Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts.

ABSTRACT: Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-in

SUBMITTER: Preisser JS 

PROVIDER: S-EPMC5464970 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature