Implementation of a Simple Method for the Analysis of Over - Dispersed Counts and Proportions

Over-dispersed binary and count data occur frequently in many fields of application. Examples include occurrence of cavity in one or more teeth, and development of tumors In one or more animals of a litter. Methods of statistical analysis that ignore over-dispersion underestimate standard errors. Consequently, coverage proportions of confidence intervals and significance levels of tests are distorted. To analyze the over-dispersed data, one could postulate a specific statistical model and use maximum likelihood methods for the estimation of parameters. However, it may be preferable to employ an approach that does not rely on mode6ng because the true model is hard to know with confidence. Rao and Scott (1992) and Scott and Rao ( 19951 proposed a simple method for analyzing correlated binary and count data exhibiting over-dispersion relative to a binomial and Poisson model. This method assumes no specific models for over-dispersion. Furthermore, it can be implemented easily using the standard procedures in SAS/ST A T• software designed to handle Independent binary and Poisson counts after a small amount of preprocessing.