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

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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:
ID Code:
66099
Deposited By:
Deposited On:
19 Aug 2013 10:21
Refereed?:
Yes
Published?:
Published
Last Modified:
11 Jun 2020 02:52