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Updated: Jun 28, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Can nomograms be superior to other prediction tools?
Shahrokh F Shariat1, Umberto Capitanio, Claudio Jeldres
1Department of Urology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. sfshariat@gmail.com
Abstract:
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.
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