A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects.
This article studies a general joint model for longitudinal measurements and competing risks survival data. The model consists of a linear mixed effects sub-model for the longitudinal outcome, a proportional cause-specific hazards frailty sub-model for the competing risks survival data, and a regression sub-model for the variance-covariance matrix of the multivariate latent random effects based on a modified Cholesky decomposition. The model provides a useful approach to adjust for non-ignorable missing data due to dropout for the longitudinal outcome, enables analysis of the survival outcome with informative censoring and intermittently measured time-dependent covariates, as well as joint analysis of the longitudinal and survival outcomes. Unlike previously studied joint models, our model allows for heterogeneous random covariance matrices. It also offers a framework to assess the homogeneous covariance assumption of existing joint models. A Bayesian MCMC procedure is developed for parameter estimation and inference. Its performances and frequentist properties are investigated using simulations. A real data example is used to illustrate the usefulness of the approach.
Huang
Xin
X
Amgen Inc., 1120 Veterans Boulevard, Mail Stop ASF3-3, South San Francisco, CA 94080, USA. xin@amgen.com
Li
Gang
G
Elashoff
Robert M
RM
Pan
Jianxin
J
D001499:Bayes Theorem; D006801:Humans; D008137:Longitudinal Studies / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D012680:Sensitivity and Specificity; D016019:Survival Analysis