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Validation techniques for logistic regression models

Authors

  • Michael E. Miller,

    1. Division of Biostatistics, Indiana University Department of Medicine, and the Regenstrief Institute for Health Care, Riley Research Wing, Rm 135, 702 Barnhill Drive, Indianapolis, IN 46202-5200, U.S.A.
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  • Siu L. Hui,

    1. Division of Biostatistics, Indiana University Department of Medicine, and the Regenstrief Institute for Health Care, Riley Research Wing, Rm 135, 702 Barnhill Drive, Indianapolis, IN 46202-5200, U.S.A.
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  • William M. Tierney

    1. Division of General Internal Medicine, Indiana University Department of Medicine, and the Regenstrief Institute for Health Care, 1001 W. 10th St., Indianapolis, IN 46202, U.S.A.
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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.

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