  Multinomial prediction models (MPMs) have a range of potential applications
across healthcare where the primary outcome of interest has multiple nominal or
ordinal categories. However, the application of MPMs is scarce, which may be
due to the added methodological complexities that they bring. This article
provides a guide of how to develop, externally validate, and update MPMs. Using
a previously developed and validated MPM for treatment outcomes in rheumatoid
arthritis as an example, we outline guidance and recommendations for producing
a clinical prediction model using multinomial logistic regression. This article
is intended to supplement existing general guidance on prediction model
research. This guide is split into three parts: 1) Outcome definition and
variable selection, 2) Model development, and 3) Model evaluation (including
performance assessment, internal and external validation, and model
recalibration). We outline how to evaluate and interpret the predictive
performance of MPMs. R code is provided. We recommend the application of MPMs
in clinical settings where the prediction of a nominal polytomous outcome is of
interest. Future methodological research could focus on MPM-specific
considerations for variable selection and sample size criteria for external
validation.
