A Marginalized Zero-Inflated Poisson Regression Model with Overall Exposure Effects

• Activity Number: 483
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): D. Leann Long*+ and John Preisser and Amy Herring and Carol Golin
• Companies: West Virginia University and The University of North Carolina and UNC CH and Univeristy of North Carolina, Chapel Hill
• Keywords: incidence ; marginalized models ; unprotected intercourse ; zero-inflation

Abstract

The zero-inflated Poisson (ZIP) regression model is often employed in public health research to examine the relationships between exposures of interest and a count outcome exhibiting many zeros, in excess of the amount expected under Poisson sampling. The regression coefficients of the ZIP model have latent class interpretations that are not well suited for inference targeted at overall exposure effects, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. We develop a marginalized ZIP model approach for independent responses to model the population mean count directly, allowing straightforward inference for overall exposure effects and easy accommodation of offsets representing individuals' risk times and empirical robust variance estimation for overall log incidence density ratios. Through simulation studies, the performance of maximum likelihood estimation of the marginalized ZIP model is assessed and compared to existing post-hoc methods for the estimation of overall effects in the traditional ZIP model framework. The marginalized ZIP model is applied to a recent study of a safer sex counseling intervention.