A SAS macro for stepwise correlated binary regression
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Updated: Sep 30, 2026

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1University of Pennsylvania Cancer Center, Philadelphia 19104-6021, USA.
Abstract:
Several regression methods have been proposed for the analysis of correlated binary data, but none deals with the selection of covariates when there exist a large number of potentially relevant covariates. We present a SAS macro based on a stepwise selection procedure for the analysis of correlated binary data. Using regression methods based on generalized estimating equations originally proposed by Liang and Zeger and extended by Prentice, we describe a score test for forward selection, a Wald's test for backward elimination, and a test for model adequacy based on generalized scores. The methodology and the accompanying computer macro program written in SAS IML are illustrated with data from a prospective study of functional decline in the activities of daily living in a group of elderly patients.
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