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Bias correction for the proportional odds logistic regression model with application to a study of surgical complications.
- Source :
- Journal of the Royal Statistical Society: Series C (Applied Statistics); Mar2013, Vol. 62 Issue 2, p233-250, 18p, 7 Charts
- Publication Year :
- 2013
-
Abstract
- . The proportional odds logistic regression model is widely used for relating an ordinal outcome to a set of covariates. When the number of outcome categories is relatively large, the sample size is relatively small and/or certain outcome categories are rare, maximum likelihood can yield biased estimates of the regression parameters. Firth and Kosmidis proposed a procedure to remove the leading term in the asymptotic bias of the maximum likelihood estimator. Their approach is most easily implemented for univariate outcomes. We derive a bias correction that exploits the proportionality between Poisson and multinomial likelihoods for multinomial regression models. Specifically, we describe a bias correction for the proportional odds logistic regression model, based on the likelihood from a collection of independent Poisson random variables whose means are constrained to sum to 1, that is straightforward to implement. The method proposed is motivated by a study of predictors of post-operative complications in patients undergoing colon or rectal surgery. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00359254
- Volume :
- 62
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of the Royal Statistical Society: Series C (Applied Statistics)
- Publication Type :
- Academic Journal
- Accession number :
- 85674846
- Full Text :
- https://doi.org/10.1111/j.1467-9876.2012.01057.x