Risk prediction models for discrete ordinal outcomes: calibration and the impact of the proportional odds assumption

DOI10.1002/sim.9281zbMATH Open1547.62214MaRDI QIDQ6626790

Michael Edlinger, Ben van Calster, Hannes F. Alber, Maarten van Smeden, Maria Wanitschek

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

simulationcalibrationpredictive performancerisk predictionproportional oddsdiscrete ordinal outcome

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis
• Vector generalized linear and additive models. With an implementation in R
• The analysis of ordered categorical data: An overview and a survey of recent developments. (With discussion)
• Discrimination ability of prediction models for ordinal outcomes: Relationships between existing measures and a new measure
• Partial Proportional Odds Models for Ordinal Response Variables
• Using simulation studies to evaluate statistical methods
• Sample size considerations and predictive performance of multinomial logistic prediction models
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