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.

Public Health Relevance

Stroke, the leading cause of major disability and the third leading cause of death worldwide, can be prevented through lifestyle changes. It is therefore important to develop and disseminate effective behavioral intervention programs for stroke prevention. This research aims to extend our statistical capacity to develop optimal, personalized behavioral intervention curriculums through carefully designed randomized community-based studies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS072127-02
Application #
8269641
Study Section
Special Emphasis Panel (ZRG1-AARR-F (52))
Program Officer
Gnadt, James W
Project Start
2011-06-01
Project End
2014-05-31
Budget Start
2012-06-01
Budget End
2013-05-31
Support Year
2
Fiscal Year
2012
Total Cost
$281,750
Indirect Cost
$106,750
Name
Columbia University (N.Y.)
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Chakraborty, Bibhas; Murphy, Susan A (2014) Dynamic Treatment Regimes. Annu Rev Stat Appl 1:447-464
Cheung, Ken; Duan, Naihua (2014) Design of implementation studies for quality improvement programs: an effectiveness-cost-effectiveness framework. Am J Public Health 104:e23-30
Chakraborty, Bibhas; Laber, Eric B; Zhao, Ying-Qi (2014) Inference about the expected performance of a data-driven dynamic treatment regime. Clin Trials 11:408-417
Chakraborty, Bibhas; Laber, Eric B; Zhao, Yingqi (2013) Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme. Biometrics 69:714-23
Boden-Albala, Bernadette; Quarles, Leigh W (2013) Education strategies for stroke prevention. Stroke 44:S48-51
Mohr, David C; Cheung, Ken; Schueller, Stephen M et al. (2013) Continuous evaluation of evolving behavioral intervention technologies. Am J Prev Med 45:517-23