Bayesian comparison of diagnostic tests with largely non-informative missing data. Issue 10 (3rd July 2019)

• Record Type: Journal Article
• Authors: Paulino, Carlos Daniel; Silva, Giovani L.
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 10(2019)
• Page Start: 1877
• Page End: 1886
• Publication Date: 2019-07-03
• Subjects: Missing at random data -- diagnostic accuracy measures -- chain data augmentation algorithm -- generalized Dirichlet distribution; 62F15; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1601726
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: This work was motivated by a real problem of comparing binary diagnostic tests based upon a gold standard, where the collected data showed that the large majority of classifications were incomplete and the feedback received from the medical doctors allowed us to consider the missingness as non-informative. Taking into account the degree of data incompleteness, we used a Bayesian approach via MCMC methods for drawing inferences of interest on accuracy measures. Its direct implementation by well-known software demonstrated serious problems of chain convergence. The difficulties were overcome by the proposal of a simple, efficient and easily adaptable data augmentation algorithm, performed through an ad hoc computer program.