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Biom J. 2013 Nov;55(6):844-65. doi: 10.1002/bimj.201200272. Epub 2013 Aug 1.

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

Author information

1
Department of Statistics, Shahid Beheshti University, Tehran, 1983963113, Iran.

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.

KEYWORDS:

Bayesian approach; Cox's proportional hazard model; Joint models; Longitudinal data; Markov Chain Monte Carlo; Normal/independent distributions; Time to event data

PMID:
23907983
DOI:
10.1002/bimj.201200272
[Indexed for MEDLINE]

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