Comparison of Two Bayesian Methods in Evaluation of the Absence of the Gold Standard Diagnostic Tests
Joint Authors
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-08-21
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Objective.
The Bayesian model plays an important role in diagnostic test evaluation in the absence of the gold standard, which used the external prior distribution of a parameter combined with sample data to yield the posterior distribution of the test characteristics.
However, the correlation between diagnostic tests has always been a problem that cannot be ignored in the Bayesian model evaluation.
This study will discuss how different Bayesian model, correlation scenarios, and prior distribution affect the outcome.
Methods.
The data analyzed in this study was gathered during studies of patients presenting to the Nanjing Chest Hospital with suspected tuberculosis.
The diagnostic character of T-SPOT.Tb and KD38 tuberculosis antibody test were evaluated in different Bayesian model, and discharge diagnosis as a gold standard was used to verify the model results in the end.
Result.
The comparison of four models under the conditional independence situation found that Bayesian probabilistic constraint model was consistent with the Conditional Covariance Bayesian model.
The results were mainly affected by prior information.
The sensitivity and specificity of the two tests in Conditional Covariance Bayesian model in prior constraint situation were considerably higher than the Bayesian probabilistic constraint model in prior constraint situation.
The results of the four models under the conditional dependence situation were similar to the conditional independence situation; pD was also negative with no prior constraint situation in both model Bayesian probabilistic constraint model and Conditional Covariance Bayesian model.
The Deviance Information Criterion of Bayesian probabilistic constraint model was close to model Conditional Covariance Bayesian model, but pD of Conditional Covariance Bayesian model in Prior constraint situation (pD=2.40) was higher than the Bayesian probabilistic constraint model in Prior constraint situation (pD=1.66).
Conclusion.
The result of Conditional Covariance Bayesian model in prior constraint with conditional independence situation was closest to the result of gold standard evaluation in our data.
Both of the two Bayesian methods are the feasible way for the evaluation of diagnostic test in the absence of the gold standard diagnostic.
Prior source, priority number, and conditional dependencies should be considered in the method selection, the accuracy of posterior estimation mainly depending on the prior distribution.
American Psychological Association (APA)
Li, Taishun& Liu, Pei. 2019. Comparison of Two Bayesian Methods in Evaluation of the Absence of the Gold Standard Diagnostic Tests. BioMed Research International،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1123306
Modern Language Association (MLA)
Li, Taishun& Liu, Pei. Comparison of Two Bayesian Methods in Evaluation of the Absence of the Gold Standard Diagnostic Tests. BioMed Research International No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1123306
American Medical Association (AMA)
Li, Taishun& Liu, Pei. Comparison of Two Bayesian Methods in Evaluation of the Absence of the Gold Standard Diagnostic Tests. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1123306
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1123306