Confidence intervals for intraclass correlation coefficients in variance components models. 2016

Nino Demetrashvili, and Ernst C Wit, and Edwin R van den Heuvel
Department of Epidemiology, University Medical Center Groningen, University of Groningen, RB Groningen, The Netherlands Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, AK Groningen, The Netherlands n.demetrashvili@umcg.nl.

Confidence intervals for intraclass correlation coefficients in agreement studies with continuous outcomes are model-specific and no generic approach exists. This paper provides two generic approaches for intraclass correlation coefficients of the form [Formula: see text] The first approach uses Satterthwaite's approximation and an F-distribution. The second approach uses the first and second moments of the intraclass correlation coefficient estimate in combination with a Beta distribution. Both approaches are based on the restricted maximum likelihood estimates for the variance components involved. Simulation studies are conducted to examine the coverage probabilities of the confidence intervals for agreement studies with a mix of small sample sizes. Two different three-way variance components models and balanced and unbalanced one-way random effects models are investigated. The proposed approaches are compared with other approaches developed for these specific models. The approach based on the F-distribution provides acceptable coverage probabilities, but the approach based on the Beta distribution results in accurate coverages for most settings in both balanced and unbalanced designs. A real agreement study is provided to illustrate the approaches.

UI MeSH Term Description Entries
D008495 Medical Oncology A subspecialty of internal medicine concerned with the study of neoplasms. Oncology, Medical,Clinical Oncology,Oncology, Clinical
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000704 Analysis of Variance A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable. ANOVA,Analysis, Variance,Variance Analysis,Analyses, Variance,Variance Analyses
D013363 Submandibular Gland One of two salivary glands in the neck, located in the space bound by the two bellies of the digastric muscle and the angle of the mandible. It discharges through the submandibular duct. The secretory units are predominantly serous although a few mucous alveoli, some with serous demilunes, occur. (Stedman, 25th ed) Submaxillary Gland,Gland, Submandibular,Gland, Submaxillary,Glands, Submandibular,Glands, Submaxillary,Submandibular Glands,Submaxillary Glands
D016001 Confidence Intervals A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable. Confidence Interval,Interval, Confidence,Intervals, Confidence
D016013 Likelihood Functions Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters. Likelihood Ratio Test,Maximum Likelihood Estimates,Estimate, Maximum Likelihood,Estimates, Maximum Likelihood,Function, Likelihood,Functions, Likelihood,Likelihood Function,Maximum Likelihood Estimate,Test, Likelihood Ratio
D018401 Sample Size The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95) Sample Sizes,Size, Sample,Sizes, Sample

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