SMART Methodology for Constucting Adaptive Interventions

用于构建适应性干预措施的 SMART 方法

基本信息

批准号：
8379097
负责人：
SUSAN A MURPHY
金额：
$ 53.16万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/8379097
关键词：
AIDS prevention AIDS/HIV problem Address Adherence Alcohol or Other Drugs use Area Artificial Intelligence Attention deficit hyperactivity disorder Autistic Disorder Behavioral Case Study Characteristics Child Clinical Communities Computer software Confidence Intervals Data Data Analyses Data Quality Decision Making Development Dimensions Disease Dose Dropout Drug Addiction Drug abuse Empirical Research Engineering Ensure Evaluation Event Experimental Designs Family Funding Gender Goals HIV Health Individual Internet Intervention Learning Linear Models Literature Measures Mentors Methodology Methods Naltrexone National Institute of Drug Abuse National Institute on Alcohol Abuse and Alcoholism Non-linear Models Outcome Outcome Measure Output Patient Preferences Patients Pharmaceutical Preparations Postdoctoral Fellow Pregnant Women Prevention Prevention program Preventive Intervention Principal Investigator Procedures Psychological reinforcement Public Health Relapse Relative (related person)Research Research Design Research Methodology Research Personnel Resources Risk Risk Behaviors Sample Size Scientist Severities Social support Societies Statistical Methods Substance abuse problem Technology Transfer Time Training Treatment Effectiveness Treatment outcome Work base clinical practice combat computer science cost design drug abuse prevention evidence base flexibility graduate student high risk improved infancy innovation interest preference prevent primary outcome randomized trial response simulation software development stressor substance abuse treatment time use tool treatment effect user friendly software web site

项目摘要

The long-term goal of this project is to improve clinical practice and thus public health by facilitating the evidence-based construction of efficacious, individualized, i.e., adaptive, interventions and treatments in drug abuse. Clinicians naturally adapt the level and type of therapy according to patient outcomes such as severity, response to past therapy, risk, stressors, adherence, preference and burden. This project will develop methods for using data to inform and enhance this adaptive clinical practice. Adaptive interventions are composed of operationalized decision rules that input patient outcomes and output recommended alterations in intensity and/or type of therapy. The construction of adaptive interventions requires addressing questions such as, "How do we best use measures of risk and other outcomes in order to decide when a patient's therapy needs to be intensified or stepped down?". "What sequence of therapies is best for achieving maximal improvement or preventing drug dependence?" and "Should this sequence of therapies vary by patient outcomes?" This project facilitates the construction of adaptive interventions by developing the following methodological innovations. First, the SMART experimental design methodology will be extended for use with time varying outcomes; in particular this component will provide guidance to researchers on how to match their use of the time varying outcome in the data analysis of SMART studies to their prevention/clinical goals. Second, this component will generalize a data analytic method from engineering and computer science for use with SMART study data so as to develop adaptive behavioral or combined behavioral/pharmacological interventions. Third, this component will provide methods for using data to construct more flexible adaptive interventions. This innovation will be achieved by constructing measures of confidence that can be used to ascertain when there is no evidence to discriminate between two or more successful treatments as part of an adaptive intervention. This work will include collaborative research with health scientists interested in constructing adaptive interventions. The goal is to accelerate the improvement of both drug abuse prevention programs and treatments.

该项目的长期目标是改善临床实践，从而通过促进公共卫生基于证据的有效，个性化的，即适应性，干预和药物治疗虐待。临床医生自然会根据患者结局的水平和类型来调整治疗水平和类型严重性，对过去治疗，风险，压力源，依从性，偏好和负担的反应。这个项目将开发使用数据来告知和增强这种自适应临床实践的方法。自适应干预措施由输入患者结果和建议输出的操作决策规则组成强度和/或治疗类型的改变。自适应干预措施的建设需要解决诸如“我们如何最好地使用风险和其他结果的措施来决定何时患者的治疗需要加强或辞职？”。实现最大改善或预防药物依赖？”和“这种疗法序列应该应该因患者的结果而有所不同吗？以下方法论创新。首先，智能实验设计方法将是延长用于时间变化的结果；特别是该组件将为研究人员如何在智能研究的数据分析中匹配他们对时间的使用变化的结果他们的预防/临床目标。其次，该组件将从与智能研究数据一起使用的工程和计算机科学，以发展自适应行为或联合行为/药理干预措施。第三，此组件将提供使用的方法数据以构建更灵活的自适应干预措施。这项创新将通过构建来实现置信度措施可以用来确定何时没有证据来区分作为适应性干预的一部分，两种或多个成功的治疗方法。这项工作将包括协作与有兴趣建立适应性干预措施的健康科学家进行的研究。目标是加速改善预防药物滥用计划和治疗方法。