Higher-order evidence
Higher-order evidence is evidence about evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or o...
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| Veröffentlicht in: | European journal of epidemiology 2024, Vol.39 (1), p.1-11 |
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| container_title | European journal of epidemiology |
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| creator | Cole, Stephen R. Shook-Sa, Bonnie E. Zivich, Paul N. Edwards, Jessie K. Richardson, David B. Hudgens, Michael G. |
| description | Higher-order evidence is evidence
about
evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted. |
| doi_str_mv | 10.1007/s10654-023-01062-9 |
| format | Article |
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evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted.</description><identifier>ISSN: 0393-2990</identifier><identifier>ISSN: 1573-7284</identifier><identifier>EISSN: 1573-7284</identifier><identifier>DOI: 10.1007/s10654-023-01062-9</identifier><identifier>PMID: 38195955</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Bias ; Biopsy ; Cardiology ; Epidemiology ; Error analysis ; Error correction ; Essay ; Humans ; Hypoxia ; Independent sample ; Infectious Diseases ; Medical records ; Medicine ; Medicine & Public Health ; Oncology ; Pathology ; Public Health ; Self report ; Sensitivity and Specificity</subject><ispartof>European journal of epidemiology, 2024, Vol.39 (1), p.1-11</ispartof><rights>Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. Springer Nature B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c382t-ba55030885312f17c27b80049ee2152c905798922ce4e22ca65d98789f3158413</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10654-023-01062-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10654-023-01062-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38195955$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cole, Stephen R.</creatorcontrib><creatorcontrib>Shook-Sa, Bonnie E.</creatorcontrib><creatorcontrib>Zivich, Paul N.</creatorcontrib><creatorcontrib>Edwards, Jessie K.</creatorcontrib><creatorcontrib>Richardson, David B.</creatorcontrib><creatorcontrib>Hudgens, Michael G.</creatorcontrib><title>Higher-order evidence</title><title>European journal of epidemiology</title><addtitle>Eur J Epidemiol</addtitle><addtitle>Eur J Epidemiol</addtitle><description>Higher-order evidence is evidence
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evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted.</description><subject>Bias</subject><subject>Biopsy</subject><subject>Cardiology</subject><subject>Epidemiology</subject><subject>Error analysis</subject><subject>Error correction</subject><subject>Essay</subject><subject>Humans</subject><subject>Hypoxia</subject><subject>Independent sample</subject><subject>Infectious Diseases</subject><subject>Medical records</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Oncology</subject><subject>Pathology</subject><subject>Public Health</subject><subject>Self report</subject><subject>Sensitivity and Specificity</subject><issn>0393-2990</issn><issn>1573-7284</issn><issn>1573-7284</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kDtPwzAQgC0EoqUwsjAgJBYWw50dJ_aEUAUUqRILzFaaXNpUaVLsphL_HpeU8hhYfJbuu9fH2BnCNQIkNx4hVhEHITmEr-Bmj_VRJZInQkf7rA_SSC6MgR478n4OABqMOmQ9qdEoo1SfnY7K6Ywcb1xO7oLWZU51RsfsoEgrTyfbOGCvD_cvwxEfPz8-De_GPJNarPgkVQokaK0kigKTTCQTDRAZIoFKZAZUYrQRIqOIwpvGKjc60aaQqHSEcsBuu77LdrKgPKN65dLKLl25SN27bdLS_s7U5cxOm7VFRGF0GD5gV9sOrnlrya_sovQZVVVaU9N6KwyGBTEGFdDLP-i8aV0d7ttQWsZxFHwNmOiozDXeOyp22yDYjXbbabdBu_3UbjdF5z_v2JV8eQ6A7AAfUvWU3Pfsf9p-AFrbieU</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Cole, Stephen R.</creator><creator>Shook-Sa, Bonnie E.</creator><creator>Zivich, Paul N.</creator><creator>Edwards, Jessie K.</creator><creator>Richardson, David B.</creator><creator>Hudgens, Michael G.</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><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>3V.</scope><scope>7QL</scope><scope>7T2</scope><scope>7TK</scope><scope>7TS</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>2024</creationdate><title>Higher-order evidence</title><author>Cole, Stephen R. ; 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about
evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><pmid>38195955</pmid><doi>10.1007/s10654-023-01062-9</doi><tpages>11</tpages></addata></record> |
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| ispartof | European journal of epidemiology, 2024, Vol.39 (1), p.1-11 |
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| language | eng |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Bias Biopsy Cardiology Epidemiology Error analysis Error correction Essay Humans Hypoxia Independent sample Infectious Diseases Medical records Medicine Medicine & Public Health Oncology Pathology Public Health Self report Sensitivity and Specificity |
| title | Higher-order evidence |
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