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Joint modelling of bivariate longitudinal data with informative dropout and left‐censoring, with application to the evolution of CD4+ cell count and HIV RNA viral load in response to treatment of HIV infection
Author(s) -
Thiébaut Rodolphe,
JacqminGadda Hélène,
Babiker Abdel,
Commenges Daniel
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1923
Subject(s) - bivariate analysis , dropout (neural networks) , viral load , censoring (clinical trials) , human immunodeficiency virus (hiv) , longitudinal data , longitudinal study , medicine , statistics , virology , computer science , mathematics , data mining , machine learning
Several methodological issues occur in the context of the longitudinal study of HIV markers evolution. Three of them are of particular importance: (i) correlation between CD4+ T lymphocytes (CD4+) and plasma HIV RNA; (ii) left‐censoring of HIV RNA due to a lower quantification limit; (iii) and potential informative dropout. We propose a likelihood inference for a parametric joint model including a bivariate linear mixed model for the two markers and a lognormal survival model for the time to drop out. We apply the model to data from patients starting antiretroviral treatment in the CASCADE collaboration where all of the three issues needed to be addressed. Copyright © 2004 John Wiley & Sons, Ltd.