. To resolve these issues, we provide new inferential tools based on resampling to assess the replicability of post-hoc identified subgroups from observational studies. Through careful theoretical justification and extensive simulations, we show that our proposed approach delivers asymptotically sharp confidence intervals and debiased estimates for the selected subgroup treatment effects in the presence of high-dimensional covariates. We further demonstrate the merit of the proposed methods by analyzing the UK Biobank data.