Abstract
Objective
The objective of the paper was to design a computer algorithm to calculate sample
sizes for estimating proportions incorporating clustered sampling units using a beta-binomial
model when information concerning the intraclass correlation is not available.
Study Design and Setting
A computer algorithm was written in FORTRAN and evaluated for a hypothetical sample
size situation.
Results
The developed algorithm was able to incorporate clustering in estimated sample sizes
through the specification of a beta distribution to account for within-cluster correlation.
In a hypothetical example, the usual normal approximation method for estimation of
a proportion ignoring the clustered sampling design resulted in a calculated sample
size of 107, whereas the developed algorithm suggested that 208 sampling units would
be necessary.
Conclusion
It is important to incorporate cluster adjustment in sample size calculations when
designing epidemiologic studies for estimation of disease burden and other population
proportions in the situation of correlated data even when information concerning the
intraclass correlation is not available. Beta-binomial models can be used to account
for clustering, and design effects can be estimated by generating beta distributions
that encompass within-cluster correlation.
Keywords
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Article info
Publication history
Published online: September 29, 2006
Accepted:
June 2,
2006
Identification
Copyright
© 2007 Elsevier Inc. Published by Elsevier Inc. All rights reserved.