Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data.
This paper presents a novel semiparametric joint model for multivariate longitudinal and survival data (SJMLS) by relaxing the normality assumption of the longitudinal outcomes, leaving the baseline hazard functions unspecified and allowing the history of the longitudinal response having an effect on the risk of dropout. Using Bayesian penalized splines to approximate the unspecified baseline hazard function and combining the Gibbs sampler and the Metropolis-Hastings algorithm, we propose a Bayesian Lasso (BLasso) method to simultaneously estimate unknown parameters and select important covariates in SJMLS. Simulation studies are conducted to investigate the finite sample performance of the proposed techniques. An example from the International Breast Cancer Study Group (IBCSG) is used to illustrate the proposed methodologies.
Department of Statistics, Yunnan University, Kunming, 650091, China.
An-Min
AM
Tang
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.|Shenzhen Research Institute, Hong Kong Polytechnic University, Shenzhen 518057, China.
Xingqiu
X
Zhao
Department of Statistics, Yunnan University, Kunming, 650091, China.
Nian-Sheng
NS
Tang
D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000401:mortality; D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D016019:Survival Analysis
Bayesian Lasso; Bayesian penalized splines; Joint models; Mixture of normals; Survival analysis