A generic nomogram for multinomial prediction models: theory and guidance for construction

Background: The use of multinomial logistic regression models is advocated for modeling the associations of covariates with three or more mutually exclusive outcome categories. As compared to a binary logistic regression analysis, the simultaneous modeling of multiple outcome categories using a multinomial model often better resembles the clinical setting, where a physician typically must distinguish between more than two possible diagnoses or outcome events for an individual patient (e.g., the differential diagnosis). A disadvantage of the multinomial logistic model is that the interpretation of its results is often complex. In particular, the calculation of predicted probabilities for the various outcomes requires a series of careful calculations. Nomograms are widely used in studies reporting binary logistic regression models to facilitate the interpretation of the results and allow the calculation of the predicted probability for individuals. Methods and results: In this paper we outline an approach for deriving a generic nomogram for multinomial logistic regression models and an accompanying scoring chart that can further simplify the calculation of predicted multinomial probabilities. We illustrate the use of the nomogram and scoring chart and their interpretation using a