Online Program Home
My Program

Abstract Details

Activity Number: 354 - Topics in Machine Learning
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #330174 Presentation
Title: Using Q-Learning Method in Identify Optimal Treatment Regime
Author(s): Haocheng Li* and Vincent Shen and Hao Xu and Sylvia Hu
Companies: Hoffmann-La Roche Limited (Roche Canada) and Hoffmann-La Roche Limited (Roche Canada) and Hoffmann-La Roche Limited (Roche Canada) and Roche-Genentech
Keywords: Q-learning; Optimal treatment regime

For advanced cancer patients, there are multiple lines of treatment. In each line of treatment, there are often multiple treatment options such as chemotherapy, immunotherapy and targeted therapy. For example, if the first treatment line fails, clinicians can choose alternative therapies as the second line. Patients may also get third, fourth or later lines if previous treatments do not work. Therefore, the selection of proper therapies in optimal sequences becomes important in both drug development and clinical practice. In machine learning and data-mining fields, Q-learning is a model-free learning technique, which can be used to find an optimal action-selection policy for decision process. Q-learning method also has certain statistical properties. We use Q-learning approach to find the optimal treatment regime or treatment strategy. Results from simulation studies will be presented. Performance of optimal treatment regime estimation will be presented. We will also compare the statistical properties of Q-learning method with the properties of other causal inference methods such as weighting method.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program