Marginalized zero-altered models for longitudinal count data.

ABSTRACT: Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative

SUBMITTER: Tabb LP 

PROVIDER: S-EPMC5111636 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature