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A0599
Title: Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness Authors:  Mauricio Castro - Pontificia Universidad Catolica de Chile (Chile) [presenting]
Victor Hugo Lachos Davila - University of Connecticut (United States)
Wan-Lun Wang - Feng Chia University (Taiwan)
Vanda Inacio - University of Edinburgh (United Kingdom)
Abstract: In biomedical studies, the analysis of longitudinal data based on Gaussian assumptions is common practice. Nevertheless, more often than not, the observed responses are naturally skewed, rendering the use of symmetric mixed effects models inadequate. In addition, it is also common in clinical assays that the patients responses are subject to some upper and/or lower quantification limit, depending on the diagnostic assays used for their detection. Furthermore, the responses may also often present a nonlinear relation with some covariates such as time. To address the aforementioned three situations, we consider a Bayesian semiparametric model based on a combination of splines and wavelets for longitudinal censored data using the multivariate skew-normal distribution. The proposed semiparametric approach is focused on the use of splines to approximate the nonlinear general mean and wavelets for modeling the individual trajectories per subject. Lastly, the use of the skew normal distribution allows to capture the skewness of the data. The newly developed method is illustrated through simulated data and real data concerning AIDS/HIV infected patients.