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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>11/2013
<mark>Journal</mark>Biometrical Journal
Issue number6
Number of pages22
Pages (from-to)844-865
Publication StatusPublished
Early online date1/08/13
<mark>Original language</mark>English


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.