Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data
Article first published online: 4 MAR 2012
© 2012, The International Biometric Society
Volume 68, Issue 3, pages 954–964, September 2012
How to Cite
Zhu, H., Ibrahim, J. G., Chi, Y.-Y. and Tang, N. (2012), Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data. Biometrics, 68: 954–964. doi: 10.1111/j.1541-0420.2012.01745.x
- Issue published online: 26 SEP 2012
- Article first published online: 4 MAR 2012
- Received March 2011. Revised December 2011. Accepted December 2011.
- Bayesian influence measure;
- Perturbation model;
- Sensitivity analysis;
Summary 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.