Validation techniques for logistic regression models

Michael E. Miller, Siu L. Hui, William M. Tierney

Research output: Contribution to journalArticle

205 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1213-1226
Number of pages14
JournalStatistics in Medicine
Volume10
Issue number8
DOIs
StatePublished - Aug 1991

Fingerprint

Logistic Regression Model
Prediction Model
Logistic Models
Regression Diagnostics
Influential Observations
Model Validation
Logistic Model
Logistic Regression
Calibration
Predictors
Recommendations
Refinement
Regression Analysis
Model
Review

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

Validation techniques for logistic regression models. / Miller, Michael E.; Hui, Siu L.; Tierney, William M.

In: Statistics in Medicine, Vol. 10, No. 8, 08.1991, p. 1213-1226.

Research output: Contribution to journalArticle

Miller, Michael E. ; Hui, Siu L. ; Tierney, William M. / Validation techniques for logistic regression models. In: Statistics in Medicine. 1991 ; Vol. 10, No. 8. pp. 1213-1226.
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