Developing Optimal Dynamic Behavioral Intervention in Community-Based Studies.

描述（由申请人提供）：中风预防可以通过改变生活方式来实现，如体育活动和药物依从性。因此，发展和传播行为干预项目作为预防中风的公共卫生措施的重要性再怎么强调都不为过。出于同样的原因，一个行为干预项目自然包含了解决各种问题的多个组成部分；一个成功的多成分方案很可能是基于中间健康结果以最佳顺序管理每个介入成分的直接结果，从而最大限度地提高最终的健康结果，如在12个月内血压降低。这种类型的治疗方案根据个体自身特点量身定制干预顺序，有时被称为动态治疗方案（DTR）。本研究旨在通过精心设计的随机社区研究，开发、验证和传播统计方法，以确定最佳DTR。我们计划分四个步骤实现这一研究目标。首先，我们将开发一种数据分析技术，称为Q-learning，这将使我们能够使用基于社区的研究数据以无偏的方式确定最佳DTR。Q-learning是一种起源于计算机科学文献的前沿技术；这项研究将把这一创新理念应用于临床应用，在临床应用中，观察到的数据具有高度的可变性（噪声）。其次，我们将开发统计设计，促进通过q学习发现最佳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合成：顺序多任务随机试验（SMART）和自适应随机化（AR）。第三，我们将通过计算机模拟和分析实际行为干预研究的数据来验证所提出的理论和方法。第四，我们将通过构建公众可访问的软件来传播这些方法，并将这些方法应用于下一阶段干预研究的规划中；这一步骤旨在缩短新方法与其临床应用之间的滞后时间。我们的长期公共卫生目标是提高制定最佳行为干预课程的能力。

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.