clusterPower (version 0.6.111)

hayes.power.poisson: An implementation of power calculations for cluster-randomized study based on the coefficient of variation.

Description

This function calculates the power for a specified cluster-randomized study based on the methods described by Hayes et al (1999).

Usage

hayes.power.poisson(n.clusters, period.effect, btw.clust.var, at.risk.params,
  cluster.size, effect.size, alpha = 0.05)

Arguments

n.clusters

number of clusters

period.effect

period effect, on the link scale. See details.

btw.clust.var

the between-cluster variance

at.risk.params

the expected at-risk time per individual in the study

cluster.size

the number of individuals in each cluster

effect.size

effect size, specified on the GLM link scale

alpha

desired type I error rate

Value

A numeric vector of length 1, containing the estimated power for the given study specifications.

Details

Calculates, for a specified study design, the power of that study to detect the specified effect size. The model is specified as a Poisson log-linear random effects model (period.effect and btw.clust.var are parameters from the model specified in Reich et al (2012)). Based on this model specification, the coefficient of varation between cluster-level outcomes is calculated using conditional expectation (see mixed.eff.params()) and then the formula from Hayes and Bennett (1999) is implemented.

References

Reich NG et al. PLoS ONE. Empirical Power and Sample Size Calculations for Cluster-Randomized and Cluster-Randomized Crossover Studies. 2012. http://ow.ly/fEn39

Hayes RJ and Bennett S. Int J Epi. Simple sample size calculation for cluster-randomized trials. 1999. http://www.ncbi.nlm.nih.gov/pubmed/10342698

See Also

mixed.eff.params

Examples

# NOT RUN {
hayes.power.poisson(n.clusters=36, period.effect=log(.015), btw.clust.var=0,
  at.risk.params=20, cluster.size=20, effect.size=log(.7))

# }