Predicting renal graft failure using multivariate longitudinal profiles.
Patients who have undergone renal transplantation are monitored longitudinally at irregular time intervals over 10 years or more. This yields a set of biochemical and physiological markers containing valuable information to anticipate a failure of the graft. A general linear, generalized linear, or nonlinear mixed model is used to describe the longitudinal profile of each marker. To account for the correlation between markers, the univariate mixed models are combined into a multivariate mixed model (MMM) by specifying a joint distribution for the random effects. Due to the high number of markers, a pairwise modeling strategy, where all possible pairs of bivariate mixed models are fitted, is used to obtain parameter estimates for the MMM. These estimates are used in a Bayes rule to obtain, at each point in time, the prognosis for long-term success of the transplant. It is shown that allowing the markers to be correlated can improve this prognosis.
Fieuws
Steffen
S
Biostatistical Centre, Katholieke Universiteit Leuven, Leuven, Belgium. steffen.fieuws@med.kuleuven.be
Verbeke
Geert
G
Maes
Bart
B
Vanrenterghem
Yves
Y
D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis; D001699:Biometry / Q000379:methods; D001794:Blood Pressure; D016002:Discriminant Analysis; D005919:Glomerular Filtration Rate; D006084:Graft Rejection / Q000097:blood / Q000503:physiopathology* / Q000652:urine; D006400:Hematocrit; D006801:Humans; D016030:Kidney Transplantation / Q000276:immunology*; D008137:Longitudinal Studies; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D011507:Proteinuria / Q000503:physiopathology; D013269:Stochastic Processes
D015415:Biomarkers