Validation techniques for logistic regression models

Validation techniques for logistic regression models This paper presents a comprehensive approach to the validation of logistic prediction models. It reviews measures of overall goodness‐of‐fit, and indices of calibration and refinement. Using a model‐based approach developed by Cox, we adapt logistic regression diagnostic techniques for use in model validation. This allows identification of problematic predictor variables in the prediction model as well as influential observations in the validation data that adversely affect the fit of the model. In appropriate situations, recommendations are made for correction of models that provide poor fit. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Medicine Wiley

Validation techniques for logistic regression models

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Publisher
Wiley
Copyright
Copyright © 1991 John Wiley & Sons, Ltd.
ISSN
0277-6715
eISSN
1097-0258
D.O.I.
10.1002/sim.4780100805
Publisher site
See Article on Publisher Site

Abstract

This paper presents a comprehensive approach to the validation of logistic prediction models. It reviews measures of overall goodness‐of‐fit, and indices of calibration and refinement. Using a model‐based approach developed by Cox, we adapt logistic regression diagnostic techniques for use in model validation. This allows identification of problematic predictor variables in the prediction model as well as influential observations in the validation data that adversely affect the fit of the model. In appropriate situations, recommendations are made for correction of models that provide poor fit.

Journal

Statistics in MedicineWiley

Published: Aug 1, 1991

References

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