Random changepoint modelling of HIV immunologic responses.
We propose a changepoint model for the analysis of longitudinal CD4 T-cell counts for HIV infected subjects following highly active antiretroviral treatment. The profile of CD4 counts for each subject follows a simple, 'broken stick' changepoint model, with random subject-specific parameters, including the changepoint. The model accounts for baseline covariates. The longitudinal CD4 records are censored at the time of the subject going off-study-treatment. This is a potentially informative drop-out mechanism, which we address by modelling it jointly with the CD4 count outcome. The drop-out model incorporates terms from the CD4 model, including the changepoint. The estimation is done in a Bayesian framework, with implementation via Markov chain Monte Carlo methods in the WinBUGS software. Model selection using DIC indicates that the data support the complex random changepoint and informative censoring model.
Ghosh
Pulak
P
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303-3083, USA. pghosh@mathstat.gsu.edu
Vaida
Florin
F
D023241:Antiretroviral Therapy, Highly Active; D001499:Bayes Theorem; D018791:CD4 Lymphocyte Count; D015658:HIV Infections / Q000188:drug therapy* / Q000276:immunology*; D015497:HIV-1 / Q000276:immunology*; D006801:Humans; D016014:Linear Models; D008390:Markov Chains; D018448:Models, Immunological; D009010:Monte Carlo Method