Sample size calculations for hierarchical Poisson and zero-inflated Poisson regression models

Nabil Channouf, Marc Fredette, Brenda MacGibbon

Research output: Contribution to journalReview article

Abstract

In biomedical research there is a growing interest in the use of hierarchical Poisson regression models. Although sample size calculations for testing parameters in a Poisson regression model with prespecified power and size have been previously done, very little attention has been paid to this problem for the hierarchical model. We propose to use Monte Carlo simulations to calculate the sample size necessary to perform the Wald tests when the number of clusters is fixed in advance, but the cluster size is variable. The effect of the number of clusters and the covariance structure of the fixed effects is also studied. The method and the simulation study are also extended to the case of the hierarchical zero-inflated Poisson regression model in order to obtain analogous results there. The method is also illustrated on an interesting real dataset.

Original languageEnglish
JournalCommunications in Statistics: Simulation and Computation
DOIs
Publication statusPublished - Jan 1 2019

    Fingerprint

Keywords

  • Hierarchical generalized linear models
  • Information matrix
  • Intraclass correlation
  • Monte Carlo simulations
  • Score function
  • Wald test

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation

Cite this