Novel Methods for Intensive Longitudinal Data in SMART Studies of Drug Abuse and HIV

药物滥用和艾滋病毒 SMART 研究中密集纵向数据的新方法

• 批准号: 10197064
• 负责人: Daniel Almirall
• 金额: $ 57.85万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10197064
• 关键词: AIDS prevention; Abstinence; Address; Adolescent; Behavior; Behavior Therapy; Behavioral Sciences; Clinical; Collection; Data; Data Analyses; Data Collection; Data Scientist; Decision Making; Development; Dose; Dropout; Drug abuse; Drug usage; Experimental Designs; Future; Generations; HIV; Hand; Health Technology; Individual; Intervention; Journals; Measures; Methodology; Methods; Mission; Modernization; National Institute of Drug Abuse; Observational Study; Online Systems; Outcome; Patients; Peer Review; Preparation; Protocols documentation; Public Health; Publishing; Research; Resources; Rewards; Scientist; Sequential Multiple Assignment Randomized Trial; Short-Term Memory; Technology; Testing; Time; Training; Treatment Efficacy; Urinalysis; Work; adaptive intervention; base; cognitive function; computerized; contingency management; design; dosage; drug abuse prevention; drug use screening; effective intervention; experience; improved; innovation; insight; longitudinal analysis; marijuana use disorder; multidisciplinary; novel; overtreatment; personalized decision; personalized medicine; responders and non-responders; response; substance use; therapy design; time interval; trial design; user friendly software; user-friendly; wearable device

ABSTRACT The treatment of drug use and HIV often requires sequential, individualized decision-making concerning the type or delivery of treatment. An adaptive intervention is a treatment design that uses ongoing information from the patient to guide whether, and how to modify the treatment over time. By providing the appropriate treatment to those who need it, when they need it, adaptive interventions hold the promise of improving long- term outcomes for greater numbers of people, thereby increasing the reach and impact of drug use and HIV treatments in real-world settings. The sequential multiple assignment randomized trial (SMART) is a powerful experimental design that is used for constructing efficacious AIs. However, in order to make the SMART maximally useful to drug use and HIV intervention scientists, methodological work is needed to expand the options for analyzing data that arise from a SMART and to expand the kinds of SMARTs that are currently being designed, which are limited in important ways. This proposal is a competing renewal application of R01(DA039901; ending 07/31/20), in which our multidisciplinary team of behavior intervention and data scientists has successfully developed and tested a suite of methods for the design and longitudinal analysis of SMARTs. In this renewal we embark in new methodological directions related to use of novel treatment technologies and resulting intensive longitudinal data (ILD) to inform the development of effective AIs. A growing number of drug abuse and HIV interventions capitalize on modern technologies such as mobile and web-based applications, and wearable devices to deliver and augment treatments. These technologies enable the collection of frequent measures of an individual’s behavior and experiences. Such data have great potential for addressing new research questions about the dynamic effects of adaptive interventions and for constructing more sophisticated, yet less burdensome tailoring variables. However, existing longitudinal methods for the analysis of SMARTs do not currently accommodate ILD. Further, existing SMART designs do not currently take full advantage of the clinical potential of ILD in terms of how ILD can be employed as tailoring variables. Our objective is to bridge these critical gaps by developing and evaluating new methods to enable drug use/HIV scientists to analyze multiple types of ILD in a SMART; developing and evaluating new methods to analyze existing ILD in order to design more effective SMARTs; applying and illustrating these methods using data from three SMART studies and two observational studies in drug abuse and HIV that employ an ILD collection protocol; and developing free, user-friendly resources to implement these methods. The methods developed in this project will improve clinical and public health outcomes by enabling drug use and HIV scientists to develop more potent adaptive interventions to guide the individualization of drug use and HIV treatments.

摘要 药物使用和艾滋病毒的治疗往往需要顺序的、个性化的决策，涉及 治疗的类型或提供。适应性干预是一种治疗设计，它使用来自 指导患者是否，以及如何随着时间的推移修改治疗。通过提供适当的 对需要治疗的人，当他们需要治疗时，适应性干预有望改善长期- 对更多人的长期结果，从而增加吸毒和艾滋病毒的影响范围和影响 现实世界中的治疗方法。序贯多分配随机试验(SMART)是一种强大的 用于构建有效人工智能的实验设计。然而，为了让智能 最大限度地对药物使用和艾滋病毒干预科学家有用，需要方法论工作来扩大 用于分析来自SMART的数据的选项，以及扩展当前 正在设计中，这在重要方面是有限的。此提案是与之竞争的续订申请 R01(DA039901；截至07/31/20)，其中我们的行为干预和数据多学科团队 科学家们成功地开发和测试了一套用于设计和纵向分析的方法 聪明人。在这次更新中，我们开始了与使用新的治疗方法相关的新的方法方向 技术和由此产生的密集纵向数据(ILD)，为有效的人工智能的发展提供信息。一个 越来越多的药物滥用和艾滋病毒干预利用现代技术，如移动和 基于网络的应用程序，以及提供和增强治疗的可穿戴设备。这些技术使 对个人行为和经历的频繁衡量的集合。这样的数据具有巨大的潜力 解决关于适应性干预的动态影响的新的研究问题，并建立 更复杂，但不那么繁琐的定制变量。然而，现有的纵向方法用于 对SMART的分析目前不支持ILD。此外，现有的智能设计目前不支持 充分利用ILD的临床潜力，如何将ILD用作定制变量。 我们的目标是通过开发和评估使药物成为可能的新方法来弥合这些关键差距 使用/HIV科学家在SMART中分析多种类型的ILD；开发和评估新方法以 分析现有的ILD以设计更有效的SMART；使用以下方法应用和说明这些方法 来自使用ILD的三项关于药物滥用和艾滋病毒的SMART研究和两项观察性研究的数据 收集协议；并开发免费的、用户友好的资源来实现这些方法。这些方法 在这个项目中开发的将通过促进药物使用和艾滋病毒来改善临床和公共卫生结果 科学家将开发更有效的适应性干预措施，以指导药物使用和艾滋病毒的个性化 治疗。