SMART with Adaptive Randomization for Quality Improvement in Depression Treatment Program

• Activity Number: 592
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Ken Cheung* and Bibhas Chakraborty and Karina Davidson
• Companies: Columbia University and Duke University and Columbia University
• Keywords: Behavioral intervention ; Dynamic treatment regime ; Q-learning ; Play-the-winner ; SMART ; Adaptive randomization

Abstract

Implementation study is an important tool for deploying state-of-the-art treatments from clinical efficacy studies into a treatment program, with the dual goals of learning about effectiveness of the treatments and improving the quality of care for patients enrolled into the program. In this talk, I will introduce a SMART-based methodology to optimize a treatment program of dynamic treatment regimens (DTRs) for patients with depression post acute coronary syndrome. The proposed method involves a novel application of adaptive randomization aimed to address three main concerns of an implementation study: it allows incorporation of historical data or opinions, it includes randomization for learning purposes, and it aims to improve care via adaptation throughout the program. By simulation, we illustrate that the inputs from historical data are important for the program performance measured by the expected outcomes of the enrollees, but also show that the adaptive randomization scheme is able to compensate poorly specified historical inputs by improving patient outcomes within a reasonable horizon. The simulation results also confirm that the proposed design allows efficient learnin