Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions:a Bayesian approach

Baghfalaki, Taban and Ganjali, Mojtaba and Berridge, Damon (2013) Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions:a Bayesian approach. Biometrical Journal, 55 (6). pp. 844-865. ISSN 0323-3847

Full text not available from this repository.

Abstract

Joint modeling of longitudinal data and survival data has been used widely for analyzing AIDS clinical trials, where a biological marker such as CD4 count measurement can be an important predictor of survival. In most of these studies, a normal distribution is used for modeling longitudinal responses, which leads to vulnerable inference in the presence of outliers in longitudinal measurements. Powerful distributions for robust analysis are normal/independent distributions, which include univariate and multivariate versions of the Student's t, the slash and the contaminated normal distributions in addition to the normal. In this paper, a linear-mixed effects model with normal/independent distribution for both random effects and residuals and Cox's model for survival time are used. For estimation, a Bayesian approach using Markov Chain Monte Carlo is adopted. Some simulation studies are performed for illustration of the proposed method. Also, the method is illustrated on a real AIDS data set and the best model is selected using some criteria.

Item Type: Journal Article
Journal or Publication Title: Biometrical Journal
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2700
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 66099
Deposited By: ep_importer_pure
Deposited On: 19 Aug 2013 10:21
Refereed?: Yes
Published?: Published
Last Modified: 01 Jan 2020 08:35
URI: https://eprints.lancs.ac.uk/id/eprint/66099

Actions (login required)

View Item View Item