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Orthogonalized Residuals for Estimation of Marginally Specified Association Parameters in Multivariate Binary Data


Bahjat F. Qaqish, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.


Abstract.  This paper focuses on marginal regression models for correlated binary responses when estimation of the association structure is of primary interest. A new estimating function approach based on orthogonalized residuals is proposed. A special case of the proposed procedure allows a new representation of the alternating logistic regressions method through marginal residuals. The connections between second-order generalized estimating equations, alternating logistic regressions, pseudo-likelihood and other methods are explored. Efficiency comparisons are presented, with emphasis on variable cluster size and on the role of higher-order assumptions. The new method is illustrated with an analysis of data on impaired pulmonary function.