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Bayesian hierarchical latent class models for estimating diagnostic accuracy.

Authors
  • Wang, Chunling1
  • Lin, Xiaoyan1
  • Nelson, Kerrie P2
  • 1 Department of Statistics, University of South Carolina, Columbia, SC, USA.
  • 2 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Type
Published Article
Journal
Statistical Methods in Medical Research
Publisher
SAGE Publications
Publication Date
Apr 01, 2020
Volume
29
Issue
4
Pages
1112–1128
Identifiers
DOI: 10.1177/0962280219852649
PMID: 31146651
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The diagnostic accuracy of a test or rater has a crucial impact on clinical decision making. The assessment of diagnostic accuracy for multiple tests or raters also merits much attention. A Bayesian hierarchical conditional independence latent class model for estimating sensitivities and specificities for a large group of tests or raters is proposed, which is applicable to both with-gold-standard and without-gold-standard situations. Through the hierarchical structure, not only are the sensitivities and specificities of individual tests estimated, but also the diagnostic performance of the whole group of tests. For a small group of tests or raters, the proposed model is further extended by introducing pairwise covariances between tests to improve the fitting and to allow for more modeling flexibility. Correlation residual analysis is applied to detect any significant covariance between multiple tests. Just Another Gibbs Sampler (JAGS) implementation is efficiently adopted for both models. Three real data sets from literature are analyzed to explicitly illustrate the proposed methods.

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