title: Improving calibration of logistic regression models by local estimates.
abstract: OBJECTIVE
  To improve the calibration of logistic regression (LR) estimates using local information.
  
  
  BACKGROUND
  Individualized risk assessment tools are increasingly being utilized. External validation of these tools often reveals poor model calibration.
  
  
  METHODS
  We combine a clustering algorithm with an LR model to produce probability estimates that are close to the true probabilities for a particular case. The new method is compared to a standard LR model in terms of calibration, as measured by the sum of absolute differences (SAD) between model estimates and true probabilities, and discrimination, as measured by area under the ROC curve (AUC).
  
  
  RESULTS
  We evaluate the new method on two synthetic data sets. SADs are significantly lower (p < 0.0001) in both data sets, and AUCs are significantly higher in one data set (p < 0.01).
  
  
  CONCLUSIONS
  The results suggest that the proposed method may be useful to improve the calibration of LR models.
authors.affiliation: Department of Biomedical Engineering, University of Health Sciences, Medical Informatics and Technology, Hall, Austria.
authors.forename: Melanie
authors.initials: M
authors.lastname: Osl
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
authors.forename: Christian
authors.initials: C
authors.lastname: Baumgartner
authors.forename: Bernhard
authors.initials: B
authors.lastname: Tilg
authors.forename: Stephan
authors.initials: S
authors.lastname: Dreiseitl
mesh_terms: D000465:Algorithms; D002138:Calibration; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D016015:Logistic Models; D016016:Proportional Hazards Models; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods* / Q000592:standards