The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
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]
Citations
Cites
A REVIEW OF GOODNESS OF FIT STATISTICS FOR USE IN THE DEVELOPMENT OF LOGISTIC REGRESSION MODELS1
LEMESHOW, S., & HOSMER, D. W. (1982) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a113284 · Cited by 1,947
Regression and ordered categorical variables
Anderson, J. A. (1984) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1984.tb01270.x · Cited by 574
ORDINAL REGRESSION MODELS FOR EPIDEMIOLOGIC DATA
Armstrong, B., & SLOAN, M. (1989) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a115109 · Cited by 323
A note on a goodness-of-fit test for the logistic regression model
Tsiatis, A. A. (1980) · Biometrika · doi:10.1093/biomet/67.1.250 · Cited by 171
A comparison of regression models for ordinal data in an analysis of transplanted‐kidney function
Greenwood, C., & Farewell, V. T. (1988) · Canadian Journal of Statistics · doi:10.2307/3314931 · Cited by 26
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Understanding and interpreting generalized ordered logit models
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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
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