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Professor and Director of Ph.D. Graduate Program
Department of Biostatistics
Graduate School of Public Health
University of Pittsburgh
Pittsburgh, Pennsylvania

Patients with cancer or other recurrent diseases may undergo a long process of initial treatment, treatment resistance or disease recurrences followed by salvage treatments. Optimizing leukemia treatment should account for this complex process to maximally prolong patients' survival. Comparing disease-free survival for each treatment stage over-penalizes disease recurrences but under penalizes treatment-related mortalities. Moreover, treatment regimes used in practice are dynamic; that is, the choice of next treatment depends on a patient's responses to previous therapies. In this talk, using accelerated failure time models, we will develop a modified Q-learning method to optimize such dynamic treatment regimes. This method utilizes all the longitudinal data collected during the multi-stage process of disease recurrences and treatments, and identifies the optimal dynamic treatment regime for each individual patient by maximizing his or her expected overall survival. We illustrate the application of this method using data from a study of acute myeloid leukemia.