A BAYESIAN TIME-VARYING EFFECT MODEL FOR BEHAVIORAL MHEALTH DATA

• 成果类型: Article
• 署名作者: Koslovsky, Matthew D.; Hebert, Emily T.; Businelle, Michael S.; Vannucci, Marina
• 署名单位: Colorado State University System; Colorado State University Fort Collins; University of Oklahoma System; University of Oklahoma - Norman; University of Oklahoma Health Sciences Center; Rice University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1402
• 发表日期: 2020
• 页码: 1878-1902
• 关键词: ecological momentary assessment; random effects selection; variable selection; smoking lapse; markov-models; cigarette-smoking; longitudinal data; negative affect; inference; priors

摘要

The integration of mobile health (mHealth) devices into behavioral health research has fundamentally changed the way researchers and interventionalists are able to collect data as well as deploy and evaluate intervention strategies. In these studies, researchers often collect intensive longitudinal data (ILD) using ecological momentary assessment methods which aim to capture psychological, emotional and environmental factors that may relate to a behavioral outcome in near real time. In order to investigate ILD collected in a novel, smartphone-based smoking cessation study, we propose a Bayesian variable selection approach for time-varying effect models, designed to identify dynamic relations between potential risk factors and smoking behaviors in the critical moments around a quit attempt. We use parameter-expansion and data-augmentation techniques to efficiently explore how the underlying structure of these relations varies over time and across subjects. We achieve deeper insights into these relations by introducing nonparametric priors for regression coefficients that cluster similar effects for risk factors while simultaneously determining their inclusion. Results indicate that our approach is well positioned to help researchers effectively evaluate, design and deliver tailored intervention strategies in the critical moments surrounding a quit attempt.