An evaluation of methods for the stratified analysis of clustered binary data in community intervention trials

James X. Song, Chul W. Ahn

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

A simulation study is conducted in a community intervention setting. Several methods of stratified analysis of clustered binary data are compared in terms of empirical significance and empirical power levels. They are the Mantel-Haenszel test statistic (χMH 2), the adjusted Mantel-Haenszel test statistic of Donald-Donner (χDD 2), Rao-Scott (χRSN 2 and χRSP 2), and Zhang-Boos (χZBN 2 and χZBP 2), Wald (χW 2), robust Wald (χRW 2), score (χS 2), robust score (χRS 2), and the test statistic based on generalized linear mixed model (GLMM) (χGLMM 2. When ρ≠0, χMH 2 has inflated type I error, and it should not be used when observations are correlated. The results also warn of the use of χRSN 2 and χRW 2 due to their poor performance in terms of empirical significance level. χZBP 2 and χGLMM 2 have better empirical significance levels as compared to other statistics; however, χZBP 2 tends to have lower empirical powers than other statistics when the number of clusters (N) is less than 24. χRSP 2 provides the highest empirical powers when ρ≥0.1 and N ≤ 12. When ρ≤0.01, we recommend the use of χRS 2 and χGLMM 2 since they have better overall performance in terms of empirical significance levels and empirical power levels.

Original languageEnglish (US)
Pages (from-to)2205-2216
Number of pages12
JournalStatistics in Medicine
Volume22
Issue number13
DOIs
Publication statusPublished - Jul 15 2003

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Keywords

  • Community intervention
  • Correlated binary data
  • Simulation
  • Stratified analysis

ASJC Scopus subject areas

  • Epidemiology

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