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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Longitudinally collected clinician CTCAE assessments better predict unfavorable clinical events, whereas patient reports better reflect daily health status. These perspectives are complementary, each providing clinically meaningful information. Inclusion of both types of data in treatment trial results and drug labels appears to be warranted.

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