ROBUST JOINT MODELLING OF LEFT-CENSORED LONGITUDINAL DATA AND SURVIVAL DATA WITH APPLICATION TO HIV VACCINE STUDIES

In jointly modelling longitudinal and survival data, the longitudinal data may be complex in the sense that they may contain outliers and may be left censored. Motivated from an HIV vaccine study, we propose a robust method for joint models of longitudinal and survival data, where the outliers in longi-tudinal data are addressed using a multivariate t-distribution for b-outliers and using an M-estimator for e-outliers. We also propose a computationally effi-cient method for approximate likelihood inference. The proposed method is evaluated by simulation studies. Based on the proposed models and method, we analyze the HIV vaccine data and find a strong association between lon-gitudinal biomarkers and the risk of HIV infection.