epi.ssstrataestc: Sample size to estimate a continuous outcome using a...

In epiR: Tools for the Analysis of Epidemiological Data

Sample size to estimate a continuous outcome using a stratified random sampling design

Description

Sample size to estimate a continuous outcome using a stratified random sampling design.

Usage

epi.ssstrataestc(strata.n, strata.xbar, strata.sigma, epsilon, 
   error = "relative", nfractional = FALSE, conf.level = 0.95)

Arguments

strata.n	vector of integers, defining the number of individual listing units in each strata.
strata.xbar	vector of numbers, defining the expected means of the continuous variable to be estimated for each strata.
strata.sigma	vector of numbers, defining the expected standard deviation of the continous variable to be estimated for each strata.
epsilon	scalar number, the maximum difference between the estimate and the unknown population value expressed in absolute or relative terms.
error	character string. Options are absolute for absolute error and relative for relative error.
nfractional	logical, return fractional sample size.
conf.level	scalar number, the level of confidence in the computed result.

Value

A list containing the following:

strata.sample	the estimated sample size for each strata.
strata.total	the estimated total size.
strata.stats	mean the mean across all strata, sigma.bx the among-strata variance, sigma.wx the within-strata variance, and sigma.x the among-strata variance plus the within-strata variance, rel.var the within-strata variance divided by the square of the mean, and gamma the ratio of among-strata variance to within-strata variance.

Author(s)

Mark Stevenson (Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Australia).

Javier Sanchez (Atlantic Veterinary College, University of Prince Edward Island, Charlottetown Prince Edward Island, C1A 4P3, Canada).

References

Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 175 - 179.

Examples

## EXAMPLE 1 (from Levy and Lemeshow 1999, page 176 -- 178):
## We plan to take a sample of the members of a health maintenance 
## organisation (HMO) for purposes of estimating the average number
## of hospital episodes per person per year. The sample will be selected
## from membership lists according to age (under 45 years, 45 -- 64 years, 
## 65 years and over). The number of members in each strata are 600, 500,
## and 400 (respectively). Previous data estimates the mean number of 
## hospital episodes per year for each strata as 0.164, 0.166, and 0.236
## (respectively). The variance of these estimates are 0.245, 0.296, and 
## 0.436 (respectively). How many from each strata should be sampled to be
## 95% that the sample estimate of hospital episodes is within 20% of the 
## true value?

strata.n <- c(600,500,400)
strata.xbar <- c(0.164,0.166,0.236)
strata.sigma <- sqrt(c(0.245,0.296,0.436))
epi.ssstrataestc(strata.n, strata.xbar, strata.sigma, epsilon = 0.20, 
   error = "relative", nfractional = FALSE, conf.level = 0.95)

## The number allocated to the under 45 years, 45 -- 64 years, and 65 years 
## and over stratums should be 224, 187, and 150 (a total of 561). These 
## results differ from the worked example provided in Levy and Lemeshow where 
## certainty is set to approximately 99%.

epiR documentation built on Sept. 30, 2024, 9:16 a.m.