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Assessing proportionality in the proportional odds model for ordinal logistic regression.
Abstract
The proportional odds model for ordinal logistic regression provides a useful extension of the binary logistic model to situations where the response variable takes on values in a set of ordered categories. The model may be represented by a series of logistic regressions for dependent binary variables, with common regression parameters reflecting the proportional odds assumption. Key to the valid application of the model is the assessment of the proportionality assumption. An approach is described arising from comparisons of the separate (correlated) fits to the binary logistic models underlying the overall model. Based on asymptotic distributional results, formal goodness-of-fit measures are constructed to supplement informal comparisons of the different fits. A number of proposals, including application of bootstrap simulation, are discussed and illustrated with a data example.
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“The proportional odds assumption of logistic regression is not violated (using a Brant test [26], P = 0.59).”
From Extending body space in immersive virtual reality: a very long arm illusion (2012) · cites this paper as [26]
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Metadata
- Authors
- Brant, R.
- Affiliations
- University of Calgary (CA)
- Corresponding author
- Brant, Rollin
- ORCID
- Brant, Rollin
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1990-12
- Volume
- 46
- Issue
- 4
- Pages
- 1171
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2016013569
- Cited by
- 1,864
- References
- 5
- Citations by year
- 2026: 213 · 2025: 135 · 2024: 135 · 2023: 161 · 2022: 168 · 2021: 145 · 2020: 141 · 2019: 99 · 2018: 76 · 2017: 69 · 2016: 68 · 2015: 57 · 2014: 64 · 2013: 51 · 2012: 45
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- proportional odds model, ordinal logistic regression, proportional odds assumption, goodness-of-fit tests, binary logistic regression, asymptotic distribution, bootstrap simulation, correlated binary responses
- MeSH terms
- Biometry, Humans, Regression Analysis, Tissue Donors, Models, Statistical, Liver Transplantation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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