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Abstract:
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Dynamic treatment regime (DTR) plays a critical role in precision medicine to assign patient-specific treatments at multiple stages and to optimize a long term clinical outcome. However, most of existing work about DTRs have been focused on categorical treatment scenarios, instead of continuous treatment. Also, the performance of regular black-box machine learning methods and regular tree learning methods are lack of interpretability and global optimality respectively. In this paper, we propose a non-greedy global optimization method for dose search, namely Global Optimal Dosage Tree-based learning method (GoDoTree-Learning), which combines a robust estimation of the counterfactual outcome with an interpretable and non-greedy decision tree for estimating the global optimal dynamic dosage treatment regime in a multiple-stage setting. GoDoTree-Learning recursively estimates the counterfactual outcome of continuous treatment using doubly robust estimator at each stage, and optimizes the stage-specific decision tree in a non-greedy way. We conduct simulation studies to evaluate the finite sample performance of the proposed method and apply it to a real data application.
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