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Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making

Rhiannon Owen Orcid Logo, Nicola J. Cooper, Terence J. Quinn, Rosalind Lees, Alex J. Sutton

Journal of Clinical Epidemiology, Volume: 99, Pages: 64 - 74

Swansea University Author: Rhiannon Owen Orcid Logo

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DOI (Published version): 10.1016/j.jclinepi.2018.03.005

Abstract

ObjectivesNetwork meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are...

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Published in: Journal of Clinical Epidemiology
ISSN: 0895-4356
Published: Elsevier BV 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa60670
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-08-04T12:21:00.2596063</datestamp><bib-version>v2</bib-version><id>60670</id><entry>2022-07-28</entry><title>Network meta-analysis of diagnostic test accuracy studies identifies and&#xA0;ranks the optimal diagnostic tests and thresholds for health care&#xA0;policy and decision-making</title><swanseaauthors><author><sid>0d30aa00eef6528f763a1e1589f703ec</sid><ORCID>0000-0001-5977-376X</ORCID><firstname>Rhiannon</firstname><surname>Owen</surname><name>Rhiannon Owen</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-07-28</date><deptcode>HDAT</deptcode><abstract>ObjectivesNetwork meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis.Study Design and SettingMotivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE &lt;25/30 and &lt;27/30, and MoCA &lt;22/30 and &lt;26/30. Using Markov chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study.ResultsWe developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold &lt;26/30 appeared to have the best true positive rate, whereas MMSE at threshold &lt;25/30 appeared to have the best true negative rate.ConclusionThe combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making.</abstract><type>Journal Article</type><journal>Journal of Clinical Epidemiology</journal><volume>99</volume><journalNumber/><paginationStart>64</paginationStart><paginationEnd>74</paginationEnd><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0895-4356</issnPrint><issnElectronic/><keywords>Network meta-analysis; Meta-analysis; Diagnostic test accuracy; Multiple tests; Multiple thresholds</keywords><publishedDay>1</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2018</publishedYear><publishedDate>2018-07-01</publishedDate><doi>10.1016/j.jclinepi.2018.03.005</doi><url/><notes/><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2022-08-04T12:21:00.2596063</lastEdited><Created>2022-07-28T20:32:33.2155457</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Rhiannon</firstname><surname>Owen</surname><orcid>0000-0001-5977-376X</orcid><order>1</order></author><author><firstname>Nicola J.</firstname><surname>Cooper</surname><order>2</order></author><author><firstname>Terence J.</firstname><surname>Quinn</surname><order>3</order></author><author><firstname>Rosalind</firstname><surname>Lees</surname><order>4</order></author><author><firstname>Alex J.</firstname><surname>Sutton</surname><order>5</order></author></authors><documents><document><filename>60670__24840__8a40fc253dbe4a99a1cafe0370540196.pdf</filename><originalFilename>60670.pdf</originalFilename><uploaded>2022-08-04T12:19:16.0212578</uploaded><type>Output</type><contentLength>842408</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: 2018 The Authors. This is an open access article under the CC BY-NC-ND license</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by-nc-nd/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-08-04T12:21:00.2596063 v2 60670 2022-07-28 Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making 0d30aa00eef6528f763a1e1589f703ec 0000-0001-5977-376X Rhiannon Owen Rhiannon Owen true false 2022-07-28 HDAT ObjectivesNetwork meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis.Study Design and SettingMotivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study.ResultsWe developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whereas MMSE at threshold <25/30 appeared to have the best true negative rate.ConclusionThe combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making. Journal Article Journal of Clinical Epidemiology 99 64 74 Elsevier BV 0895-4356 Network meta-analysis; Meta-analysis; Diagnostic test accuracy; Multiple tests; Multiple thresholds 1 7 2018 2018-07-01 10.1016/j.jclinepi.2018.03.005 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University 2022-08-04T12:21:00.2596063 2022-07-28T20:32:33.2155457 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Rhiannon Owen 0000-0001-5977-376X 1 Nicola J. Cooper 2 Terence J. Quinn 3 Rosalind Lees 4 Alex J. Sutton 5 60670__24840__8a40fc253dbe4a99a1cafe0370540196.pdf 60670.pdf 2022-08-04T12:19:16.0212578 Output 842408 application/pdf Version of Record true Copyright: 2018 The Authors. This is an open access article under the CC BY-NC-ND license true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making
spellingShingle Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making
Rhiannon Owen
title_short Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making
title_full Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making
title_fullStr Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making
title_full_unstemmed Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making
title_sort Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making
author_id_str_mv 0d30aa00eef6528f763a1e1589f703ec
author_id_fullname_str_mv 0d30aa00eef6528f763a1e1589f703ec_***_Rhiannon Owen
author Rhiannon Owen
author2 Rhiannon Owen
Nicola J. Cooper
Terence J. Quinn
Rosalind Lees
Alex J. Sutton
format Journal article
container_title Journal of Clinical Epidemiology
container_volume 99
container_start_page 64
publishDate 2018
institution Swansea University
issn 0895-4356
doi_str_mv 10.1016/j.jclinepi.2018.03.005
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
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 - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description ObjectivesNetwork meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis.Study Design and SettingMotivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study.ResultsWe developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whereas MMSE at threshold <25/30 appeared to have the best true negative rate.ConclusionThe combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making.
published_date 2018-07-01T04:18:58Z
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score 11.03559

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