Abstract
In this article, we describe a new software for modeling correlated binary data based on orthogonalized residuals (Zink and Qaqish, 2009), a recently developed estimating equations approach that includes, as a special case, alternating logistic regressions (Carey et al., 1993). The software is flexible with respect to fitting in that the user can choose estimating equations for the association model based on alternating logistic regressions or orthogonalized residuals, the latter choice providing a non-diagonal working covariance matrix for second moment parameters providing potentially greater efficiency. Regression diagnostics based on this method are also implemented in the software. The mathematical details of the procedure are briefly reviewed and the software is applied to medical data sets.
Disciplines
Biostatistics | Categorical Data Analysis | Longitudinal Data Analysis and Time Series | Numerical Analysis and Computation
Suggested Citation
By, Kunthel; Qaqish, Bahjat F.; Preisser, John S.; Perin, Jamie; and Zink, Richard C., "ORTH: R and SAS Software for Regression Models of Correlated Binary Data Based on Orthogonalized Residuals and Alternating Logistic Regressions" (July 2011). The University of North Carolina at Chapel Hill Department of Biostatistics Technical Report Series. Working Paper 22.
http://biostats.bepress.com/uncbiostat/art22
Included in
Biostatistics Commons, Categorical Data Analysis Commons, Longitudinal Data Analysis and Time Series Commons, Numerical Analysis and Computation Commons