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.
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.


CONCLUSIONS
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.
Chun
Felix K-H
FK
Cancer Prognostics and Health Outcomes Unit, University of Montreal, Montreal, Quebec, Canada.
Karakiewicz
Pierre I
PI
Briganti
Alberto
A
Walz
Jochen
J
Kattan
Michael W
MW
Huland
Hartwig
H
Graefen
Markus
M
D001707:Biopsy, Needle; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models; D008297:Male; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D049451:Nomograms; D011237:Predictive Value of Tests; D011379:Prognosis; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000145:classification / Q000473:pathology*; D012044:Regression Analysis; D018570:Risk Assessment