Resampling dependent concordance correlation coefficients

John M. Williamson, Sara B. Crawford, Hung Mo Lin

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The concordance correlation coefficient (CCC) is a popular index for measuring the reproducibility of continuous variables. We examine two resampling approaches, permutation testing and the bootstrap, for conducting hypothesis tests on dependent CCCs obtained from the same sample. Resampling methods are flexible, require minimal marginal and joint distributional assumptions, and do not rely on large sample theory. However, the permutation test requires a restrictive assumption (exchangeability) which limits its applicability in this situation. Simulation results indicate that inference based on the bootstrap is valid, although type-I error rates are inflated for small sample sizes (≈30). For illustration we analyze data from a carotid stenosis screening study.

Original languageEnglish
Pages (from-to)685-696
Number of pages12
JournalJournal of Biopharmaceutical Statistics
Volume17
Issue number4
DOIs
StatePublished - Jul 2007
Externally publishedYes

Keywords

  • Agreement
  • Bootstrap
  • Concordance correlation coefficient
  • Correlated data
  • Permutation test
  • Resampling

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