Distributional assumptions of most of the existing methods for joint modeling of longitudinal
measurements and time-to-event data cannot allow incorporation of outlier robustness. In
this article, we develop and implement a joint modeling of longitudinal and time-to-event
data using some powerful distributions for robust analyzing that are known as
normal/independent distributions. These distributions include univariate and multivariate
versions of the Student'st, the slash, and the contaminated normal distributions. The …

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 …