title: Joint estimation of multiple disease-specific sensitivities and specificities via crossed random effects models for correlated reader-based diagnostic data: application of data cloning.
abstract: We present a model for describing correlated binocular data from reader-based diagnostic studies, where the same group of readers evaluates the presence or absence of certain diseases on binocular organs (e.g., fellow eyes) of patients. Multiple random effects are incorporated to meaningfully delineate various associations in the data including crossed random effects to account for reader-specific variability and to incorporate cross correlations. To overcome the computational complexity involved in the evaluation and maximization of the marginal likelihood, we adopt the data cloning approach, which calculates maximum likelihood estimates under the Bayesian paradigm. The bias and efficiency of the estimates are assessed in two simulation studies. We apply our model to data from a diabetic retinopathy study.
authors.affiliation: Department of Economics and Statistics, Sabaragamuwa University of Sri Lanka, Belihuloya, Sri Lanka.
authors.forename: Niroshan
authors.initials: N
authors.lastname: Withanage
authors.affiliation: Department of Mathematics and Statistics, University of Calgary, Alberta, Canada.
authors.forename: Alexander R
authors.identifier: http://orcid.org/0000-0002-3073-908X
authors.initials: AR
authors.lastname: de Leon
authors.affiliation: Department of Ophthalmology, University of Alberta, Alberta, Canada.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Rudnisky
mesh_terms: D000416:Alberta; D001499:Bayes Theorem; D015982:Bias; D003001:Cloning, Molecular / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003930:Diabetic Retinopathy / Q000175:diagnosis / Q000473:pathology*; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D009010:Monte Carlo Method; D010781:Photography / Q000379:methods; D011379:Prognosis; D012044:Regression Analysis; D012680:Sensitivity and Specificity
keywords: binocular data; correlated binary outcomes; latent variable; likelihood estimation; multivariate probit model; tetrachoric correlations