Dynamic treatment regimes (DTRs) have gained increasing interest in the field of personalized health care in the last two decades, as they provide a sequence of individualized decision rules for treating patients over time. In a DTR, treatment is adapted in response to the changes in an individual's disease progression and health care history.
However, specific challenges emerge when applying the current methods of DTR in practice. For example, a treatment decision often happens after a medical test, and is thus nested within the decision of whether a test is needed or not. Such nested test-and-treat strategies are attractive to improve cost-effectiveness. In the first project of this dissertation, we develop a Step-adjusted Tree-based Learning (SAT-Learning) method to estimate the optimal DTR within such a step-nested multiple-stage multiple-treatment dynamic decision framework using test-and-treat observational data. At each step within each stage, we combine a doubly robust semiparametric estimator via Augmented Inverse Probability Weighting with a tree-based reinforcement learning procedure to achieve the counterfactual optimization. SAT-Learning is robust and easy to interpret for the strategies of disease screening and subsequent treatments when necessary. We applied our method to a Johns Hopkins University prostate cancer active surveillance dataset to evaluate the necessity of prostate biopsy and identify the optimal test-and-treatment regimes for prostate cancer patients.
Our second project is motivated by scenarios in medical practice where one need to decide on patients radiation or drug doses over time. Due to the complexity of continuous dose scales, few existing studies have extended their methods of multi-treatment decision making to a method to estimate the optimal DTR with continuous doses. We develop a new method, Kernel-Involved-Dosage-Decision learning (KIDD-Learning), which combines a kernel estimation of the dose-response function with a tree-based dose-search algorithm, in a multiple-stage setting. At each stage, KIDD-Learning recursively estimates a personalized dose-response function using kernel regression and then identifies the interpretable optimal dosage regime by growing an interpretable decision tree. The application of KIDD-Learning is illustrated by evaluating the dynamic dosage regimes of the adaptive radiation therapy using a Michigan Medicine liver cancer dataset.
In KIDD-Learning, our algorithm splits each node of a tree-based decision rule from the root node to terminal nodes. This heuristic algorithm may fail to identify the optimal decision rule when there are critical tailoring variables hidden from an imperceptible parent node. Therefore, in the third project, we propose an important modification of KIDD-Learning, Stochastic Spline-Involved Tree Search (SSITS), to estimate a more robust optimal dosage regime. This new method uses a simulated annealing algorithm to stochastically search the space of tree-based decision rules. In each visited decision rule, a non-parametric smooth coefficient model is applied to estimate the dose-response function. We further implement backward induction to estimate the optimal regime from the final stage in a reverse sequential order to previous treatment stages. We apply SSITS to determine the optimal dosing strategy for patients treated with Warfarin using data from the International Warfarin Pharmacogenetics Consortium.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163090/1/mingtang_1.pd
