title: Resampling dependent concordance correlation coefficients.
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 ( approximately 30). For illustration we analyze data from a carotid stenosis screening study.
authors.affiliation: Division of Parasitic Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia 30341, USA. jow5@cdc.gov
authors.forename: John M
authors.initials: JM
authors.lastname: Williamson
authors.forename: Sara B
authors.initials: SB
authors.lastname: Crawford
authors.forename: Hung-Mo
authors.initials: HM
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry / Q000379:methods*; D016893:Carotid Stenosis / Q000175:diagnosis; D003198:Computer Simulation; D016001:Confidence Intervals; D019937:Diagnostic Techniques and Procedures / Q000706:statistics & numerical data*; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000379:methods / Q000706:statistics & numerical data; D015233:Models, Statistical; D015588:Observer Variation; D015203:Reproducibility of Results; D016008:Statistical Distributions