Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data

An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed to maximize the expected survival time under a binary treatment framework, the proposed method solves the multicategory treatment problem given multiple stages for censored data. Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. In theory, we establish Fisher consistency and provide the risk bound for the proposed estimator under regularity conditions. Our numerical studies show that the proposed method outperforms competing methods in terms of maximizing the conditional survival probability. We apply the proposed method to two real datasets: Framingham heart study data and acquired immunodeficiency syndrome clinical data. for this article are available online.

Automated summary

An optimal dynamic treatment regime that maximizes the conditional survival function for patients with censored data is proposed using an angle-based approach under a multicategory treatment framework. The method integrates estimations of decision rules at multiple stages into a single classification algorithm, outperforming existing methods in terms of maximizing the conditional survival probability. The proposed estimator is Fisher consistent with a risk bound established under regularity conditions.