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.
Indiana University, Department of Medicine, Indianapolis.
M E
ME
Miller
S L
SL
Hui
W M
WM
Tierney
D001581:Benzothiadiazines; D002138:Calibration; D004232:Diuretics; D006801:Humans; D007008:Hypokalemia / Q000175:diagnosis / Q000188:drug therapy / Q000652:urine; D016015:Logistic Models; D011188:Potassium / Q000652:urine; D011237:Predictive Value of Tests; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results; D012307:Risk Factors; D049993:Sodium Chloride Symporter Inhibitors / Q000627:therapeutic use
D001581:Benzothiadiazines; D004232:Diuretics; D049993:Sodium Chloride Symporter Inhibitors; D011188:Potassium