A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies
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 propo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2012/194194 |
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| Summary: | 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. |
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| ISSN: | 1687-952X 1687-9538 |