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):
First Author:
Presenting Author:
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
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
No
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand