title: A SAS macro for stepwise correlated binary regression.
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.
authors.affiliation: University of Pennsylvania Cancer Center, Philadelphia 19104-6021, USA.
authors.forename: I F
authors.initials: IF
authors.lastname: Nuamah
authors.forename: Y
authors.initials: Y
authors.lastname: Qu
authors.forename: S B
authors.initials: SB
authors.lastname: Amini
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D000369:Aged, 80 and over; D001699:Biometry / Q000379:methods; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D011446:Prospective Studies; D012044:Regression Analysis; D012984:Software