Can nomograms be superior to other prediction tools?

Shahrokh F. Shariat, Umberto Capitanio, Claudio Jeldres, Pierre I. Karakiewicz

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)492-495
Number of pages4
JournalBJU International
Issue number4
Publication statusPublished - Feb 2009


  • Artificial neural networks
  • Classification and regression trees
  • Nomograms
  • Probability tables
  • Risk groupings

ASJC Scopus subject areas

  • Urology


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