title: 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 rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias.
authors.affiliation: Department of Epidemiology & Biostatistics, School of Public Health, Drexel University, Philadelphia, PA, USA Tel.: +267-359-6217 lpp22@drexel.edu.
authors.forename: Loni Philip
authors.initials: LP
authors.lastname: Tabb
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
authors.forename: Eric J Tchetgen
authors.initials: EJ
authors.lastname: Tchetgen
authors.affiliation: Department of Community Health, Brown University, Boston, MA, USA.
authors.forename: Greg A
authors.initials: GA
authors.lastname: Wellenius
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
authors.forename: Brent A
authors.initials: BA
authors.lastname: Coull
keywords: Longitudinal data; Marginal regression; Negative binomial; Poisson; Zero inflation