Estimates the number of clusters to be sampled using a cluster-sample design.
epi.clustersize(p, b, rho, epsilon.r, conf.level = 0.95)the estimated prevalence of the outcome in the population.
the number of units sampled per cluster.
the intra-cluster correlation, a measure of the variation between clusters compared with the variation within clusters.
scalar, the acceptable relative error.
scalar, defining the level of confidence in the computed result.
A list containing the following:
the estimated number of clusters to be sampled.
the total number of units to be sampled.
the design effect.
Bennett S, Woods T, Liyanage WM, Smith DL (1991). A simplified general method for cluster-sample surveys of health in developing countries. World Health Statistics Quarterly 44: 98 - 106.
Otte J, Gumm I (1997). Intra-cluster correlation coefficients of 20 infections calculated from the results of cluster-sample surveys. Preventive Veterinary Medicine 31: 147 - 150.
## EXAMPLE 1:
## The expected prevalence of disease in a population of cattle is 0.10.
## We wish to conduct a survey, sampling 50 animals per farm. No data
## are available to provide an estimate of rho, though we suspect
## the intra-cluster correlation for this disease to be moderate.
## We wish to be 95% certain of being within 10% of the true population
## prevalence of disease. How many herds should be sampled?
p <- 0.10; b <- 50; D <- 4
rho <- (D - 1) / (b - 1)
epi.clustersize(p = 0.10, b = 50, rho = rho, epsilon.r = 0.10,
conf.level = 0.95)
## We need to sample 278 herds (13900 samples in total).
## EXAMPLE 2 (from Bennett et al. 1991):
## A cross-sectional study is to be carried out to determine the prevalence
## of a given disease in a population using a two-stage cluster design. We
## estimate prevalence to be 0.20 and we expect rho to be in the order of 0.02.
## We want to take sufficient samples to be 95% certain that our estimate of
## prevalence is within 5% of the true population value (that is, a relative
## error of 0.05 / 0.20 = 0.25). Assuming 20 responses from each cluster,
## how many clusters do we need to be sample?
epi.clustersize(p = 0.20, b = 20, rho = 0.02, epsilon.r = 0.25,
conf.level = 0.95)
## We need to sample 18 clusters (360 samples in total).
Run the code above in your browser using DataLab