title: Bayesian inference for kappa from single and multiple studies.
abstract: Cohen's kappa coefficient is a widely popular measure for chance-corrected nominal scale agreement between two raters. This article describes Bayesian analysis for kappa that can be routinely implemented using Markov chain Monte Carlo (MCMC) methodology. We consider the case of m > or = 2 independent samples of measured agreement, where in each sample a given subject is rated by two rating protocols on a binary scale. A major focus here is on testing the homogeneity of the kappa coefficient across the different samples. The existing frequentist tests for this case assume exchangeability of rating protocols, whereas our proposed Bayesian test does not make any such assumption. Extensive simulation is carried out to compare the performances of the Bayesian and the frequentist tests. The developed methodology is illustrated using data from a clinical trial in ophthalmology.
authors.affiliation: Division of Statistics, Northern Illinois University, Dekalb 60115, USA. basu@math.niu.edu
authors.forename: S
authors.initials: S
authors.lastname: Basu
authors.forename: M
authors.initials: M
authors.lastname: Banerjee
authors.forename: A
authors.initials: A
authors.lastname: Sen
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D001699:Biometry / Q000379:methods*; D005740:Gases / Q000627:therapeutic use; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012164:Retinal Diseases / Q000601:surgery; D012828:Silicones / Q000627:therapeutic use; D014821:Vitrectomy
chemical_list: D005740:Gases; D012828:Silicones