Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease.

ABSTRACT:

Background

Joint modeling is appropriate when one wants to predict the time to an event with covariates that are measured longitudinally and are related to the event. An underlying random effects structure links the survival and longitudinal submodels and allows for individual-specific predictions. Multiple time-varying and time-invariant covariates can be included to potentially increase prediction accuracy. The goal of this study was to estimate a multivariate joint model on several longitudinal observational studies of Huntington's disease, examine external validity performance, and compute individual-specific predictions for characterizing disease progression. Emphasis was on the survival submodel for predicting the hazard of motor diagnosis.

Methods

Data from four obser

SUBMITTER: Long JD 

PROVIDER: S-EPMC6240282 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature