@article{ATM14736,
author = {Zhongheng Zhang and Michael W. Kattan Kattan},
title = {Drawing Nomograms with R: applications to categorical outcome and survival data},
journal = {Annals of Translational Medicine},
volume = {5},
number = {10},
year = {2017},
keywords = {},
abstract = {Outcome prediction is a major task in clinical medicine. The standard approach to this work is to collect a variety of predictors and build a model of appropriate type. The model is a mathematical equation that connects the outcome of interest with the predictors. A new patient with given clinical characteristics can be predicted for outcome with this model. However, the equation describing the relationship between predictors and outcome is often complex and the computation requires software for practical use. There is another method called nomogram which is a graphical calculating device allowing an approximate graphical computation of a mathematical function. In this article, we describe how to draw nomograms for various outcomes with nomogram() function. Binary outcome is fit by logistic regression model and the outcome of interest is the probability of the event of interest. Ordinal outcome variable is also discussed. Survival analysis can be fit with parametric model to fully describe the distributions of survival time. Statistics such as the median survival time, survival probability up to a specific time point are taken as the outcome of interest.},
issn = {2305-5847}, url = {https://atm.amegroups.org/article/view/14736}
}