title: Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions: a Bayesian approach.
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.
authors.affiliation: Department of Statistics, Shahid Beheshti University, Tehran, 1983963113, Iran.
authors.forename: Taban
authors.initials: T
authors.lastname: Baghfalaki
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Ganjali
authors.forename: Damon
authors.initials: D
authors.lastname: Berridge
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D015658:HIV Infections / Q000188:drug therapy; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D016019:Survival Analysis; D013997:Time Factors; D015215:Zidovudine / Q000009:adverse effects / Q000627:therapeutic use
chemical_list: D015215:Zidovudine
keywords: Bayesian approach; Cox's proportional hazard model; Joint models; Longitudinal data; Markov Chain Monte Carlo; Normal/independent distributions; Time to event data