title: Can nomograms be superior to other prediction tools?
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.
authors.affiliation: Department of Urology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. sfshariat@gmail.com
authors.forename: Shahrokh F
authors.initials: SF
authors.lastname: Shariat
authors.forename: Umberto
authors.initials: U
authors.lastname: Capitanio
authors.forename: Claudio
authors.initials: C
authors.lastname: Jeldres
authors.forename: Pierre I
authors.initials: PI
authors.lastname: Karakiewicz
mesh_terms: D003661:Decision Support Techniques; D006801:Humans; D016571:Neural Networks, Computer; D049451:Nomograms; D012044:Regression Analysis; D018570:Risk Assessment; D014571:Urologic Neoplasms; D014572:Urology / Q000706:statistics & numerical data*