Learning Individualized Treatment Rules with Sequential and Multi-Outcome Data

Under the direction of Yufeng Liu and Donglin Zeng

Learning optimal individualized treatment rules (ITRs) has become increasingly important in the modern era of precision medicine. In this dissertation, we propose several approaches to solve some important problems regarding the data generating process and the learning algorithm for estimating ITRs. In the first project, we improve the outcome of interest in a clinical trial using a sequentially rule-adaptive design. Each entering patient will be allocated with a high probability to the current best treatment for this patient, which is estimated using the past data based on machine learning algorithm. We discuss the tradeoff between the training and test performance of the learnt ITR in the framework of contextual bandits. In the second project, we focus on the multi-stage stationary treatment policy (MSTP), which prescribes treatment assignment probabilities using the same decision function over stages. We propose to estimate the MSTP based on a penalized doubly robust estimator of the value function, and construct confidence intervals of the low-dimensional policy parameters that we are interested in using a one-step estimator. In the third project, we estimate the ITR that maximizes the primary outcome and causes little harm to auxiliary outcomes in the meanwhile. We propose a fusion penalty to encourage ITRs based on the primary outcome and auxiliary outcomes to yield similar recommendations, and optimize a surrogate loss function for estimation.

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