Inference on cancer screening exam accuracy using population‐level administrative data. (16th August 2015)

• Record Type: Journal Article
• Authors: Jiang, H.; Brown, P. E.; Walter, S.D.
• Is Part Of: Statistics in medicine. Volume 35:Number 1(2016)
• Page Start: 130
• Page End: 146
• Publication Date: 2015-08-16
• Subjects: Bayesian inference -- test accuracy -- cancer screening -- clustered analysis -- random effect -- latent‐class model; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6619
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : This paper develops a model for cancer screening and cancer incidence data, accommodating the partially unobserved disease status, clustered data structures, general covariate effects, and dependence between exams. The true unobserved cancer and detection status of screening participants are treated as latent variables, and a Markov Chain Monte Carlo algorithm is used to estimate the Bayesian posterior distributions of the diagnostic error rates and disease prevalence. We show how the Bayesian approach can be used to draw inferences about screening exam properties and disease prevalence while allowing for the possibility of conditional dependence between two exams. The techniques are applied to the estimation of the diagnostic accuracy of mammography and clinical breast examination using data from the Ontario Breast Screening Program in Canada. Copyright © 2015 John Wiley & Sons, Ltd.