epi.clustersize {epiR} | R Documentation |
Estimates the number of clusters to be sampled using a cluster-sample design.
epi.clustersize(p, b, rho, epsilon, conf.level = 0.95)
p |
the estimated prevalence of disease in the population. |
b |
the number of units to be sampled per cluster. |
rho |
the intra-cluster correlation, a measure of the variation between clusters compared with the variation within clusters. |
epsilon |
scalar, the acceptable absolute error. |
conf.level |
scalar, defining the level of confidence in the computed result. |
A list containing the following:
clusters |
the estimated number of clusters to be sampled. |
units |
the total number of units to be sampled. |
design |
the design effect. |
The intra-cluster correlation (rho
) will be higher for those situations where the between-cluster variation is greater than within-cluster variation. The design effect is dependent on rho
and b
(the number of units sampled per cluster): rho = (D - 1) / (b - 1)
. Design effects of 2, 4, and 7 can be used to estimate rho
when intra-cluster correlation is low, medium, and high (respectively). A design effect of 7.5 should be used when the intra-cluster correlation is unknown.
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.
Bennett S, Woods T, Liyanage WM, Smith DL (1991). A simplified general method for cluster-sample surveys of health in developing countries. Raport trimestriel de statistiques sanitaires modiales 44: 98 - 106.
## 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 relatively high. ## 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 <- 7 rho <- (D - 1) / (b - 1) epi.clustersize(p = 0.10, b = 50, rho = rho, epsilon = 0.10, conf.level = 0.95) ## We need to sample 485 herds (24250 samples in total).