Comparing methods for risk prediction of multicategory outcomes: dichotomized logistic regression vs. multinomial logit regression

Lei Li , Matthew A. Rysavy , Georgiy Bobashev , Abhik Das ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1186/s12874-024-02389-x

Estimating multiple logistic regression models of dichotomized outcomes may result in poorly calibrated predictions for an outcome with multiple ordinal categories. Multinomial continuation-ratio logit regression produces better calibrated predictions, constrains the sum of predicted probabilities to 100%, and has the advantages of simplicity in model interpretation, flexibility to include outcome category-specific predictors and random-effect terms for patient heterogeneity by hospital. It also accounts for mutual dependence among multiple categories and accommodates competing risks.

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Multinomial logistic regression Logistic regression Statistics Regression Logit Logistic model tree Regression analysis Ordered logit Econometrics Medicine Mathematics

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Type: article
Language: en
Landing Page: https://doi.org/10.1186/s12874-024-02389-x
OA Status: gold
Cited By: 5
References: 21
Related Works: 10
OpenAlex ID: https://openalex.org/W4403948869

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