Associating Longitudinal Trajectory as a Covariate to Longitudinal Outcomes via a Joint Model

Oregon Convention Center 

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.

Longitudinal data

Measurement Error

Biomarker 

Biometrics Section