Assessing the Performance of Prediction Models

Original by E.W.Steyberg, A.J.Vickers et al, 2010, 11 pagesHamster_gagarin_linkedin
hamster writter This summary note was posted on 3 January 2017, by in Credit risk Finance #, #, #
  • New measures: Variants of the c statistics for survival, reclassification tables, NR, IDI
  • Decision analytics measures : “decision curves” by making decisions based on model predictions
  • Validation in fully independent, external data is the best way to compare the performance of a model with and without a new marker
  • Nagelkerke’s R2 test  for logarithm predictions is based on the difference in the -2 log likelihood of a model and a model with one or more predictors
  • Scaled Brier Score very similar to the Pearson’s R2 statistic
  • C statistic is related to Somer’s D
  • A popular extension of the C statistics with censored data can be obtained by ignoring the pairs that cannot be ordered
  • Addition to the c statistics is the discriminatory slope
  • Smoothing technique: Loess algorithm
  • Recalibration framework proposed by Cox
    Cook proposed a reclassification test as a variant of the Hosmer-Lemeshow statistic within the reclassified categories leading to the CH2 statistic (idea extended by Pencia et al by conditioning on the outcome)
  • Net classification Improvement (NRI)
  • Youden index implies weighting by the non-events odds
  • Documentation  of decision-curve analysis can be found at:
  • Cook’s reclassification test
  • Reclassification can be assessed using a scatter plot before and after change in the model.
  • Use design library to generate good plots in R
  • Recalibration parameters as proposed by Cox (intercept and calibration slope) are more informative
  • Key information for comparing performances of 2 models is contained in the margins of the reclassification tables