repolr

R package repolr: Repeated Measures Proportional Odds Logistic Regression. Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R. 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.


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Nikoloulopoulos, Aristidis K.: Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response (2020)
  2. da Silva, José L. P.; Colosimo, Enrico A.; Demarqui, Fábio N.: A general GEE framework for the analysis of longitudinal ordinal missing data and related issues (2019)
  3. Inan, G.; Yucel, R.: Joint GEEs for multivariate correlated data with incomplete binary outcomes (2017)
  4. da Silva, Nívea B.; Amorim, Leila D. A. F.; Colosimo, Enrico A.; Heller, Leo: Modeling ordinal longitudinal outcomes: an applied perspective of marginal and conditional approaches (2016)
  5. Anestis Touloumis: R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses (2014) arXiv
  6. Nooraee, Nazanin; Molenberghs, Geert; van den Heuvel, Edwin R.: GEE for longitudinal ordinal data: comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN (2014)
  7. Verwaeren, Jan; Waegeman, Willem; De Baets, Bernard: Learning partial ordinal class memberships with kernel-based proportional odds models (2012)
  8. Edler, Lutz; Lee, Jae Won; Mittlböck, Martina; Niland, Joyce; Victor, Norbert: Editorial: Computational statistics within clinical research (2009)
  9. Parsons, Nick R.; Costa, Matthew L.; Achten, Juul; Stallard, Nigel: Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R (2009)
  10. Parsons, N. R.; Gilmour, S. G.; Edmondson, R. N.: Analysis of robust design experiments with time-dependent ordinal response characteristics: a quality improvement study from the horticulture industry (2009)