Developing Optimal Dynamic Behavioral Intervention in Community-Based Studies.

在基于社区的研究中制定最佳动态行为干预。

• 批准号: 8185679
• 负责人: BERNADETTE Marie BODEN-ALBALA
• 金额: $ 28.18万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8185679
• 关键词: Address; Advocate; Base Sequence; Behavior Therapy; Behavioral; Blood Pressure; Cause of Death; Characteristics; Clinical Trials Design; Communities; Computer Simulation; Computer software; Data; Data Analyses; Education; Educational Curriculum; Equation; Family; Family member; Goals; Health; Hybrids; Individual; Intervention; Intervention Studies; Learning; Life Style; Literature; Measures; Methodology; Methods; Modeling; Noise; Outcome; Participant; Patients; Physical activity; Prevention program; Psychological reinforcement; Public Health; Publications; Randomized; Research; Risk Factors; Risk Reduction; Self Management; Social support; Staging; Statistical Methods; Stroke; Stroke prevention; Techniques; Time; base; clinical application; computer science; design; disability; innovation; intervention effect; intervention program; medication compliance; model development; novel; patient population; prevent; programs; randomized trial; theories; treatment program

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. 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.

描述（由申请人提供）：可以通过在体育活动和药物依从性等各种问题上改变生活方式来预防中风。因此，很难夸大制定和传播行为干预计划作为预防中风的公共卫生措施的重要性。出于同样的原因，行为干预计划自然涉及解决各种问题的多个组件。成功的多组分程序可能是基于中间健康结果以最佳序列管理每个介入的成分的直接结果，以最大程度地提高最终的健康结果，例如12个月内血压降低。这种类型的治疗计划根据个人自身的特征量身定制干预序列，有时被称为动态治疗方案（DTR）。这项研究旨在通过精心设计的随机基于社区的研究来开发，验证和传播统计方法，以识别最佳DTR。我们计划通过四个步骤来实现这一研究目标。首先，我们将开发一种称为Q-学习的数据分析技术，该技术将使我们能够使用基于社区研究的数据以公正的方式识别最佳DTR。 Q学习是一种源自计算机科学文献的尖端技术。这项研究将使这一创新思想适应具有高度可变性（噪声）的临床应用。其次，我们将开发统计设计，以通过Q学习来促进最佳DTR，同时使试验参与者受益。这将涉及两个临床试验设计概念的新型合成：顺序多重分配随机试验（SMART）和自适应随机化（AR）。第三，我们将通过使用计算机模拟并分析实际行为干预研究中的数据来验证提出的理论和方法。第四，我们将通过公共访问构建软件来传播这些方法，并在下一阶段的干预研究计划中采用这些方法；此步骤旨在结束新方法及其临床应用之间的滞后时间。我们的长期公共卫生目标是增强开发最佳行为干预课程的能力。 公共卫生相关性：中风是主要残疾的主要原因，也是全球第三大死亡原因，可以通过改变生活方式来阻止。因此，重要的是开发和传播有效的预防行为干预计划。这项研究旨在通过精心设计的随机基于社区的研究来扩展我们的统计能力，以开发最佳的个性化行为干预课程。