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Abstract:
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We propose optimizing dynamic treatment regimes using sequential conditional structural mean models in the treatment of Acute Myelogenous Leukemia (AML) involving multiple stages of chemotherapy. The inverse-probability-of-treatment weighted (IPTW) or g-computation estimator is used at each stage to estimate what we call the 'preliminary' optimal treatment regime, given patient information up to the current stage and prior treatment assignment. Essentially, this tailors the optimal treatment assignment at the current stage, and provides an optimal strategy for the remaining stages given the information currently available. We compare this method to Q-learning. Additionally, we use a two step prescriptive variable selection procedure that supports the tailored optimization of dynamic treatment regimes using conditional structural mean models by eliminating from consideration any suboptimal treatment regimes and sifting out the covariates that prescribe the optimal treatment regimes. Though applied to a specific two-stage sequential multiple assignment randomized trial (SMART) design, the methods described herein are easily generalized to other SMART designs and applications.
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