epiR (version 0.9-82)

epi.simplesize: Sample size under simple random sampling

Description

Estimates the required sample size under under simple random sampling.

Usage

epi.simplesize(N = 1E+06, Vsq, Py, epsilon.r, method = "mean", 
   conf.level = 0.95)

Arguments

N

scalar, representing the population size.

Vsq

scalar, if method is total or mean this is the relative variance of the variable to be estimated (i.e. var/mean^2).

Py

scalar, if method is proportion this is an estimate of the unknown population proportion.

epsilon.r

the maximum relative difference between our estimate and the unknown population value.

method

a character string indicating the method to be used. Options are total, mean, or proportion.

conf.level

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

Value

Returns an integer defining the size of the sample is required.

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

## EXAMPLE 1:
## A city contains 20 neighbourhood health clinics and it is desired to take a 
## sample of clinics to estimate the total number of persons from all these 
## clinics who have been given, during the past 12 month period, prescriptions 
## for a recently approved antidepressant. If we assume that the average number 
## of people seen at these clinics is 1500 per year with the standard deviation 
## equal to 300, and that approximately 5% of patients (regardless of clinic) 
## are given this drug, how many clinics need to be sampled to yield an estimate 
## that is within 20% of the true population value?

pmean <- 1500 * 0.05; pvar <- (300 * 0.05)^2
epi.simplesize(N = 20, Vsq = (pvar / pmean^2), Py = NA, epsilon.r = 0.20, 
   method = "total", conf.level = 0.95)

## Three clinics need to be sampled to meet the survey requirements. 

## EXAMPLE 2:
## We want to estimate the mean bodyweight of deer on a farm. There are 278
## animals present. We anticipate the mean body weight to be around 200 kg
## and the standard deviation of body weight to be 30 kg. We would like to
## be 95% certain that our estimate is within 10 kg of the true mean. How
## many deer should be sampled?

epi.simplesize(N = 278, Vsq = 30^2 / 200^2, Py = NA, epsilon.r = 10/200, 
   method = "mean", conf.level = 0.95)

## A total of 31 deer need to be sampled to meet the survey requirements.

## EXAMPLE 3:
## 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.simplesize(N = 1E+06, Vsq = NA, Py = 0.15, epsilon.r = 0.20,
   method = "proportion", conf.level = 0.95)
n.crude

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

## EXAMPLE 3 (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.