Widen NomoGram for multinomial logistic regression: An application to staging liver fibrosis in chronic hepatitis C patients

Ilaria Ardoino, Monica Lanzoni, Giuseppe Marano, Patrizia Boracchi, Elisabetta Sagrini, Alice Gianstefani, Fabio Piscaglia, Elia M. Biganzoli

Research output: Contribution to journalArticlepeer-review

Abstract

The interpretation of regression models results can often benefit from the generation of nomograms, 'user friendly' graphical devices especially useful for assisting the decision-making processes. However, in the case of multinomial regression models, whenever categorical responses with more than two classes are involved, nomograms cannot be drawn in the conventional way. Such a difficulty in managing and interpreting the outcome could often result in a limitation of the use of multinomial regression in decision-making support. In the present paper, we illustrate the derivation of a non-conventional nomogram for multinomial regression models, intended to overcome this issue. Although it may appear less straightforward at first sight, the proposed methodology allows an easy interpretation of the results of multinomial regression models and makes them more accessible for clinicians and general practitioners too. Development of prediction model based on multinomial logistic regression and of the pertinent graphical tool is illustrated by means of an example involving the prediction of the extent of liver fibrosis in hepatitis C patients by routinely available markers.

Original languageEnglish
Pages (from-to)823-838
Number of pages16
JournalStatistical Methods in Medical Research
Volume26
Issue number2
DOIs
Publication statusPublished - Apr 1 2017

Keywords

  • Categorical outcome
  • multinomial logistic regression
  • nomogram
  • prediction

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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