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ABSTRACT
In HIV/AIDS study, the measurements viral load are often highly skewed and left-censored because of a lower detection limit. Furthermore, a terminal event (e.g., death) stops the follow-up process. The time to terminal event may be dependent on the viral load measurements. In this article, we present a joint analysis framework to model the censored longitudinal data with skewness and a terminal event process. The estimation is carried out by adaptive Gaussian quadrature techniques in SAS procedure NLMIXED. The proposed model is evaluated by a simulation study and is applied to the motivating Multicenter AIDS Cohort Study (MACS).
MATHEMATICS SUBJECT CLASSIFICATION:
Acknowledgments
Sheng Luo’s research was supported in part by the National Institute of Neurological Disorders and Stroke under Award Number R01NS091307 and by the National Center for Advancing Translational Sciences under Award Number KL2-TR000370. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
