Abstract

The use of mixed-effects models to understand the evolution of the human immunodeficiency virus (HIV) and the progression of acquired immune deficiency syndrome (AIDS) has been the cornerstone of longitudinal data analysis in recent years. However, data from HIV/AIDS clinical trials can present several complexities. Some of the most common recurrences are related to the situation where the HIV viral load can be undetectable, and the measures of the patient can be registered irregularly due to challenges in the data collection. Although censored mixed-effects models assuming conditionally independent normal random errors are commonly used to analyze this data type, this model may not be appropriate in the presence of outlying observations and responses recorded at irregular intervals.
In this talk, I will propose and illustrate a Bayesian analysis of censored linear mixed-effects models that replace Gaussian assumptions with a flexible class of distributions, such as the scale mixture of normal family distributions, considering a damped exponential correlation structure which was employed to account for within-subject autocorrelation among irregularly observed measures.