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Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia / Athena McBride

Swansea University Author: Athena McBride

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DOI (Published version): 10.23889/SUthesis.69487

Abstract

This thesis considers a range of methodological challenges related to the synthesis of comparative diagnostic accuracy studies that evaluate two tests in the same patients, and aims to address them through the development of novel meta-analysis methodol-ogy in a Bayesian framework. The novel methods...

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Published: Swansea, Wales, UK 2025
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Owen, Rhiannon K. ; Fry, Richard ; Bujkiewicz, Sylwia ; Quinn, Terence J.
URI: https://cronfa.swan.ac.uk/Record/cronfa69487
first_indexed 2025-05-09T14:03:17Z
last_indexed 2025-05-10T08:17:58Z
id cronfa69487
recordtype RisThesis
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spelling 2025-05-09T15:19:14.7737443 v2 69487 2025-05-09 Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia 4def66cb410a07fa9606d2ebd54e46f4 Athena McBride Athena McBride true false 2025-05-09 This thesis considers a range of methodological challenges related to the synthesis of comparative diagnostic accuracy studies that evaluate two tests in the same patients, and aims to address them through the development of novel meta-analysis methodol-ogy in a Bayesian framework. The novel methods are applied to a real-world example in Alzheimer’s disease dementia, for which test comparisons are important in opti-mising diagnostic pathways and improving detection. Firstly, the thesis introduces complex dependence structures present in meta-analyses of comparative diagnostic accuracy studies; in particular, within-study associations arising between sensitivi-ties and specificities when patients undergo both tests of interest. This thesis assesses the impact of accounting for within-study dependencies on key test accuracy param-eters by fitting a meta-analysis model that treats the two tests as independent to simulated data in which the associations are known. Ignoring within-study depen-dencies is shown to lead to underestimation of joint sensitivity and specificity, which measure the agreement between tests and enable modelling of diagnostic test com-binations and pathways. This motivates the need for methodological development to jointly model the accuracy of two diagnostic tests and the associations between them. Novel Bayesian meta-analysis models for synthesising evidence on the ac-curacy of two diagnostic tests are developed, capturing within-study dependencies using bivariate copulas. Motivated by an example in Alzheimer’s disease dementia, the bivariate copula framework is shown to lead to improved model fit compared to the approach that does not account for within-study associations. The bivariate copula models are extended to incorporate individual participant data on combined test performance, capturing within-study dependencies through trivariate copulas. These methods can be used to inform optimal combinations of diagnostic tests for health care policy and decision-making. E-Thesis Swansea, Wales, UK diagnostic test accuracy, meta-analysis, test comparison, health technology assessment, copula, Bayesian analysis 7 5 2025 2025-05-07 10.23889/SUthesis.69487 ORCiD identifier: https://orcid.org/0000-0003-1564-0740 COLLEGE NANME COLLEGE CODE Swansea University Owen, Rhiannon K. ; Fry, Richard ; Bujkiewicz, Sylwia ; Quinn, Terence J. Doctoral Ph.D Health Data Research UK (HDR UK), HDR UK Studentship N1WA1 Health Data Research UK (HDR UK), HDR UK Studentship N1WA1 2025-05-09T15:19:14.7737443 2025-05-09T14:57:47.3628564 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Athena McBride 1 69487__34228__1fa313e39fdc478a96ee811d9ae4b813.pdf McBride_Athena_PhD_Thesis_Final_Cronfa.pdf 2025-05-09T15:11:07.5070323 Output 7516025 application/pdf E-Thesis – open access true Copyright: The Author, Athena McBride, 2025. Licensed under the terms of a Creative Commons Attribution-Only (CC-BY) license. Third party content is excluded for use under the license terms. true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia
spellingShingle Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia
Athena McBride
title_short Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia
title_full Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia
title_fullStr Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia
title_full_unstemmed Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia
title_sort Bayesian evidence synthesis methods for two diagnostic tests: application to Alzheimer’s disease dementia
author_id_str_mv 4def66cb410a07fa9606d2ebd54e46f4
author_id_fullname_str_mv 4def66cb410a07fa9606d2ebd54e46f4_***_Athena McBride
author Athena McBride
author2 Athena McBride
format E-Thesis
publishDate 2025
institution Swansea University
doi_str_mv 10.23889/SUthesis.69487
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
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description This thesis considers a range of methodological challenges related to the synthesis of comparative diagnostic accuracy studies that evaluate two tests in the same patients, and aims to address them through the development of novel meta-analysis methodol-ogy in a Bayesian framework. The novel methods are applied to a real-world example in Alzheimer’s disease dementia, for which test comparisons are important in opti-mising diagnostic pathways and improving detection. Firstly, the thesis introduces complex dependence structures present in meta-analyses of comparative diagnostic accuracy studies; in particular, within-study associations arising between sensitivi-ties and specificities when patients undergo both tests of interest. This thesis assesses the impact of accounting for within-study dependencies on key test accuracy param-eters by fitting a meta-analysis model that treats the two tests as independent to simulated data in which the associations are known. Ignoring within-study depen-dencies is shown to lead to underestimation of joint sensitivity and specificity, which measure the agreement between tests and enable modelling of diagnostic test com-binations and pathways. This motivates the need for methodological development to jointly model the accuracy of two diagnostic tests and the associations between them. Novel Bayesian meta-analysis models for synthesising evidence on the ac-curacy of two diagnostic tests are developed, capturing within-study dependencies using bivariate copulas. Motivated by an example in Alzheimer’s disease dementia, the bivariate copula framework is shown to lead to improved model fit compared to the approach that does not account for within-study associations. The bivariate copula models are extended to incorporate individual participant data on combined test performance, capturing within-study dependencies through trivariate copulas. These methods can be used to inform optimal combinations of diagnostic tests for health care policy and decision-making.
published_date 2025-05-07T05:55:24Z
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score 11.064947

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