Comparing Bayesian regression and classic zeroinflated negative binomial on size estimation of people who use alcohol

Background Aim: Nowadays, we have some data in different sciences which number of zeros is more than expected, such data are called zero-inflated which can be modeled by regressions for count data. Many researches have been conducted in the field of classical method on count data. Most of Bayesian analysis which is conducted for these data used zero-inflated Poisson regression. Therefore, the main purpose of this research is comparison of Bayesian and classic approaches in regression of zero-inflated negative binomial (NB) on data for determining the size estimation of people who have used alcohol more than once in last year. Methods Materials: This research had been in two provinces of Fars and Kerman in 2011, a sample size of each province was formed proportional to people of that province, and totally the calculated sample size was 700. Zero-inflated NB regression was fitted to the data in two Bayesian and classical methods, and then two methods have been compared. Results of Bayesian method were extracted in OpenBUGS software and through related codes in R and results of classical method were extracted in R software too. Results: After fitting classical method, variables of province, gender, age groups, and education had been effective on identifying number of alcoholics, but in Bayesian method, three variables of gender, age groups, and education have become significant. In this research, it was specified that obtained probability intervals from Bayesian method are much Widder than classical method. Conclusion: Results of this research indicate that Bayesian method has better function than the classic