### 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 language | English |
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Journal | Communications in Statistics: Simulation and Computation |

DOIs | |

Publication status | Published - 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

**Sample size calculations for hierarchical Poisson and zero-inflated Poisson regression models.** / Channouf, Nabil; Fredette, Marc; MacGibbon, Brenda.

Research output: Contribution to journal › Review article

}

TY - JOUR

T1 - Sample size calculations for hierarchical Poisson and zero-inflated Poisson regression models

AU - Channouf, Nabil

AU - Fredette, Marc

AU - MacGibbon, Brenda

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Hierarchical generalized linear models

KW - Information matrix

KW - Intraclass correlation

KW - Monte Carlo simulations

KW - Score function

KW - Wald test

UR - http://www.scopus.com/inward/record.url?scp=85062768881&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062768881&partnerID=8YFLogxK

U2 - 10.1080/03610918.2019.1577975

DO - 10.1080/03610918.2019.1577975

M3 - Review article

AN - SCOPUS:85062768881

JO - Communications in Statistics Part B: Simulation and Computation

JF - Communications in Statistics Part B: Simulation and Computation

SN - 0361-0918

ER -