title: Bayesian influence measures for joint models for longitudinal and survival data.
abstract: This article develops a variety of influence measures for carrying out perturbation (or sensitivity) analysis to joint models of longitudinal and survival data (JMLS) in Bayesian analysis. A perturbation model is introduced to characterize individual and global perturbations to the three components of a Bayesian model, including the data points, the prior distribution, and the sampling distribution. Local influence measures are proposed to quantify the degree of these perturbations to the JMLS. The proposed methods allow the detection of outliers or influential observations and the assessment of the sensitivity of inferences to various unverifiable assumptions on the Bayesian analysis of JMLS. Simulation studies and a real data set are used to highlight the broad spectrum of applications for our Bayesian influence methods.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, USA. hzhu@bios.unc.edu
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
authors.forename: Yueh-Yun
authors.initials: YY
authors.lastname: Chi
authors.forename: Niansheng
authors.initials: N
authors.lastname: Tang
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D001943:Breast Neoplasms / Q000401:mortality / Q000628:therapy; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008137:Longitudinal Studies / Q000706:statistics & numerical data*; D015233:Models, Statistical; D011788:Quality of Life; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D016019:Survival Analysis