A critical appraisal of logistic regression-based nomograms, artificial neural networks, classification and regression-tree models, look-up tables and risk-group stratification models for prostate cancer

Felix K H Chun, Pierre I. Karakiewicz, Alberto Briganti, Jochen Walz, Michael W. Kattan, Hartwig Huland, Markus Graefen

Research output: Contribution to journalArticle

Abstract

OBJECTIVE: To evaluate several methods of predicting prostate cancer-related outcomes, i.e. nomograms, look-up tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and risk-group stratification (RGS) models, all of which represent valid alternatives. METHODS: We present four direct comparisons, where a nomogram was compared to either an ANN, a look-up table, a CART model or a RGS model. In all comparisons we assessed the predictive accuracy and performance characteristics of both models. RESULTS: Nomograms have several advantages over ANN, look-up tables, CART and RGS models, the most fundamental being a higher predictive accuracy and better performance characteristics. CONCLUSION: These results suggest that nomograms are more accurate and have better performance characteristics than their alternatives. However, ANN, look-up tables, CART analyses and RGS models all rely on methodologically sound and valid alternatives, which should not be abandoned.

Original languageEnglish
Pages (from-to)794-800
Number of pages7
JournalBJU International
Volume99
Issue number4
DOIs
Publication statusPublished - Apr 2007

Keywords

  • Artificial neural network
  • Classification and regression tree
  • Look-up table
  • Nomogram
  • Outcome
  • Prediction tools
  • Prostate cancer
  • Risk group stratification

ASJC Scopus subject areas

  • Urology

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