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Distribution-free models for longitudinal count responses with overdispersion and structural zeros.

Overdispersion and structural zeros are two major manifestations of departure from the Poisson assumption when modeling count responses using Poisson log-linear regression. As noted in a large body of literature, ignoring such departures could yield bias and lead to wrong conclusions. Different approaches have been developed to tackle these two major problems. In this paper, we review available methods for dealing with overdispersion and structural zeros within a longitudinal data setting and propose a distribution-free modeling approach to address the limitations of these methods by utilizing a new class of functional response models. We illustrate our approach with both simulated and real study data.

Pubmed ID: 23239019


  • Yu Q
  • Chen R
  • Tang W
  • He H
  • Gallop R
  • Crits-Christoph P
  • Hu J
  • Tu XM


Statistics in medicine

Publication Data

June 30, 2013

Associated Grants

  • Agency: NIDA NIH HHS, Id: R21/R33 DA027521
  • Agency: NIDA NIH HHS, Id: R33 DA027521

Mesh Terms

  • Bias (Epidemiology)
  • Biostatistics
  • Humans
  • Linear Models
  • Longitudinal Studies
  • Models, Statistical
  • Monte Carlo Method
  • Poisson Distribution