How to develop, validate, and update clinical prediction models using multinomial logistic regression

Multicategory prediction models (MPMs) can be used in health care when the primary outcome of interest has more than two categories. The application of MPMs is scarce, possibly due to added methodological complexities compared to binary outcome models. We provide a guide of how to develop, validate, and update clinical prediction models based on multinomial logistic regression.

We present guidance and recommendations based on recent methodological literature, illustrated by a previously developed and validated MPM for treatment outcomes in rheumatoid arthritis. Prediction models using multinomial logistic regression can be developed for nominal outcomes, but also for ordinal outcomes. 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 multicategory outcome is of interest. Future methodological research could focus on MPM-specific considerations for variable selection and sample size criteria for external validation.