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

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 poten...

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Bibliographic Details
Published in:Statistics in medicine 2005-01, Vol.24 (1), p.65-82
Main Authors: Thiébaut, Rodolphe, Jacqmin-Gadda, Hélène, Babiker, Abdel, Commenges, Daniel
Format: Article
Language:English
Subjects:
Anti-HIV Agents - therapeutic use
Antiretroviral Therapy, Highly Active - standards
bivariate mixed model
CD4 Lymphocyte Count
Cohort Studies
HIV infection
HIV Infections - drug therapy
HIV Infections - immunology
HIV Infections - transmission
HIV Infections - virology
HIV-1 - genetics
HIV-1 - immunology
Humans
informative dropout
left-censoring
Longitudinal Studies
Models, Biological
Models, Statistical
Patient Dropouts
repeated measurements
RNA, Viral - blood
Substance Abuse, Intravenous
Viral Load
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Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1923