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的应用潜力，尤其适用于关注结果变量为多分类名义或有序变量的研究场景。然而，由于此类模型带来的额外方法学复杂性，其实际应用仍较为有限。本文旨在提供开发、外部验证及更新MPMs的实践指南。以先前已开发并验证的类风湿关节炎治疗结局MPM为例，我们系统阐述了基于多项逻辑回归构建临床预测模型的指导原则与建议。本文意在补充现有预测模型研究的通用指南，全文分为三部分：（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、内部与外部验证以及模型再校准）。我们详细阐释了如何评价与解读MPMs的预测性能，并附有R代码实现。建议在关注名义多分类结局预测的临床场景中积极应用MPMs。未来方法学研究可聚焦于MPMs特有的变量选择策略及外部验证样本量标准的制定。