A marginalized zero-inflated Poisson regression model with random effects

Published

Journal Article

© 2015 The Royal Statistical Society and John Wiley & Sons Ltd. Public health research often concerns relationships between exposures and correlated count outcomes. When counts exhibit more 0s than expected under Poisson sampling, the zero-inflated Poisson (ZIP) model with random effects may be used. However, the latent class formulation of the ZIP model can make marginal inference on the population sampled challenging. The paper presents a marginalized ZIP model with random effects to model directly the mean of the mixture distribution consisting of 'susceptible' individuals and excess 0s, providing straightforward inference for overall exposure effects. Simulations evaluate finite sample properties, and the new methods are applied to a motivational interviewing-based safer sex intervention trial, designed to reduce the number of unprotected sexual acts, to illustrate the new methods.

Full Text

Duke Authors

Cited Authors

  • Leann Long, D; Preisser, JS; Herring, AH; Golin, CE

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 64 / 5

Start / End Page

  • 815 - 830

Electronic International Standard Serial Number (EISSN)

  • 1467-9876

International Standard Serial Number (ISSN)

  • 0035-9254

Digital Object Identifier (DOI)

  • 10.1111/rssc.12104

Citation Source

  • Scopus