Evaluating the accuracy and economic value of a new test in the absence of a perfect reference test
Background Streptococcus pneumoniae (SP) pneumonia is often treated empirically as diagnosis is challenging because of the lack of a perfect test. Using BinaxNOW‐SP, a urinary antigen test, as an add‐on to standard cultures may not only increase diagnostic yield but also increase costs. Objective To...
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| Veröffentlicht in: | Research synthesis methods 2017-09, Vol.8 (3), p.321-332 |
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| creator | Xie, Xuanqian Sinclair, Alison Dendukuri, Nandini |
| description | Background
Streptococcus pneumoniae (SP) pneumonia is often treated empirically as diagnosis is challenging because of the lack of a perfect test. Using BinaxNOW‐SP, a urinary antigen test, as an add‐on to standard cultures may not only increase diagnostic yield but also increase costs.
Objective
To estimate the sensitivity and specificity of BinaxNOW‐SP and subsequently estimate the cost‐effectiveness of adding BinaxNOW‐SP to the diagnostic work‐up.
Design
We fit a Bayesian latent‐class meta‐analysis model to obtain estimates of BinaxNOW‐SP accuracy that adjust for the imperfect accuracy of culture. Meta‐analysis results were combined with information on prevalence of SP pneumonia to estimate the number of patients who are correctly classified under competing diagnostic strategies. Taking into consideration the cost of antibiotics, we determined the incremental cost of adding BinaxNOW‐SP to the work‐up per case correctly diagnosed.
Results
The BinaxNOW‐SP test had a pooled sensitivity of 0.74 (95% credible interval [CrI], 0.67‐0.83) and a pooled specificity of 0.96 (95% CrI, 0.92‐0.99). An overall increase in diagnostic accuracy of 6.2% due to the addition of BinaxNOW‐SP corresponded to an incremental cost per case correctly classified of $582 Canadian dollars.
Conclusions
The methods we have described allow us to evaluate the accuracy and economic value of a new test in the absence of a perfect reference test using an evidence‐based approach. |
| doi_str_mv | 10.1002/jrsm.1243 |
| format | Article |
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Streptococcus pneumoniae (SP) pneumonia is often treated empirically as diagnosis is challenging because of the lack of a perfect test. Using BinaxNOW‐SP, a urinary antigen test, as an add‐on to standard cultures may not only increase diagnostic yield but also increase costs.
Objective
To estimate the sensitivity and specificity of BinaxNOW‐SP and subsequently estimate the cost‐effectiveness of adding BinaxNOW‐SP to the diagnostic work‐up.
Design
We fit a Bayesian latent‐class meta‐analysis model to obtain estimates of BinaxNOW‐SP accuracy that adjust for the imperfect accuracy of culture. Meta‐analysis results were combined with information on prevalence of SP pneumonia to estimate the number of patients who are correctly classified under competing diagnostic strategies. Taking into consideration the cost of antibiotics, we determined the incremental cost of adding BinaxNOW‐SP to the work‐up per case correctly diagnosed.
Results
The BinaxNOW‐SP test had a pooled sensitivity of 0.74 (95% credible interval [CrI], 0.67‐0.83) and a pooled specificity of 0.96 (95% CrI, 0.92‐0.99). An overall increase in diagnostic accuracy of 6.2% due to the addition of BinaxNOW‐SP corresponded to an incremental cost per case correctly classified of $582 Canadian dollars.
Conclusions
The methods we have described allow us to evaluate the accuracy and economic value of a new test in the absence of a perfect reference test using an evidence‐based approach.</description><identifier>ISSN: 1759-2879</identifier><identifier>EISSN: 1759-2887</identifier><identifier>DOI: 10.1002/jrsm.1243</identifier><identifier>PMID: 28544646</identifier><language>eng</language><publisher>England: Wiley-Blackwell</publisher><subject>Accuracy ; Antibiotics ; Antigens, Bacterial - analysis ; Antigens, Bacterial - urine ; Bayes Theorem ; Bayesian analysis ; Bayesian latent class meta‐analysis model ; Bayesian Statistics ; composite decision rule ; conditional dependence ; Cost Effectiveness ; Cost-Benefit Analysis ; diagnostic accuracy ; Diagnostic systems ; Diagnostic Test Approval ; Diagnostic Tests ; Diseases ; Humans ; Identification ; Medical Services ; Meta Analysis ; Pneumonia ; Pneumonia, Pneumococcal - diagnosis ; Pneumonia, Pneumococcal - urine ; Reagent Kits, Diagnostic ; Reference Standards ; Reference Values ; Sensitivity ; Sensitivity and Specificity ; Streptococcus infections</subject><ispartof>Research synthesis methods, 2017-09, Vol.8 (3), p.321-332</ispartof><rights>Copyright © 2017 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3753-49fc6f5810e3fdc23af3691994d5b11f288d0555c418581d38f149e3b7037da83</citedby><cites>FETCH-LOGICAL-c3753-49fc6f5810e3fdc23af3691994d5b11f288d0555c418581d38f149e3b7037da83</cites><orcidid>0000-0002-2330-0976</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjrsm.1243$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjrsm.1243$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=EJ1256834$$DView record in ERIC$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28544646$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xie, Xuanqian</creatorcontrib><creatorcontrib>Sinclair, Alison</creatorcontrib><creatorcontrib>Dendukuri, Nandini</creatorcontrib><title>Evaluating the accuracy and economic value of a new test in the absence of a perfect reference test</title><title>Research synthesis methods</title><addtitle>Res Synth Methods</addtitle><description>Background
Streptococcus pneumoniae (SP) pneumonia is often treated empirically as diagnosis is challenging because of the lack of a perfect test. Using BinaxNOW‐SP, a urinary antigen test, as an add‐on to standard cultures may not only increase diagnostic yield but also increase costs.
Objective
To estimate the sensitivity and specificity of BinaxNOW‐SP and subsequently estimate the cost‐effectiveness of adding BinaxNOW‐SP to the diagnostic work‐up.
Design
We fit a Bayesian latent‐class meta‐analysis model to obtain estimates of BinaxNOW‐SP accuracy that adjust for the imperfect accuracy of culture. Meta‐analysis results were combined with information on prevalence of SP pneumonia to estimate the number of patients who are correctly classified under competing diagnostic strategies. Taking into consideration the cost of antibiotics, we determined the incremental cost of adding BinaxNOW‐SP to the work‐up per case correctly diagnosed.
Results
The BinaxNOW‐SP test had a pooled sensitivity of 0.74 (95% credible interval [CrI], 0.67‐0.83) and a pooled specificity of 0.96 (95% CrI, 0.92‐0.99). An overall increase in diagnostic accuracy of 6.2% due to the addition of BinaxNOW‐SP corresponded to an incremental cost per case correctly classified of $582 Canadian dollars.
Conclusions
The methods we have described allow us to evaluate the accuracy and economic value of a new test in the absence of a perfect reference test using an evidence‐based approach.</description><subject>Accuracy</subject><subject>Antibiotics</subject><subject>Antigens, Bacterial - analysis</subject><subject>Antigens, Bacterial - urine</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian latent class meta‐analysis model</subject><subject>Bayesian Statistics</subject><subject>composite decision rule</subject><subject>conditional dependence</subject><subject>Cost Effectiveness</subject><subject>Cost-Benefit Analysis</subject><subject>diagnostic accuracy</subject><subject>Diagnostic systems</subject><subject>Diagnostic Test Approval</subject><subject>Diagnostic Tests</subject><subject>Diseases</subject><subject>Humans</subject><subject>Identification</subject><subject>Medical Services</subject><subject>Meta Analysis</subject><subject>Pneumonia</subject><subject>Pneumonia, Pneumococcal - diagnosis</subject><subject>Pneumonia, Pneumococcal - urine</subject><subject>Reagent Kits, Diagnostic</subject><subject>Reference Standards</subject><subject>Reference Values</subject><subject>Sensitivity</subject><subject>Sensitivity and Specificity</subject><subject>Streptococcus infections</subject><issn>1759-2879</issn><issn>1759-2887</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kEtP3DAUha2qqCBgwQ9oZambshjGzvUjXlZoeAmERMs68jjXbUZ5TO2kaP49zmSYBRLeXMvnu_f4HkLOOLvgjGXzVYjNBc8EfCJHXEszy_Jcf97ftTkkpzGuWDpgVKb0F3KY5VIIJdQRcYv_th5sX7V_aP8XqXVuCNZtqG1Liq5ru6ZydGSQdp5a2uIL7TH2tGqnhmXE1u3ENQaPrqcBPYbt84iekANv64inu3pMnq8Wvy9vZveP17eXP-9nDrSEmTDeKS9zzhB86TKwHpThxohSLjn3aa-SSSmd4HmiSsg9FwZhqRno0uZwTH5Mc9eh-zck46KposO6ti12Qyy4YcAVmAwS-v0duuqG0KbfJQoUgNbCJOp8olzoYkxLFetQNTZsCs6KMfxiDL8Yw0_st93EYdlguSffok7A1wnAULm9vLjjmVQ5iKTPJ_2lqnHzsVNx9_TrYWv5CtpXliA</recordid><startdate>201709</startdate><enddate>201709</enddate><creator>Xie, Xuanqian</creator><creator>Sinclair, Alison</creator><creator>Dendukuri, Nandini</creator><general>Wiley-Blackwell</general><general>Wiley Subscription Services, Inc</general><scope>7SW</scope><scope>BJH</scope><scope>BNH</scope><scope>BNI</scope><scope>BNJ</scope><scope>BNO</scope><scope>ERI</scope><scope>PET</scope><scope>REK</scope><scope>WWN</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-2330-0976</orcidid></search><sort><creationdate>201709</creationdate><title>Evaluating the accuracy and economic value of a new test in the absence of a perfect reference test</title><author>Xie, Xuanqian ; Sinclair, Alison ; Dendukuri, Nandini</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3753-49fc6f5810e3fdc23af3691994d5b11f288d0555c418581d38f149e3b7037da83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Antibiotics</topic><topic>Antigens, Bacterial - analysis</topic><topic>Antigens, Bacterial - urine</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Bayesian latent class meta‐analysis model</topic><topic>Bayesian Statistics</topic><topic>composite decision rule</topic><topic>conditional dependence</topic><topic>Cost Effectiveness</topic><topic>Cost-Benefit Analysis</topic><topic>diagnostic accuracy</topic><topic>Diagnostic systems</topic><topic>Diagnostic Test Approval</topic><topic>Diagnostic Tests</topic><topic>Diseases</topic><topic>Humans</topic><topic>Identification</topic><topic>Medical Services</topic><topic>Meta Analysis</topic><topic>Pneumonia</topic><topic>Pneumonia, Pneumococcal - diagnosis</topic><topic>Pneumonia, Pneumococcal - urine</topic><topic>Reagent Kits, Diagnostic</topic><topic>Reference Standards</topic><topic>Reference Values</topic><topic>Sensitivity</topic><topic>Sensitivity and Specificity</topic><topic>Streptococcus infections</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xie, Xuanqian</creatorcontrib><creatorcontrib>Sinclair, Alison</creatorcontrib><creatorcontrib>Dendukuri, Nandini</creatorcontrib><collection>ERIC</collection><collection>ERIC (Ovid)</collection><collection>ERIC</collection><collection>ERIC</collection><collection>ERIC (Legacy Platform)</collection><collection>ERIC( SilverPlatter )</collection><collection>ERIC</collection><collection>ERIC PlusText (Legacy Platform)</collection><collection>Education Resources Information Center (ERIC)</collection><collection>ERIC</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Research synthesis methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xie, Xuanqian</au><au>Sinclair, Alison</au><au>Dendukuri, Nandini</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ1256834</ericid><atitle>Evaluating the accuracy and economic value of a new test in the absence of a perfect reference test</atitle><jtitle>Research synthesis methods</jtitle><addtitle>Res Synth Methods</addtitle><date>2017-09</date><risdate>2017</risdate><volume>8</volume><issue>3</issue><spage>321</spage><epage>332</epage><pages>321-332</pages><issn>1759-2879</issn><eissn>1759-2887</eissn><abstract>Background
Streptococcus pneumoniae (SP) pneumonia is often treated empirically as diagnosis is challenging because of the lack of a perfect test. Using BinaxNOW‐SP, a urinary antigen test, as an add‐on to standard cultures may not only increase diagnostic yield but also increase costs.
Objective
To estimate the sensitivity and specificity of BinaxNOW‐SP and subsequently estimate the cost‐effectiveness of adding BinaxNOW‐SP to the diagnostic work‐up.
Design
We fit a Bayesian latent‐class meta‐analysis model to obtain estimates of BinaxNOW‐SP accuracy that adjust for the imperfect accuracy of culture. Meta‐analysis results were combined with information on prevalence of SP pneumonia to estimate the number of patients who are correctly classified under competing diagnostic strategies. Taking into consideration the cost of antibiotics, we determined the incremental cost of adding BinaxNOW‐SP to the work‐up per case correctly diagnosed.
Results
The BinaxNOW‐SP test had a pooled sensitivity of 0.74 (95% credible interval [CrI], 0.67‐0.83) and a pooled specificity of 0.96 (95% CrI, 0.92‐0.99). An overall increase in diagnostic accuracy of 6.2% due to the addition of BinaxNOW‐SP corresponded to an incremental cost per case correctly classified of $582 Canadian dollars.
Conclusions
The methods we have described allow us to evaluate the accuracy and economic value of a new test in the absence of a perfect reference test using an evidence‐based approach.</abstract><cop>England</cop><pub>Wiley-Blackwell</pub><pmid>28544646</pmid><doi>10.1002/jrsm.1243</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-2330-0976</orcidid></addata></record> |
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| subjects | Accuracy Antibiotics Antigens, Bacterial - analysis Antigens, Bacterial - urine Bayes Theorem Bayesian analysis Bayesian latent class meta‐analysis model Bayesian Statistics composite decision rule conditional dependence Cost Effectiveness Cost-Benefit Analysis diagnostic accuracy Diagnostic systems Diagnostic Test Approval Diagnostic Tests Diseases Humans Identification Medical Services Meta Analysis Pneumonia Pneumonia, Pneumococcal - diagnosis Pneumonia, Pneumococcal - urine Reagent Kits, Diagnostic Reference Standards Reference Values Sensitivity Sensitivity and Specificity Streptococcus infections |
| title | Evaluating the accuracy and economic value of a new test in the absence of a perfect reference test |
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