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A Comparison of Regression Models for the Analysis of Ordered Categorical Data

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  • Werner Holtbrügge
  • Martin Schumacher

Abstract

In a clinical trial on the treatment of lung cancer two chemotherapeutic strategies were compared with respect to tumour response, which was assessed under four ordinal categories. Two regression models for the analysis of ordered categorical data, the proportional odds model and the stereotype model, are used to analyse the data. Differences between the models with respect to estimation and significance tests are further investigated using simulation studies. The results indicate that estimates are less biased in the proportional odds model, but in the stereotype model an improvement may be achieved if adjacent categories are amalgamated. Similar behaviour is observed with respect to tests concerning regression coefficients which are of primary interest in most clinical trials. While in the proportional odds model no relevant deviation from the nominal level of significance is observed, comparable results are obtained in the stereotype model only after amalgamation of categories.

Suggested Citation

  • Werner Holtbrügge & Martin Schumacher, 1991. "A Comparison of Regression Models for the Analysis of Ordered Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(2), pages 249-259, June.
    • Handle: RePEc:bla:jorssc:v:40:y:1991:i:2:p:249-259
    DOI: 10.2307/2347590
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    Cited by:

    1. Garcia, Alexis Arthur B. & Rejesus, Roderick M. & Genio, Emmanuel L., 2008. "Factors Influencing Artisanal Fisherfolks' Level of Support for Fishery Regulations: An Approach Using Alternative Ordered Logit Models," 2008 Annual Meeting, February 2-6, 2008, Dallas, Texas 6791, Southern Agricultural Economics Association.
    2. Daniel Fernández & Louise McMillan & Richard Arnold & Martin Spiess & Ivy Liu, 2022. "Goodness-of-Fit and Generalized Estimating Equation Methods for Ordinal Responses Based on the Stereotype Model," Stats, MDPI, vol. 5(2), pages 1-14, June.
    3. Kuss, Oliver, 2006. "On the estimation of the stereotype regression model," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 1877-1890, April.
    4. K. Vaitheeswaran & M. Subbiah & R. Ramakrishnan & T. Kannan, 2016. "A comparison of ordinal logistic regression models using Classical and Bayesian approaches in an analysis of factors associated with diabetic retinopathy," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2254-2260, September.

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