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Regression analysis of correlated ordinal data using orthogonalized residuals
Author(s) -
Perin J.,
Preisser J. S.,
Phillips C.,
Qaqish B.
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12210
Subject(s) - ordinal regression , ordinal data , statistics , data mining , regression analysis , ordinal optimization , regression , computer science , mathematics , econometrics
Summary Semi‐parametric regression models for the joint estimation of marginal mean and within‐cluster pairwise association parameters are used in a variety of settings for population‐averaged modeling of multivariate categorical outcomes. Recently, a formulation of alternating logistic regressions based on orthogonalized, marginal residuals has been introduced for correlated binary data. Unlike the original procedure based on conditional residuals, its covariance estimator is invariant to the ordering of observations within clusters. In this article, the orthogonalized residuals method is extended to model correlated ordinal data with a global odds ratio, and shown in a simulation study to be more efficient and less biased with regards to estimating within‐cluster association parameters than an existing extension to ordinal data of alternating logistic regressions based on conditional residuals. Orthogonalized residuals are used to estimate a model for three correlated ordinal outcomes measured repeatedly in a longitudinal clinical trial of an intervention to improve recovery of patients' perception of altered sensation following jaw surgery.