ArticleAnnals of translational medicine2017
Drawing Nomograms with R: applications to categorical outcome and survival data.
Article in Annals of translational medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 151 papers.
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151 citing papers in PubMed.
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- Development and validation of an interpretable nomogram for predicting the risk of the prolonged postoperative length of stay for tuberculous spondylitis: a novel approach for risk stratification.BMC musculoskeletal disorders · 2025Article
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- Nomogram model for predicting the long-term prognosis of cervical cancer patients: a population-based study in Mato Grosso, Brazil.BMC cancer · 2025Article
- Development and validation of Galectin-3 and CVAI-based model for predicting cognitive impairment in type 2 diabetes.Journal of endocrinological investigation · 2025Article
91 more citing papers are in PubMed but not listed here.
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2 authors.
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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.
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