epiR (version 1.0-15)

epi.sssimpleestb: Sample size to estimate a binary outcome using simple random sampling

Description

Sample size to estimate a binary outcome using simple random sampling.

Usage

epi.sssimpleestb(N = 1E+06, Py, epsilon.r, conf.level = 0.95)

Arguments

N

scalar integer, the total number of individual listing units in the population.

Py

scalar number, an estimate of the population proportion to be estimated.

epsilon.r

scalar number, the maximum relative difference between the estimate and the unknown population value.

conf.level

scalar number, the level of confidence in the computed result.

Value

Returns an integer defining the required sample size.

References

Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 70 - 75.

Scheaffer RL, Mendenhall W, Lyman Ott R (1996). Elementary Survey Sampling. Duxbury Press, New York, pp. 95.

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.

Examples

# NOT RUN {
## EXAMPLE 1:
## We want to estimate the seroprevalence of Brucella abortus in a population 
## of cattle. An estimate of the unknown prevalence of B. abortus in this 
## population is 0.15. We would like to be 95% certain that our estimate is 
## within 20% of the true proportion of the population that is seropositive 
## to B. abortus. Calculate the required sample size.

n.crude <- epi.sssimpleestb(N = 1E+06, Py = 0.15, epsilon.r = 0.20,
   conf.level = 0.95)
n.crude

## A total of 544 cattle need to be sampled to meet the survey requirements.

## EXAMPLE 1 (continued):
## Being seropositive to brucellosis is likely to cluster within herds.
## Otte and Gumm (1997) cite the intraclass correlation coefficient (rho) of
## Brucella abortus to be in the order of 0.09. Adjust the sample size
## estimate to account for clustering at the herd level. Assume that, on
## average, 20 animals will be sampled per herd:

## Let D equal the design effect and nbar equal the average number of 
## individuals per cluster:

## rho = (D - 1) / (nbar - 1)

## Solving for D:
## D <- rho * (nbar - 1) + 1

rho <- 0.09; nbar <- 20
D <- rho * (nbar - 1) + 1

n.adj <- ceiling(n.crude * D)
n.adj

## After accounting for the presence of clustering at the herd level we
## estimate that a total of 1475 cattle need to be sampled to meet
## the requirements of the survey.

# }