A new method for synthesizing test accuracy data outperformed the bivariate method
<h3>Objectives</h3><p dir="ltr">This study outlines the development of a new method (split component synthesis; SCS) for meta-analysis of diagnostic accuracy studies and assesses its performance against the commonly used bivariate random effects model.</p><h3>...
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2020
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| _version_ | 1864513551505293312 |
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| author | Luis Furuya-Kanamori (477124) |
| author2 | Polychronis Kostoulas (5205047) Suhail A.R. Doi (13032075) |
| author2_role | author author |
| author_facet | Luis Furuya-Kanamori (477124) Polychronis Kostoulas (5205047) Suhail A.R. Doi (13032075) |
| author_role | author |
| dc.creator.none.fl_str_mv | Luis Furuya-Kanamori (477124) Polychronis Kostoulas (5205047) Suhail A.R. Doi (13032075) |
| dc.date.none.fl_str_mv | 2020-12-14T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jclinepi.2020.12.015 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_new_method_for_synthesizing_test_accuracy_data_outperformed_the_bivariate_method/24087669 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Epidemiology Mathematical sciences Statistics Diagnostic odds ratio Diagnostic accuracy Performance Hierarchical Bivariate Meta-analysis |
| dc.title.none.fl_str_mv | A new method for synthesizing test accuracy data outperformed the bivariate method |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Objectives</h3><p dir="ltr">This study outlines the development of a new method (split component synthesis; SCS) for meta-analysis of diagnostic accuracy studies and assesses its performance against the commonly used bivariate random effects model.</p><h3>Methods</h3><p dir="ltr">The SCS method summarizes the study-specific diagnostic odds ratio (on the ln(DOR) scale), which mainly reflects test discrimination rather than threshold effects, and then splits the summary ln(DOR) into its component parts, logit sensitivity (Se) and logit specificity (Sp). Performance of SCS estimator was assessed through simulation and compared against the bivariate random effects model estimator in terms of bias, mean squared error (MSE), and coverage probability across varying degrees of between-studies heterogeneity.</p><h3>Results</h3><p dir="ltr">The SCS estimator for the DOR, Se, and Sp was less biased and had smaller MSE than the bivariate model estimator. Despite the wider width of the 95% confidence intervals under the bivariate model, the latter had a poorer coverage probability than that under the SCS method.</p><h3>Conclusion</h3><p dir="ltr">The SCS estimator outperforms the bivariate model estimator and thus represents an improvement in the approach to diagnostic meta-analyses. The SCS method is available to researchers through the <i>diagma</i> module in Stata and the <i>SCSmeta</i> function in R.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Clinical Epidemiology<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jclinepi.2020.12.015" target="_blank">https://dx.doi.org/10.1016/j.jclinepi.2020.12.015</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_12b19bde4c20acf8bfe5f65e07be351e |
| identifier_str_mv | 10.1016/j.jclinepi.2020.12.015 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24087669 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A new method for synthesizing test accuracy data outperformed the bivariate methodLuis Furuya-Kanamori (477124)Polychronis Kostoulas (5205047)Suhail A.R. Doi (13032075)Health sciencesEpidemiologyMathematical sciencesStatisticsDiagnostic odds ratioDiagnostic accuracyPerformanceHierarchicalBivariateMeta-analysis<h3>Objectives</h3><p dir="ltr">This study outlines the development of a new method (split component synthesis; SCS) for meta-analysis of diagnostic accuracy studies and assesses its performance against the commonly used bivariate random effects model.</p><h3>Methods</h3><p dir="ltr">The SCS method summarizes the study-specific diagnostic odds ratio (on the ln(DOR) scale), which mainly reflects test discrimination rather than threshold effects, and then splits the summary ln(DOR) into its component parts, logit sensitivity (Se) and logit specificity (Sp). Performance of SCS estimator was assessed through simulation and compared against the bivariate random effects model estimator in terms of bias, mean squared error (MSE), and coverage probability across varying degrees of between-studies heterogeneity.</p><h3>Results</h3><p dir="ltr">The SCS estimator for the DOR, Se, and Sp was less biased and had smaller MSE than the bivariate model estimator. Despite the wider width of the 95% confidence intervals under the bivariate model, the latter had a poorer coverage probability than that under the SCS method.</p><h3>Conclusion</h3><p dir="ltr">The SCS estimator outperforms the bivariate model estimator and thus represents an improvement in the approach to diagnostic meta-analyses. The SCS method is available to researchers through the <i>diagma</i> module in Stata and the <i>SCSmeta</i> function in R.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Clinical Epidemiology<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jclinepi.2020.12.015" target="_blank">https://dx.doi.org/10.1016/j.jclinepi.2020.12.015</a></p>2020-12-14T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jclinepi.2020.12.015https://figshare.com/articles/journal_contribution/A_new_method_for_synthesizing_test_accuracy_data_outperformed_the_bivariate_method/24087669CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240876692020-12-14T00:00:00Z |
| spellingShingle | A new method for synthesizing test accuracy data outperformed the bivariate method Luis Furuya-Kanamori (477124) Health sciences Epidemiology Mathematical sciences Statistics Diagnostic odds ratio Diagnostic accuracy Performance Hierarchical Bivariate Meta-analysis |
| status_str | publishedVersion |
| title | A new method for synthesizing test accuracy data outperformed the bivariate method |
| title_full | A new method for synthesizing test accuracy data outperformed the bivariate method |
| title_fullStr | A new method for synthesizing test accuracy data outperformed the bivariate method |
| title_full_unstemmed | A new method for synthesizing test accuracy data outperformed the bivariate method |
| title_short | A new method for synthesizing test accuracy data outperformed the bivariate method |
| title_sort | A new method for synthesizing test accuracy data outperformed the bivariate method |
| topic | Health sciences Epidemiology Mathematical sciences Statistics Diagnostic odds ratio Diagnostic accuracy Performance Hierarchical Bivariate Meta-analysis |