TY - JOUR
T1 - Widen NomoGram for multinomial logistic regression
T2 - An application to staging liver fibrosis in chronic hepatitis C patients
AU - Ardoino, Ilaria
AU - Lanzoni, Monica
AU - Marano, Giuseppe
AU - Boracchi, Patrizia
AU - Sagrini, Elisabetta
AU - Gianstefani, Alice
AU - Piscaglia, Fabio
AU - Biganzoli, Elia M.
PY - 2017/4/1
Y1 - 2017/4/1
N2 - 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.
AB - 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.
KW - Categorical outcome
KW - multinomial logistic regression
KW - nomogram
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85018759989&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018759989&partnerID=8YFLogxK
U2 - 10.1177/0962280214560045
DO - 10.1177/0962280214560045
M3 - Article
AN - SCOPUS:85018759989
VL - 26
SP - 823
EP - 838
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
IS - 2
ER -