Bayesian Modeling for Various Outcomes in SAS

Nonnormality in data and small sample size are often challenges in public health data analysis. The Bayesian approach does not rely on asymptotic theory, therefore, works very well for various sources of non-normality. More important, with Bayesian approach, small sample inference proceeds in the same manner as if one had a large sample. In addition, using the distributions of the posterior parameter estimates provided by Bayesian analyses, we will be able to estimate the probability that a regression coefficient has a specific value of interest (e.g., the effect of an intervention is positive). SAS 9.2 integrates a BAYES statement in three procedures (GENMOD, LIFEREG, and PHREG) to produce Bayesian modeling and inference capability for various outcome measures, such as continuous, binary, ordinal, nominal, count data, and event history data. This study demonstrates how to use SAS for Bayesian regression model, logit model, negative binomial model, and Cox hazard model using simulated data.