Abstract
Semiparametric mixed-effects joint models are flexible for modeling complex longitudinal–competing risks data. Skew distributions are commonly observed for this type of data. Covariates in the joint models are usually measured with substantial errors. We propose a Bayesian method for semiparametric mixed-effects joint models with covariate measurement errors and skew distribution. The methods are illustrated with AIDS clinical data. Simulation results are conducted to validate the proposed methods.
Original language | English |
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Pages (from-to) | 1009-1027 |
Number of pages | 19 |
Journal | Journal of Biopharmaceutical Statistics |
Volume | 27 |
Issue number | 6 |
DOIs | |
State | Published - Nov 2 2017 |
Keywords
- Bayesian inference
- competing risks
- longitudinal data
- measurement error
- partially linear mixed-effects models
- proportional hazard models
- skew distribution
- survival data