TY - JOUR
T1 - How to build and interpret a nomogram for cancer prognosis
AU - Iasonos, Alexia
AU - Schrag, Deborah
AU - Raj, Ganesh V.
AU - Panageas, Katherine S.
PY - 2008/3/10
Y1 - 2008/3/10
N2 - 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.
AB - 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.
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U2 - 10.1200/JCO.2007.12.9791
DO - 10.1200/JCO.2007.12.9791
M3 - Review article
C2 - 18323559
AN - SCOPUS:41649106685
SN - 0732-183X
VL - 26
SP - 1364
EP - 1370
JO - Journal of Clinical Oncology
JF - Journal of Clinical Oncology
IS - 8
ER -