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How to develop, validate, and update clinical prediction models using multinomial logistic regression.
Gehringer, Celina K; Martin, Glen P; Van Calster, Ben; Hyrich, Kimme L; Verstappen, Suzanne M M; Sergeant, Jamie C.
Afiliación
  • Gehringer CK; Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK; Centre for Biostatistics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK. Electronic
  • Martin GP; Division of Informatics, Imaging and Data Sciences, Centre for Health Informatics, University of Manchester, Manchester, UK.
  • Van Calster B; Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands; Department of Development & Regeneration, KU Leuven, Leuven, Belgium.
  • Hyrich KL; Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science
  • Verstappen SMM; Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science
  • Sergeant JC; Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK; Centre for Biostatistics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
J Clin Epidemiol ; 174: 111481, 2024 Jul 25.
Article en En | MEDLINE | ID: mdl-39067542
ABSTRACT

OBJECTIVES:

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. STUDY DESIGN AND

SETTING:

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.

RESULTS:

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.

CONCLUSION:

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos