Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer.

ABSTRACT: Typical regimens for advanced metastatic stage IIIB/IV nonsmall cell lung cancer (NSCLC) consist of multiple lines of treatment. We present an adaptive reinforcement learning approach to discover optimal individualized treatment regimens from a specially designed clinical trial (a "clinical reinforcement trial") of an experimental treatment for patients with advanced NSCLC who have not been treated previously with systemic therapy. In addition to the complexity of the problem of selecting optimal compounds for first- and second-line treatments based on prognostic factors, another primary goal is to determine the optimal time to initiate second-line therapy, either immediately or delayed after induction therapy, yielding the longest overall survival time. A reinforcement learning method cal

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC3138840 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature