title: A comparison of confidence interval methods for the concordance correlation coefficient and intraclass correlation coefficient with small number of raters.
abstract: The intraclass correlation coefficient (ICC) with fixed raters or, equivalently, the concordance correlation coefficient (CCC) for continuous outcomes is a widely accepted aggregate index of agreement in settings with small number of raters. Quantifying the precision of the CCC by constructing its confidence interval (CI) is important in early drug development applications, in particular in qualification of biomarker platforms. In recent years, there have been several new methods proposed for construction of CIs for the CCC, but their comprehensive comparison has not been attempted. The methods consisted of the delta method and jackknifing with and without Fisher's Z-transformation, respectively, and Bayesian methods with vague priors. In this study, we carried out a simulation study, with data simulated from multivariate normal as well as heavier tailed distribution (t-distribution with 5 degrees of freedom), to compare the state-of-the-art methods for assigning CI to the CCC. When the data are normally distributed, the jackknifing with Fisher's Z-transformation (JZ) tended to provide superior coverage and the difference between it and the closest competitor, the Bayesian method with the Jeffreys prior was in general minimal. For the nonnormal data, the jackknife methods, especially the JZ method, provided the coverage probabilities closest to the nominal in contrast to the others which yielded overly liberal coverage. Approaches based upon the delta method and Bayesian method with conjugate prior generally provided slightly narrower intervals and larger lower bounds than others, though this was offset by their poor coverage. Finally, we illustrated the utility of the CIs for the CCC in an example of a wake after sleep onset (WASO) biomarker, which is frequently used in clinical sleep studies of drugs for treatment of insomnia.
authors.affiliation: a Biometrics Research Department , Merck Research Laboratories, Merck & Co., Inc. , Rahway , New Jersey , USA.
authors.forename: Dai
authors.initials: D
authors.lastname: Feng
authors.forename: Vladimir
authors.initials: V
authors.lastname: Svetnik
authors.forename: Alexandre
authors.initials: A
authors.lastname: Coimbra
authors.forename: Richard
authors.initials: R
authors.lastname: Baumgartner
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation / Q000706:statistics & numerical data; D016001:Confidence Intervals; D006801:Humans; D015233:Models, Statistical; D012894:Sleep Stages / Q000502:physiology