Data Science Seminar: Xuming He (Michigan) on "How Good is Your Best Selected Subgroup"
| Day | Tuesday, February 21 |
|---|---|
| Time | 12:00 PM to 1:00 PM |
| Where | WH-100E |
Xuming He, the H.C. Carver Professor of Statistics at the University of Michigan, will speak on "How Good is Your Best Selected Subgroup.: Subgroup analysis is often performed by “slicing and dicing” the data to find one or more subgroups that show distinctive characteristics. However, evaluation of the best selected subgroup tends to be overly optimistic. In this presentation, we use the subgroup evaluation in clinical trials as an example to discuss the risk of selection bias in subgroup evaluations. In particular, we propose a novel bootstrap-based inference procedure for the best selected subgroup effect. The proposed inference procedure is model-free, easy to compute, and asymptotically sharp. We show, through both theory and empirical investigations, that how a subgroup is selected post hoc should play an important role in any statistical analysis. Much of the talk is based on joint work with Xinzhou Guo.
Add to Calendar 02/21/2023 12:00 PM 02/21/2023 1:00 PM America/New_York Data Science Seminar: Xuming He (Michigan) on "How Good is Your Best Selected Subgroup" Xuming He, the H.C. Carver Professor of Statistics at the University of Michigan, will speak on "How Good is Your Best Selected Subgroup.: Subgroup analysis is often performed by “slicing and dicing” the data to find one or more subgroups that show distinctive characteristics. However, evaluation of the best selected subgroup tends to be overly optimistic. In this presentation, we use the subgroup evaluation in clinical trials as an example to discuss the risk of selection bias in subgroup evaluations. In particular, we propose a novel bootstrap-based inference procedure for the best selected subgroup effect. The proposed inference procedure is model-free, easy to compute, and asymptotically sharp. We show, through both theory and empirical investigations, that how a subgroup is selected post hoc should play an important role in any statistical analysis. Much of the talk is based on joint work with Xinzhou Guo. WH-100E
