Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67389
Title: Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data
Authors: Tang, AM
Zhao, XQ 
Tang, NS
Keywords: Bayesian Lasso
Bayesian penalized splines
Joint models
Mixture of normals
Survival analysis
Issue Date: 2017
Publisher: Wiley-VCH
Source: Biometrical journal, 2017, v. 59, no. 1, p. 57-78 How to cite?
Journal: Biometrical journal 
Abstract: 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 BreastCancer Study Group (IBCSG) is used to illustrate the proposed methodologies.
URI: http://hdl.handle.net/10397/67389
ISSN: 0323-3847
EISSN: 1521-4036
DOI: 10.1002/bimj.201500070
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