Developing Optimal Dynamic Behavioral Intervention in Community-Based Studies.

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): Stroke prevention may be achieved through lifestyle changes on a variety of issues such as physical activities and medication adherence. It is therefore difficult to overstate the importance of developing and disseminating behavioral intervention programs as a public health measure to prevent strokes. For the same reason, a behavioral intervention program naturally involves multiple components addressing the various issues; and a successful multi-component program is likely a direct result of administering each interventional component in an optimal sequence, based on the intermediate health outcomes, so as to maximize the eventual health outcome such as blood pressure reduction over 12 months. This type of treatment program tailors the intervention sequence according to an individual's own characteristics, and is sometimes called dynamic treatment regime (DTR). This research aims to develop, validate, and disseminate statistical methods to identify optimal DTR through carefully designed randomized community-based studies. We plan to achieve this research goal in four steps. First, we will develop a data analytical technique, called Q-learning, that will enable us to identify an optimal DTR in an unbiased fashion using data from community- based studies. Q-learning is a cutting-edge technique originating from the computer science literature; this research will adapt this innovative idea to clinical applications where data are observed with high level of variability (noise). Second, we will develop statistical designs that facilitate the discovery of optimal DTR through Q-learning while benefiting the trial participants. This will involve novel synthesis of two clinical trial design concepts: sequential multiple assignment randomized trial (SMART) and adaptive randomization (AR). Third, we will validate the proposed theory and methods by using computer simulation and analyzing data from an actual behavioral intervention study. Fourth, we will disseminate the methods by building software with public access and employ the methods in the planning of the next stage of intervention study; this step is intended to close the lag time between novel methods and its clinical applications. Our long-term public health goal is to enhance the capability of developing optimal behavioral intervention curriculums.