A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies. (15th February 2012)

• Record Type: Journal Article
• Authors: Murawska, Magdalena; Rizopoulos, Dimitris; Lesaffre, Emmanuel
• Other Names: Yi Grace Y. Academic Editor.
• Is Part Of: Journal of probability and statistics. Volume 2012(2012)
• Publication Date: 2012-02-15
• Subjects: Probabilities -- Periodicals; Mathematical statistics -- Periodicals; Mathematical statistics; Probabilities; Periodicals; 519
• DOI: 10.1155/2012/194194
• Languages: English
• ISSNs: 1687-952X

Abstract

Abstract : In transplantation studies, often longitudinal measurements are collected for important markers prior to the actual transplantation. Using only the last available measurement as a baseline covariate in a survival model for the time to graft failure discards the whole longitudinal evolution. We propose a two-stage approach to handle this type of data sets using all available information. At the first stage, we summarize the longitudinal information with nonlinear mixed-effects model, and at the second stage, we include the Empirical Bayes estimates of the subject-specific parameters as predictors in the Cox model for the time to allograft failure. To take into account that the estimated subject-specific parameters are included in the model, we use a Monte Carlo approach and sample from the posterior distribution of the random effects given the observed data. Our proposal is exemplified on a study of the impact of renal resistance evolution on the graft survival.