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Construction, validation and updating of a prognostic model for kidney graft survival
Author(s) -
van Houwelingen Hans C.,
Thorogood Jane
Publication year - 1995
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780141806
Subject(s) - overfitting , computer science , covariate , parametric statistics , data set , model selection , statistics , set (abstract data type) , weibull distribution , bayes' theorem , parametric model , bayesian information criterion , unavailability , data mining , bayesian probability , mathematics , artificial intelligence , machine learning , artificial neural network , programming language
Abstract The construction, validation and updating of a prognostic model for kidney graft survival is reported using data from the Eurotransplant database. First, a model is constructed for data from transplantations in the period 1984 to 1987. The model is later updated for the 1988–1990 data. The first data set was randomly split into a training set (two‐thirds of the data) and a validation set (one‐third). To prevent overfitting empirical Bayes estimation of the transplantation centre effect was employed. After that, the validation set was used for fine‐tuning by shrinkage. For updating with the 1988–1990 data parametric models were used after suitable transformation of the time axis; it appeared that survival had slightly improved. This necessitated a correction of the parameters in the exponential model. Correctness of the model was checked by extension to a Weibull model. The lack of fit was statistically significant, but practically ignorable. Recommendations are made to place less emphasis on the selection of variables and cut‐off points, and more emphasis on the fine‐tuning of the prognostic model by means of low‐dimensional parametric models in independent data sets.