Sample size calculations for clustered binary data.

Published

Journal Article

In this paper we propose a sample size calculation method for testing on a binomial proportion when binary observations are dependent within clusters. In estimating the binomial proportion in clustered binary data, two weighting systems have been popular: equal weights to clusters and equal weights to units within clusters. When the number of units varies cluster by cluster, performance of these two weighting systems depends on the extent of correlation among units within each cluster. In addition to them, we will also use an optimal weighting method that minimizes the variance of the estimator. A sample size formula is derived for each of the estimators with different weighting schemes. We apply these methods to the sample size calculation for the sensitivity of a periodontal diagnostic test. Simulation studies are conducted to evaluate a finite sample performance of the three estimators. We also assess the influence of misspecified input parameter values on the calculated sample size. The optimal estimator requires equal or smaller sample sizes and is more robust to the misspecification of an input parameter than those assigning equal weights to units or clusters.

Full Text

Duke Authors

Cited Authors

  • Jung, SH; Kang, SH; Ahn, C

Published Date

  • July 15, 2001

Published In

Volume / Issue

  • 20 / 13

Start / End Page

  • 1971 - 1982

PubMed ID

  • 11427953

Pubmed Central ID

  • 11427953

International Standard Serial Number (ISSN)

  • 0277-6715

Digital Object Identifier (DOI)

  • 10.1002/sim.846

Language

  • eng

Conference Location

  • England