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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
Author(s) -
Chun Felix K.H.,
Karakiewicz Pierre I.,
Briganti Alberto,
Walz Jochen,
Kattan Michael W.,
Huland Hartwig,
Graefen Markus
Publication year - 2007
Publication title -
bju international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 148
eISSN - 1464-410X
pISSN - 1464-4096
DOI - 10.1111/j.1464-410x.2006.06694.x
Subject(s) - nomogram , cart , logistic regression , artificial neural network , computer science , artificial intelligence , regression , decision tree , machine learning , statistics , regression analysis , mathematics , engineering , medicine , oncology , mechanical engineering
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.