title: Widen NomoGram for multinomial logistic regression: an application to staging liver fibrosis in chronic hepatitis C patients.
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.
authors.affiliation: 1 Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
authors.forename: Ilaria
authors.initials: I
authors.lastname: Ardoino
authors.affiliation: 1 Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.|2 Fondazione IRCCS Ca'Granda, Ospedale Maggiore Policlinico, Milano, Italy.
authors.forename: Monica
authors.initials: M
authors.lastname: Lanzoni
authors.affiliation: 3 Fondazione IRCCS Istituto Nazionale Tumori, Milano, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Marano
authors.affiliation: 1 Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
authors.forename: Patrizia
authors.initials: P
authors.lastname: Boracchi
authors.affiliation: 4 Department of Clinical Medicine, University and General Hospital, Bologna, Italy.
authors.forename: Elisabetta
authors.initials: E
authors.lastname: Sagrini
authors.affiliation: 4 Department of Clinical Medicine, University and General Hospital, Bologna, Italy.
authors.forename: Alice
authors.initials: A
authors.lastname: Gianstefani
authors.affiliation: 4 Department of Clinical Medicine, University and General Hospital, Bologna, Italy.
authors.forename: Fabio
authors.initials: F
authors.lastname: Piscaglia
authors.affiliation: 1 Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.|3 Fondazione IRCCS Istituto Nazionale Tumori, Milano, Italy.
authors.forename: Elia M
authors.initials: EM
authors.lastname: Biganzoli
mesh_terms: D000328:Adult; D056808:Biostatistics / Q000379:methods; D005260:Female; D019698:Hepatitis C, Chronic / Q000473:pathology*; D006801:Humans; D008103:Liver Cirrhosis / Q000473:pathology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D049451:Nomograms
keywords: Categorical outcome; multinomial logistic regression; nomogram; prediction