A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two-stage method.

ABSTRACT: Ecological momentary assessment (EMA), a data collection method commonly employed in mHealth studies, allows for repeated real-time sampling of individuals' psychological, behavioral, and contextual states. Due to the frequent measurements, data collected using EMA are useful for understanding both the temporal dynamics in individuals' states and how these states relate to adverse health events. Motivated by data from a smoking cessation study, we propose a joint model for analyzing longitudinal EMA data to determine whether certain latent psychological states are associated with repeated cigarette use. Our method consists of a longitudinal submodel-a dynamic factor model-that models changes in the time-varying latent states and a cumulative risk submodel-a Poisson regression model-that co

SUBMITTER: Abbott MR 

PROVIDER: S-EPMC11338709 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature