Joint longitudinal and survival models for intensive longitudinal data from mobile health studies of smoking cessation

项目摘要/摘要 通过基于移动医疗(MHealth)的方式增加密集纵向数据(ILD)的收集 生态瞬时评估(EMA)等方法为以下方面提供了丰富的信息来源 了解心理状态的时间变化是戒烟的关键。然而，在这方面的进展 需要统计方法来充分利用这些丰富的数据来评估干预措施并为设计提供信息 未来的干预措施。在目标1和目标2中，这项提议寻求发展一种新的统计模型(联合 纵向重现事件模型)和估计方法，其将允许分析来自 用低维可解释状态描述行为现象的戒烟研究 以及与戒烟相关的过程。通过将即时适应性干预(JITAI)纳入 目标3中的模型，该项目将促进评估时变适应性干预措施的影响。 使用移动辅助戒烟中心的数据研究受试者未来戒烟失误的风险 吸烟(MARS)微型随机试验(U01CA229437；PI：Nahum-Shani，湿润)。 与指导小组一起制定的培训目标包括：(I)推进技术 统计理论和计算方面的培训，(2)提高书面和口头沟通能力，(3)建立 协作关系，以及(Iv)参加会议、研讨会和专业发展 活动。拟议的研究和培训将在密歇根大学(UM)进行 生物统计部，与密歇根大学社会研究所有密切联系，并享有 在研究和培训方面表现出色。 总而言之，这个项目中提出的统计方法将在两个关键方面为科学做出贡献 方法：(I)它将允许以一种方式整合许多不同的项目(例如，情绪、冲动、动力) 在衡量脆弱性(例如，失效风险)时促进解释，以及(2)其可解释性将 随后，帮助为循证适应性干预措施(例如综合技术援助)的设计提供信息，方法是 了解代表脆弱性的条件。这些科学贡献直接推动了 国家药物滥用研究所的战略目标是开发“新的和改进的治疗方法，以帮助人们 在药物使用障碍的情况下，实现并保持有意义的和持续的复苏“。尽管呈现了 在戒烟研究的背景下，这种方法框架具有高度的灵活性和广泛性 适用于物质使用障碍的移动健康研究和其他健康领域。这部小说分析了 该方法将在用户友好的R包中免费提供，从而促进对药物使用的潜在影响 研究。

Project Summary/Abstract Increasing collection of intensive longitudinal data (ILD) through mobile health (mHealth)-based approaches, such as ecological momentary assessment (EMA), present a rich source of information for understanding temporal variations in psychological states key to smoking cessation. However, advances in statistical methods are needed to fully leverage these rich data to assess interventions and inform the design of future interventions. In Aims 1 and 2, this proposal seeks to develop a novel statistical model (joint longitudinal recurrent-event model) and estimation method that will allow for analysis of EMA data from a smoking cessation study using low-dimensional interpretable states to describe the behavioral phenomenon and processes related to smoking cessation. By incorporating just-in-time adaptive interventions (JITAIs) into the model in Aim 3, this project will facilitate assessment of the impact of time-varying adaptive interventions on a subject’s risk of a future lapse in smoking cessation using data from the Mobile Assistance for Regulating Smoking (MARS) micro-randomized trial (U01CA229437; PIs: Nahum-Shani, Wetter). Training goals, which were developed with the mentorship team, include: (i) advancing technical training in statistical theory and computing, (ii) improving written and oral communication skills, (iii) building collaborative relationships, and (iv) attending conferences, workshops, and professional development activities. The proposed research and training will be conducted at the University of Michigan (UM) in the Department of Biostatistics, which has close ties to the UM Institute for Social Research and a reputation for excellence in research and training. Altogether, the statistical methodology proposed in this project will contribute to the science in two key ways: (i) it will allow for the integration of many different items (e.g. emotions, urge, motivation) in a way that facilitates interpretation when measuring vulnerability (e.g. risk of lapse) and (ii) its interpretability will subsequently help inform the design of evidence-based adaptive interventions (e.g. JITAIs) through increased understanding of the conditions that represent vulnerability. These scientific contributions directly promote the National Institute on Drug Abuse’s strategic goal of developing “new and improved treatments to help people with substance use disorders achieve and maintain a meaningful and sustained recovery”. Although presented in the context of a smoking cessation study, this methodological framework is highly flexible with broad applicability to mHealth studies of substance use disorders and in other health domains. This novel analytic method will be freely available in a user-friendly R package, thus facilitating the potential impact on drug-use research.