Models for analyzing zero-inflated and overdispersed count data: an application to cigarette and marijuana use.

ABSTRACT: This paper describes different methods for analyzing counts and illustrates their use on cigarette and marijuana smoking data. The Poisson, zero-inflated Poisson (ZIP), hurdle Poisson (HUP), negative binomial (NB), zero-inflated negative binomial (ZINB) and hurdle negative binomial (HUNB) regression models are considered. The different approaches are evaluated in terms of the ability to take into account zero-inflation (extra zeroes) and overdispersion (variance larger than expected) in count outcomes, with emphasis placed on model fit, interpretation, and choosing an appropriate model given the nature of the data. The illustrative data example focuses on cigarette and marijuana smoking reports from a study on smoking habits among youth e-cigarette users with gender, age, and e-cigarette u

SUBMITTER: Pittman B 

PROVIDER: S-EPMC7364829 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature