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.

翻译：多项预测模型（MPMs）在医疗健康领域具有广泛潜在应用，其关注的主要结局包含多个名义或有序分类。然而，由于MPMs带来额外的方法学复杂性，其实际应用仍较为罕见。本文提供了一份关于如何开发、外部验证及更新MPMs的指南。以先前开发并验证的类风湿关节炎治疗结局MPM为例，我们概述了运用多项逻辑回归构建临床预测模型的指导原则与建议。本文旨在补充现有预测模型研究通用指南。本指南分为三部分：1）结局定义与变量选择，2）模型开发，3）模型评估（包括性能评估、内部与外部验证、模型再校准）。我们阐明了如何评估与解读MPMs的预测性能，并提供了R代码。建议在需要预测名义多分类结局的临床场景中应用MPMs。未来方法学研究可聚焦于MPM特有的变量选择考量及外部验证的样本量标准。