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.

抽象的 药物使用和艾滋病毒的治疗通常需要关于 治疗的类型或交付。自适应干预是一种治疗设计，该设计使用了持续的信息 患者指导是否以及如何随着时间的推移修改治疗。通过提供适当的 对需要它的人的治疗，当他们需要时，自适应干预措施有望改善长期 更多人的期限结果，从而增加了吸毒和艾滋病毒的影响力和影响 现实环境中的治疗。顺序多次分配随机试验（SMART）是一个强大的 用于构建有效AIS的实验设计。但是，为了使聪明 对于药物使用和艾滋病毒干预科学家来说，最大程度地有用，需要方法学工作来扩展 分析源于智能的数据的选项，并扩展当前的智能类型 被设计，以重要的方式有限。该建议是竞争的续约应用 R01（DA039901；结束07/31/20），其中我们的多学科行为干预和数据团队 科学家已经成功开发并测试了一套用于设计和纵向分析的方法 聪明。在这种续签中，我们踏上了与新治疗的使用有关的新方法论方向 技术和由此产生的密集纵向数据（ILD），以告知有效AIS的开发。一个 越来越多的药物滥用和艾滋病毒干预措施利用了现代技术，例如移动和 基于Web的应用程序和可穿戴设备可提供和增强处理。这些技术启用 经常收集个人的行为和经验。这样的数据具有很大的潜力 解决有关自适应干预措施的动态影响和构建的新研究问题 更精致，但繁重的剪裁变量。但是，现有的纵向方法 智能分析目前不适合ILD。此外，现有的智能设计目前尚未 就如何利用ILD作为裁缝变量，充分利用ILD的临床潜力。 我们的目标是通过开发和评估新方法来弥合这些关键差距 使用/艾滋病毒科学家在智能中分析多种类型的ILD；开发和评估新方法 分析现有的ILD，以设计更有效的智能；使用并说明这些方法 来自三项智能研究的数据以及两项在药物滥用和艾滋病毒中的观察性研究 收集协议；并开发免费的，用户友好的资源来实施这些方法。 在该项目中开发的将通过吸毒和艾滋病毒来改善临床和公共卫生结果 科学家开发更多潜在的适应性干预措施，以指导吸毒和艾滋病毒的个性化 治疗。