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Resampling and cross‐validation techniques: a tool to reduce bias caused by model building?
Author(s) -
Schumacher Martin,
Holländer Norbert,
Sauerbrei Willi
Publication year - 1997
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/(sici)1097-0258(19971230)16:24<2813::aid-sim701>3.0.co;2-z
Subject(s) - resampling , computer science , cross validation , bootstrapping (finance) , post hoc , heuristic , quality (philosophy) , regression , machine learning , data mining , statistics , econometrics , artificial intelligence , mathematics , medicine , philosophy , dentistry , epistemology
The process of model building involved in the analysis of many medical studies may lead to a considerable amount of over‐optimism with respect to the predictive ability of the ‘final’ regression model. In this paper we illustrate this phenomenon in a simple cutpoint model and explore to what extent bias can be reduced by using cross‐validation and bootstrap resampling. These computer intensive methods are compared to an ad hoc approach and to a heuristic method. Besides illustrating all proposals with the data from a breast cancer study we perform a simulation study in order to assess the quality of the methods. © 1997 John Wiley & Sons, Ltd.