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Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R

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  • Parsons, Nick R.
  • Costa, Matthew L.
  • Achten, Juul
  • Stallard, Nigel

Abstract

The widely used proportional odds model is developed for correlated repeated ordinal score data, using a modified version of the generalized estimating equation (GEE) method for model fitting for a range of working correlation models. The algorithm developed estimates the correlation parameter, by minimizing the generalized variance of the regression parameters at each step of the fitting algorithm. Methods for parameter estimation are described for the widely used uniform and first-order autoregressive correlation models, for data potentially recorded at irregularly spaced time intervals. A full implementation of the algorithm (repolr) in the R statistical software package, that both tests the assumption of proportional odds and accommodates missing data, is described and applied to a clinical trial of post-operative treatment, after rupture of the Achilles tendon and a study of patient pain response after hip joint resurfacing.

Suggested Citation

  • Parsons, Nick R. & Costa, Matthew L. & Achten, Juul & Stallard, Nigel, 2009. "Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 632-641, January.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:3:p:632-641
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    1. Verwaeren, Jan & Waegeman, Willem & De Baets, Bernard, 2012. "Learning partial ordinal class memberships with kernel-based proportional odds models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 928-942.
    2. Lui Kung-Jong, 2016. "Testing Equality in Ordinal Data with Repeated Measurements: A Model-Free Approach," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-10, November.
    3. Saeide Sefidi & Mojtaba Ganjali & Taban Baghfalaki, 2022. "Analysis of ordinal and continuous longitudinal responses using pair copula construction," METRON, Springer;Sapienza Università di Roma, vol. 80(2), pages 255-280, August.
    4. Yuqi Tian & Bryan E. Shepherd & Chun Li & Donglin Zeng & Jonathan S. Schildcrout, 2023. "Analyzing clustered continuous response variables with ordinal regression models," Biometrics, The International Biometric Society, vol. 79(4), pages 3764-3777, December.
    5. Nooraee, Nazanin & Molenberghs, Geert & van den Heuvel, Edwin R., 2014. "GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 70-83.

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