Search

Many disease diagnoses involve subjective judgments by qualified raters. For example, through the inspection of a mammogram, MRI, or ultrasound image, the clinician himself becomes part of the measuring instrument. To reduce diagnostic errors and improve the quality of diagnoses, it is necessary to assess raters' diagnostic skills and to improve their skills over time. This paper focuses on a subjective binary classification process, proposing a hierarchical model linking data on rater opinions with patient true disease-development outcomes. The model allows for the quantification of the effects of rater diagnostic skills (bias and magnifier) and patient latent disease severity on the rating results. A Bayesian Markov chain Monte Carlo (MCMC) algorithm is developed to estimate these parameters. Linking to patient true disease outcomes, the rater-specific sensitivity and specificity can be estimated using MCMC samples. Cost theory is used to identify poor- and strong-performing raters and to guide adjustment of rater bias and diagnostic magnifier to improve the rating performance. Furthermore, diagnostic magnifier is shown as a key parameter to present a rater's diagnostic ability because a rater with a larger diagnostic magnifier has a uniformly better receiver operating characteristic (ROC) curve when varying the value of diagnostic bias. A simulation study is conducted to evaluate the proposed methods, and the methods are illustrated with a mammography example. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]