Abbott Madeline R, Dempsey Walter H, Nahum-Shani Inbal, Potter Lindsey N, Wetter David W, Lam Cho Y, Taylor Jeremy M G
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States.
Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, United States.
Biometrics. 2025 Apr 2;81(2). doi: 10.1093/biomtc/ujaf052.
The availability of mobile health (mHealth) technology has enabled increased collection of intensive longitudinal data (ILD). ILD have potential to capture rapid fluctuations in outcomes that may be associated with changes in the risk of an event. However, existing methods for jointly modeling longitudinal and event-time outcomes are not well-equipped to handle ILD due to the high computational cost. We propose a joint longitudinal and time-to-event model suitable for analyzing ILD. In this model, we summarize a multivariate longitudinal outcome as a smaller number of time-varying latent factors. These latent factors, which are modeled using an Ornstein-Uhlenbeck stochastic process, capture the risk of a time-to-event outcome in a parametric hazard model. We take a Bayesian approach to fit our joint model and conduct simulations to assess its performance. We use it to analyze data from an mHealth study of smoking cessation. We summarize the longitudinal self-reported intensity of 9 emotions as the psychological states of positive and negative affect. These time-varying latent states capture the risk of the first smoking lapse after attempted quit. Understanding factors associated with smoking lapse is of keen interest to smoking cessation researchers.
移动健康（mHealth）技术的可用性使得密集纵向数据（ILD）的收集得以增加。ILD 有潜力捕捉可能与事件风险变化相关的结果的快速波动。然而，由于计算成本高，现有的联合建模纵向和事件时间结果的方法并不适合处理 ILD。我们提出了一种适用于分析 ILD 的联合纵向和事件时间模型。在这个模型中，我们将多变量纵向结果总结为数量较少的随时间变化的潜在因素。这些潜在因素使用奥恩斯坦 - 乌伦贝克随机过程进行建模，在参数风险模型中捕捉事件时间结果的风险。我们采用贝叶斯方法来拟合我们的联合模型，并进行模拟以评估其性能。我们用它来分析一项戒烟的 mHealth 研究中的数据。我们将 9 种情绪的纵向自我报告强度总结为积极和消极情绪的心理状态。这些随时间变化的潜在状态捕捉了尝试戒烟后首次复吸的风险。了解与复吸相关的因素是戒烟研究人员非常感兴趣的。
