Na Li (Queen's University)

Statistics Seminar

Wednesday, March 23rd, 2022

Time: 10:00 a.m.  Place: Online via Zoom (contact Brian Ling for Zoom link)

Speaker: Na Li (Queen's University)

Title: Bootstrap adjustment for predictive classification

Abstract: In clinical practice, it is important to identify a subgroup of patients who may benefit more in terms of a clinical outcome from a given treatment. The subgroup is usually induced by a continuous predictive biomarker and an associated unknown cutpoint, and this predictive classification problem is formulated as testing the significance of the interaction between the treatment and the subgroup indicator. Two commonly adopted procedures, minimum p-value and profile tests, are not reliable due to the inflated Type I error and/or identifiability issues. We propose bootstrap-based adjustments for various types of outcomes and establish their asymptotic validity. The proposed methods are applied to clinical trial data.