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.

@article{Thibaut2005JointMO,
  title={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={Rodolphe Thi{\'e}baut and H{\'e}l{\`e}ne Jacqmin-Gadda and Abdel Babiker and Daniel Commenges},
  journal={Statistics in medicine},
  year={2005},
  volume={24 1},
  pages={65-82}
}
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… CONTINUE READING