TY - JOUR

T1 - Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions

T2 - a Bayesian approach

AU - Baghfalaki, Taban

AU - Ganjali, Mojtaba

AU - Berridge, Damon

PY - 2013/11

Y1 - 2013/11

N2 - 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.

AB - 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.

KW - Bayesian approach

KW - Cox's proportional hazard model

KW - Joint models

KW - Longitudinal data

KW - Markov Chain Monte Carlo

KW - Normal/independent distributions

KW - Time to event data

U2 - 10.1002/bimj.201200272

DO - 10.1002/bimj.201200272

M3 - Journal article

VL - 55

SP - 844

EP - 865

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 6

ER -