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 |
|---|---|
| Journal | Communications in Statistics: Simulation and Computation |
| DOIs | |
| Publication status | Published - Jan 1 2019 |
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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.
In: Communications in Statistics: Simulation and Computation, 01.01.2019.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 -