What is it about?
This work compares binary diagnostic tests based upon a gold standard, where the collected data have large majority of classifications that are incomplete and the feedback received from the medical doctors allowed us to consider the missingness as non-informative.
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Why is it important?
The difficulties were overcome by the proposal of a simple, efficient and easily adaptable data augmentation algorithm, performed through an ad hoc computer program.
Perspectives
Taking into account the degree of data incompleteness, we point out 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.
Giovani Silva
Universidade de Lisboa
Read the Original
This page is a summary of: Bayesian comparison of diagnostic tests with largely non-informative missing data, Journal of Statistical Computation and Simulation, April 2019, Taylor & Francis,
DOI: 10.1080/00949655.2019.1601726.
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