Can nomograms be superior to other prediction tools?
Article first published online: 2 OCT 2008
© 2008 THE AUTHORS. JOURNAL COMPILATION © 2008 BJU INTERNATIONAL
Volume 103, Issue 4, pages 492–497, February 2009
How to Cite
Shariat, S. F., Capitanio, U., Jeldres, C. and Karakiewicz, P. I. (2009), Can nomograms be superior to other prediction tools?. BJU International, 103: 492–497. doi: 10.1111/j.1464-410X.2008.08073.x
- Issue published online: 2 FEB 2009
- Article first published online: 2 OCT 2008
- Accepted for publication 18 June 2008
- artificial neural networks;
- risk groupings;
- probability tables;
- classification and regression trees
Accurate estimates of the likelihood of treatment success, complications and long-term morbidity are essential for counselling and informed decision-making in patients with urological malignancies. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous patient distribution. Nomograms, risk groupings, artificial neural networks (ANNs), probability tables, and classification and regression tree (CART) analyses represent the available decision aids that can be used within these tasks. We critically reviewed available decision aids (nomograms, risk groupings, ANNs, probability tables and CART analyses) and compared their ability to predict the outcome of interest. Of the available decision aids, nomograms provide individualized evidence-based and highly accurate risk estimates that facilitate management-related decisions. We suggest the use of nomograms for the purpose of evidence-based, individualized decision-making.