title: Optimization of individualized dynamic treatment regimes for recurrent diseases.
abstract: Patients with cancer or other recurrent diseases may undergo a long process of initial treatment, disease recurrences, and salvage treatments. It is important to optimize the multi-stage treatment sequence in this process to maximally prolong patients' survival. Comparing disease-free survival for each treatment stage over penalizes disease recurrences but under penalizes treatment-related mortalities. Moreover, treatment regimes used in practice are dynamic; that is, the choice of next treatment depends on a patient's responses to previous therapies. In this article, using accelerated failure time models, we develop a method to optimize such dynamic treatment regimes. This method utilizes all the longitudinal data collected during the multi-stage process of disease recurrences and treatments, and identifies the optimal dynamic treatment regime for each individual patient by maximizing his or her expected overall survival. We illustrate the application of this method using data from a study of acute myeloid leukemia, for which the optimal treatment strategies for different patient subgroups are identified.
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, U.S.A.
authors: Xuelin
authors: X
authors: Huang
authors: Jing
authors: J
authors: Ning
authors: Abdus S
authors: AS
authors: Wahed
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D002908:Chronic Disease / Q000188:drug therapy*; D003198:Computer Simulation; D018572:Disease-Free Survival; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000188:drug therapy; D008137:Longitudinal Studies; D015233:Models, Statistical
chemical_list: D000970:Antineoplastic Agents
keywords: backward induction; causal inference; coarsening at random; optimal treatment sequence; recurrent events; survival analysis