Quality Improvement Recent Advances in Categorical Data Analysis Maura Stokes, SUGI Proceedings, 1999. The last fifteen years have brought many changes to the practice of categorical data analysis. This paper reviews some of the major changes and shifts of emphasis and discusses several examples using SAS software procedures. Topics include the use of exact methods, generalized estimating equations, conditional logistic regression, and current uses of weighted least squares modeling.
Applications provide illustration for many topics.
This paper describes software currently available in the SAS System and indicates the areas in which new software should be available in the next few years. The references include some pertinent methodology and review papers. When asymptotic assumptions are not appropriate due to sparse data or small sample sizes, exact methods can provide a way to produce appropriate p-values for tests, valid confidence limits for odds ratios, and parameter estimates and standard errors in logistic regression. Weighted least squares, a very important strategy in the 70s and 80s for general statistical modeling, still provides a useful way of modeling functions of interest such as rank measures of association and incidence densities. What follows are descriptions of these newer strategies and illustrations of their application.