Abstract
The proportional odds model (POM) is the most popular logistic regression model for
analyzing ordinal response variables. However, violation of the main model assumption
can lead to invalid results. This is demonstrated by application of this method to
data of a study investigating the effect of smoking on diabetic retinopathy. Since
the proportional odds assumption is not fulfilled, separate binary logistic regression
models are used for dichotomized response variables based upon cumulative probabilities.
This approach is compared with polytomous logistic regression and the partial proportional
odds model. The separate binary logistic regression approach is slightly less efficient
than a joint model for the ordinal response. However, model building, investigating
goodness-of-fit, and interpretation of the results is much easier for binary responses.
The careful application of separate binary logistic regressions represents a simple
and adequate tool to analyze ordinal data with non-proportional odds.
Keywords
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Article info
Footnotes
☆Accepted for publication on 5 May 1998.
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Copyright
© 1998 Elsevier Science Inc. Published by Elsevier Inc. All rights reserved.
