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

来自戒烟移动健康研究的密集纵向数据的联合纵向和生存模型

• 批准号: 10677935
• 负责人: Madeline Abbott
• 金额: $ 4.07万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10677935
• 关键词: Algorithms; Behavioral; Biometry; Characteristics; Chronic Disease; Clinical Trials; Collection; Complex; Computer Models; Data; Data Analyses; Data Analytics; Development; Dimensions; Drug usage; Ecological momentary assessment; Educational workshop; Emotions; Ensure; Event; Funding; Future; Gender; Goals; Health; Health Technology; Income; Individual; Intervention; Joints; Knowledge; Link; Measures; Mentorship; Methodology; Methods; Michigan; Modeling; Motivation; National Institute of Drug Abuse; Outcome; Pattern; Persons; Process; Randomized; Recurrence; Research; Research Personnel; Risk; Risk Factors; Science; Scientific Advances and Accomplishments; Smoker; Smoking; Source; Statistical Methods; Statistical Models; Stochastic Processes; Substance Use Disorder; Time; Training; Universities; Variant; Work; Writing; adaptive intervention; analytical method; analytical tool; brief intervention; career; design; effective intervention; evidence base; experience; flexibility; improved; insight; interest; mHealth; negative affect; novel; oral communication; psychologic; randomized trial; skills; smoking cessation; social; sustained recovery; symposium; theories; time use; tool; treatment effect; user-friendly

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.

项目总结/摘要 通过基于移动的健康（mHealth）， 方法，如生态瞬时评估（EMA），提供了丰富的信息来源， 了解戒烟关键的心理状态的时间变化。然而， 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2中，该提案寻求开发一种新的统计模型（联合 纵向复发事件模型）和估计方法，将允许分析EMA数据， 戒烟研究使用低维可解释状态来描述行为现象 与戒烟有关的过程。通过将及时适应性干预措施（JITAIs）纳入 目标3中的模型，该项目将促进评估随时间变化的适应性干预措施的影响 使用来自移动的协助调节的数据， 吸烟（MARS）微随机试验（U 01 CA 229437; PI：Nahum-Shani，Wetter）。 培训目标是与辅导小组一起制定的，包括： 统计理论和计算方面的培训，㈡提高书面和口头沟通技能，㈢建立 合作关系，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， 生物统计学系，与UM社会研究所关系密切， 卓越的研究和培训。 总之，本项目提出的统计方法将在两个关键方面对科学做出贡献。 方式：（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，动机）， 在测量脆弱性（例如失效风险）时便于解释;（ii）其可解释性将 随后，通过增加对基于证据的适应性干预措施（如JITAIs）的了解， 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 实现并保持有意义和持续的康复”。尽管呈现 在戒烟研究的背景下，这种方法框架具有高度灵活性， 适用于物质使用障碍的移动健康研究和其他健康领域。这部分析小说 方法将以用户友好的R包形式免费提供，从而促进对药物使用的潜在影响 research.