MSc Public Lecture - Junwei Shen (DSAS)

Title: Classification-based method for estimating dynamic treatment regimes

Abstract: Dynamic treatment regimes are sequential decision rules  dictating how to individualize treatments to patients based on evolving treatments and covariate history. In this thesis, we investigate two methods of estimating dynamic treatment regimes. The first method extends outcome weighted learning from two-treatments to multi-treatments and allows for negative treatment outcome. We show that under two different sets of assumptions, the Fisher consistency can be maintained. The second method estimates treatment rules by a neural classification tree. A weighted squared loss function is defined to approximate the indicator function to maintain the smoothness. A method of tree reconstruction and pruning is proposed to increase the interpretability.  Simulation studies and real application to data from Sequential Treatment Alternatives to Relieve Depression (STAR*D) clinical trial are conducted to illustrate the proposed methods.