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Joint modeling of longitudinal data and survival outcomes via threshold regression

Abstract Number:

2855 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Mingyan Yu (1), Zhenke Wu (1), Michael Elliott (1)

Institutions:

(1) University of Michigan, N/A

Co-Author(s):

Zhenke Wu  
University of Michigan
Michael Elliott  
University of Michigan

First Author:

Mingyan Yu  
University of Michigan

Presenting Author:

Mingyan Yu  
N/A

Abstract Text:

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

Keywords:

Longitudinal Biomarker|Joint modeling|Survival outcomes|Threshold regression|Individual-level variability|Study of Women's Health Across the Nation

Sponsors:

ENAR

Tracks:

Miscellaneous

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