摘要：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.
