39 Joint modeling of longitudinal data and survival outcomes via threshold regression

Oregon Convention Center 

Longitudinal biomarker data and health outcomes are regularly collected in numerous epidemiology studies for studying the prediction of biomarker trajectories to health outcomes, which informs health interventions. Many existing methods that connect longitudinal trajectories with health outcomes put their attention mainly on mean profiles, treating variabilities as nuisance parameters. However, variabilities may also carry a substantial information. We develop a Bayesian joint modeling approach to study the association between mean trajectories along with variabilities in longitudinal biomarker and survival times. To model the longitudinal biomarker, we adopt linear mixed effects model and allow individuals to have their own variabilities. Following that, we model the survival times by incorporating random effects and variabilities as predictors through threshold regression, also known as "first-hitting-time model" which allows for non-proportional hazards. We apply the proposed model to data from Study of Women's Health Across the Nation and reveal that higher mean values and variabilities of Follicle-stimulating hormone are associated with an earlier age of final menstrual period

Longitudinal Biomarker

Joint modeling

Survival outcomes

Threshold regression

Individual-level variability

Study of Women's Health Across the Nation 

ENAR