STAT@UVM Seminar: Erica Moodie
Penalized doubly-robust estimation of adaptive treatment strategies

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 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 disease 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)