Intraclass correlation coefficients and bootstrap methods of hierarchical binary outcomes

Shiquan Ren, Shuqin Yang, Shenghan Lai

Research output: Contribution to journalArticlepeer-review

Abstract

Intraclass correlation coefficients are designed to assess consistency or conformity between two or more quantitative measurements. When multistage cluster sampling is implemented, no methods are readily available to estimate intraclass correlations of binomial-distributed outcomes within a cluster. Because statistical distribution of the intraclass correlation coefficients could be complicated or unspecified, we propose using a bootstrap method to estimate the standard error and confidence interval within the framework of a multilevel generalized linear model. We compared the results derived from a parametric bootstrap method with those from a non-parametric bootstrap method and found that the non-parametric method is more robust. For non-parametric bootstrap sampling, we showed that the effectiveness of sampling on the highest level is greater than that on lower levels; to illustrate the effectiveness, we analyse survey data in China and do simulation studies.

Original languageEnglish (US)
Pages (from-to)3576-3588
Number of pages13
JournalStatistics in Medicine
Volume25
Issue number20
DOIs
StatePublished - Oct 30 2006

Keywords

  • Bootstrap methods
  • Hierarchical binary outcomes
  • Intraclass correlation
  • Multilevel generalized linear model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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