Fast estimation algorithm for likelihood-based analysis of repeated categorical responses
Computational Statistics & Data Analysis
Editorial: Computational statistics within clinical research
Computational Statistics & Data Analysis
Learning partial ordinal class memberships with kernel-based proportional odds models
Computational Statistics & Data Analysis
Editorial: Second Issue for Computational Statistics for Clinical Research
Computational Statistics & Data Analysis
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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.