Adaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes in SAS

Abstract

This chapter describes how to use the genreg macro for adaptive logistic regression modeling of multivariate dichotomous and polytomous outcomes as described in Chap. 10 as well as its generated output. Example are provided for modeling means and dispersions for post-baseline respiratory status in terms of time, baseline respiratory status, and being on active treatment as opposed to taking a placebo. The analyses consider both dichotomous respiratory status, categorized as poor or good, and polytomous respiratory status, categorized as poor or good or excellent. Ordinal regression and multinomial regression models are considered for polytomous respiratory status. Examples are presented for transition modeling and GEE-based marginal modeling of dichotomous and polytomous respiratory status. An example residual analysis is presented for dichotomized respiratory status.