A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data.
Joint models for a time-to-event (e.g., survival) and a longitudinal response have generated considerable recent interest. The longitudinal data are assumed to follow a mixed effects model, and a proportional hazards model depending on the longitudinal random effects and other covariates is assumed for the survival endpoint. Interest may focus on inference on the longitudinal data process, which is informatively censored, or on the hazard relationship. Several methods for fitting such models have been proposed, most requiring a parametric distributional assumption (normality) on the random effects. A natural concern is sensitivity to violation of this assumption; moreover, a restrictive distributional assumption may obscure key features in the data. We investigate these issues through our proposal of a likelihood-based approach that requires only the assumption that the random effects have a smooth density. Implementation via the EM algorithm is described, and performance and the benefits for uncovering noteworthy features are illustrated by application to data from an HIV clinical trial and by simulation.
Duke Scholars
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Related Subject Headings
- Statistics & Probability
- Randomized Controlled Trials as Topic
- Proportional Hazards Models
- Monte Carlo Method
- Longitudinal Studies
- Likelihood Functions
- Humans
- HIV Infections
- Disease Progression
- Computer Simulation
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Statistics & Probability
- Randomized Controlled Trials as Topic
- Proportional Hazards Models
- Monte Carlo Method
- Longitudinal Studies
- Likelihood Functions
- Humans
- HIV Infections
- Disease Progression
- Computer Simulation