Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies

Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Steinhauser, Susanne [verfasserIn] Schumacher, Martin Rücker, Gerta

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Diagnostic accuracy study Meta-analysis Biomarker Threshold ROC curve

Anmerkung:	© The Author(s) 2016

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 16(2016), 1 vom: 12. Aug.
Übergeordnetes Werk:	volume:16 ; year:2016 ; number:1 ; day:12 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s12874-016-0196-1

Katalog-ID:	SPR027371298

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allfields	10.1186/s12874-016-0196-1 doi (DE-627)SPR027371298 (SPR)s12874-016-0196-1-e DE-627 ger DE-627 rakwb eng Steinhauser, Susanne verfasserin aut Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. Diagnostic accuracy study (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Threshold (dpeaa)DE-He213 ROC curve (dpeaa)DE-He213 Schumacher, Martin aut Rücker, Gerta (orcid)0000-0002-2192-2560 aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 12. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:12 month:08 https://dx.doi.org/10.1186/s12874-016-0196-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 12 08
spelling	10.1186/s12874-016-0196-1 doi (DE-627)SPR027371298 (SPR)s12874-016-0196-1-e DE-627 ger DE-627 rakwb eng Steinhauser, Susanne verfasserin aut Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. Diagnostic accuracy study (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Threshold (dpeaa)DE-He213 ROC curve (dpeaa)DE-He213 Schumacher, Martin aut Rücker, Gerta (orcid)0000-0002-2192-2560 aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 12. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:12 month:08 https://dx.doi.org/10.1186/s12874-016-0196-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 12 08
allfields_unstemmed	10.1186/s12874-016-0196-1 doi (DE-627)SPR027371298 (SPR)s12874-016-0196-1-e DE-627 ger DE-627 rakwb eng Steinhauser, Susanne verfasserin aut Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. Diagnostic accuracy study (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Threshold (dpeaa)DE-He213 ROC curve (dpeaa)DE-He213 Schumacher, Martin aut Rücker, Gerta (orcid)0000-0002-2192-2560 aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 12. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:12 month:08 https://dx.doi.org/10.1186/s12874-016-0196-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 12 08
allfieldsGer	10.1186/s12874-016-0196-1 doi (DE-627)SPR027371298 (SPR)s12874-016-0196-1-e DE-627 ger DE-627 rakwb eng Steinhauser, Susanne verfasserin aut Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. Diagnostic accuracy study (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Threshold (dpeaa)DE-He213 ROC curve (dpeaa)DE-He213 Schumacher, Martin aut Rücker, Gerta (orcid)0000-0002-2192-2560 aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 12. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:12 month:08 https://dx.doi.org/10.1186/s12874-016-0196-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 12 08
allfieldsSound	10.1186/s12874-016-0196-1 doi (DE-627)SPR027371298 (SPR)s12874-016-0196-1-e DE-627 ger DE-627 rakwb eng Steinhauser, Susanne verfasserin aut Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. Diagnostic accuracy study (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Threshold (dpeaa)DE-He213 ROC curve (dpeaa)DE-He213 Schumacher, Martin aut Rücker, Gerta (orcid)0000-0002-2192-2560 aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 12. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:12 month:08 https://dx.doi.org/10.1186/s12874-016-0196-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 12 08
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author	Steinhauser, Susanne
spellingShingle	Steinhauser, Susanne misc Diagnostic accuracy study misc Meta-analysis misc Biomarker misc Threshold misc ROC curve Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies
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topic_title	Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies Diagnostic accuracy study (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Threshold (dpeaa)DE-He213 ROC curve (dpeaa)DE-He213
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title	Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies
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title_full	Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies
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title_sort	modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies
title_auth	Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies
abstract	Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. © The Author(s) 2016
abstractGer	Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. © The Author(s) 2016
abstract_unstemmed	Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. © The Author(s) 2016
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title_short	Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies
url	https://dx.doi.org/10.1186/s12874-016-0196-1
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Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. 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Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies

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