Abstract Number:
3245
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Yuen Tsz Abby Lau (1), Sharon Xie (1)
Institutions:
(1) University of Pennsylvania, Perelman School of Medicine, N/A
Co-Author:
Sharon Xie
University of Pennsylvania, Perelman School of Medicine
First Author:
Presenting Author:
Abstract Text:
Research on progressive disorders typically involves understanding if the change in biomarker levels can monitor the worsening in medical conditions over time. Their relationship can be quantified by fitting a mixed model for longitudinal medical outcomes with the subject-specific rate of change in biomarker as a fixed effect. As the true long-term change in the biomarker levels is unobservable, biased estimates and invalid inferences may arise if it is replaced by the estimated random slope from a mixed effects model for biomarker measurements. We thus propose a joint modeling method where both longitudinal biomarker measurements and medical outcomes are modeled by linear mixed effects models. We show that the resulting estimators are consistent and asymptotically normal with a sandwich variance estimator. The method is evaluated via simulations and applied to analyze the association between the rate of change in the cerebrospinal fluid biomarker measurements and cognitive decline for participants with mild cognitive impairment in the Alzheimer's Disease Neuroimaging Initiative.
Keywords:
Longitudinal data|Measurement Error|Biomarker| | |
Sponsors:
Biometrics Section
Tracks:
Longitudinal/Correlated Data
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