hayes.power.poisson {clusterPower}R Documentation

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

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.

Value

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

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

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))


[Package clusterPower version 0.6.111 Index]