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How To Build and Interpret a Nomogram for Cancer Prognosis

Alexia Iasonos, Deborah Schrag, Ganesh V. Raj, Katherine S. Panageas

From the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY; and Department of Urology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX

Corresponding author: Alexia Iasonos, PhD, Assistant Attending Biostatistician, Epidemiology-Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 E 63rd St, 3rd Floor, New York, NY 10021; e-mail: iasonosa{at}mskcc.org

Nomograms are widely used for cancer prognosis, primarily because of their ability to reduce statistical predictive models into a single numerical estimate of the probability of an event, such as death or recurrence, that is tailored to the profile of an individual patient. User-friendly graphical interfaces for generating these estimates facilitate the use of nomograms during clinical encounters to inform clinical decision making. However, the statistical underpinnings of these models require careful scrutiny, and the degree of uncertainty surrounding the point estimates requires attention. This guide provides a nonstatistical audience with a methodological approach for building, interpreting, and using nomograms to estimate cancer prognosis or other health outcomes.

Presented in part at the 43rd Annual Meeting of the American Society of Clinical Oncology, June 1-5, 2007, Chicago, IL (abstr 6526).

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.