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What are logistic and Cox regression nomograms useful for? Why would I want to use nomograms? Why should I use nomolog and nomocox?

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- Nomograms allow calculating output probabilities for predictive models with a visual approach. This is useful when presenting the results of your predictive models in a convenient way in a printed format. Nomograms are better than most alternative approaches, such as providing the full regression formula or a table with all regression coefficients. Another possibility is to provide an on-line calculator, but this requires some programming and usually also "hides" the underlying model, while with a nomogram, the process is fully transparent.

- With nomograms, variable importance is clear at-a-glance. The longer the line corresponding to a given variable, the more important a variable is. Therefore, nomograms can also be used in descriptive or exploratory data analysis. 公卫论坛

- Nomograms can be useful for teaching purposes. Linear regressions have an intuitive interpretation. Logistic and Cox regressions are harder to understand. Most people are visual learners and providing a graphical representation can help "demystifying" these regressions. In fact, the nomograms generated with nomolog and nomocox separate the calculation mechanisms in two clear steps: (i) calculation of the "linear predictor", (ii) transformation of the "linear predictor" into a probability of event.

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- In our opinion, nomolog and nomocox are easy to use compared to other software packages with similar capabilities.

- The resulting nomograms can be easily customized (using Stata's graph editor), as opposed to alternative implementations. Also, the resulting graphics can be stored in high quality vector format ("infinite resolution") graphics, which are often required in scientific journals.

- We also believe that both nomolog and nomocox handle all possible two-variable interactions gracefully (Categorical x Continuous, Categorical x Categorical, Continuous x Continuous).

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Zlotnik A, Abraira V. A general-purpose nomogram generator for predictive logistic regression models. Stata Journal. 2015. Volume 15, Number 2.

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