QLS Seminar - Zeyu Bian

Penalized doubly-robust estimation of adaptive treatment strategies

Zeyu Bian, University of Miami
Tuesday January 17, 12-1pm
Zoom Link: https://mcgill.zoom.us/j/86855481591

Abstract: Adaptive treatment strategies (ATSs) are often estimated from data sources with many covariates measured, only a subset of which are useful for tailoring treatment or control of confounding. In such cases, including all the covariates in the analytic model could possibly yield an inappropriate or needlessly complicated treatment decision. Hence, it is crucial to apply variable selection techniques to ATSs. Variable selection with the objective of optimizing treatment decisions has been the subject of only very little literature. In this talk, I will present a regression-based estimation method that can naturally incorporate variable selection through a penalization approach that incorporates sparsity while ensuring strong heredity, and show how we can additionally incorporate confounder selection into the approach. We illustrate the methods using data from a pilot sequential multiple assignment randomized trial of a web-based, stress management intervention using a stepped-care method for cardiovascular diseases patients to determine useful tailoring variables while adjusting for chance imbalances in important covariates due to the smaller sample size in the pilot (joint work with Zeyu Bian, Sahir Bhatnagar, and Susan Shortreed)