Methods to elicit beliefs for Bayesian priors: a systematic review
Abstract Objective Bayesian analysis can incorporate clinicians' beliefs about treatment effectiveness into models that estimate treatment effects. Many elicitation methods are available, but it is unclear if any confer advantages based on principles of measurement science. We review belief-eli...
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| Veröffentlicht in: | Journal of clinical epidemiology 2010-04, Vol.63 (4), p.355-369 |
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| creator | Johnson, Sindhu R Tomlinson, George A Hawker, Gillian A Granton, John T Feldman, Brian M |
| description | Abstract Objective Bayesian analysis can incorporate clinicians' beliefs about treatment effectiveness into models that estimate treatment effects. Many elicitation methods are available, but it is unclear if any confer advantages based on principles of measurement science. We review belief-elicitation methods for Bayesian analysis and determine if any of them had an incremental value over the others based on its validity, reliability, and responsiveness. Study Design and Setting A systematic review was performed. MEDLINE, EMBASE, CINAHL, Health and Psychosocial Instruments, Current Index to Statistics, MathSciNet, and Zentralblatt Math were searched using the terms (prior OR prior probability distribution) AND (beliefs OR elicitation) AND (Bayes OR Bayesian). Studies were evaluated on: design, question stem, response options, analysis, consideration of validity, reliability, and responsiveness. Results We identified 33 studies describing methods for elicitation in a Bayesian context. Elicitation occurred in cross-sectional studies ( n = 30, 89%), to derive point estimates with individual-level variation ( n = 19; 58%). Although 64% ( n = 21) considered validity, 24% ( n = 8) reliability, 12% ( n = 4) responsiveness of the elicitation methods, only 12% ( n = 4) formally tested validity, 6% ( n = 2) tested reliability, and none tested responsiveness. Conclusions We have summarized methods of belief elicitation for Bayesian priors. The validity, reliability, and responsiveness of elicitation methods have been infrequently evaluated. Until comparative studies are performed, strategies to reduce the effects of bias on the elicitation should be used. |
| doi_str_mv | 10.1016/j.jclinepi.2009.06.003 |
| format | Article |
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Many elicitation methods are available, but it is unclear if any confer advantages based on principles of measurement science. We review belief-elicitation methods for Bayesian analysis and determine if any of them had an incremental value over the others based on its validity, reliability, and responsiveness. Study Design and Setting A systematic review was performed. MEDLINE, EMBASE, CINAHL, Health and Psychosocial Instruments, Current Index to Statistics, MathSciNet, and Zentralblatt Math were searched using the terms (prior OR prior probability distribution) AND (beliefs OR elicitation) AND (Bayes OR Bayesian). Studies were evaluated on: design, question stem, response options, analysis, consideration of validity, reliability, and responsiveness. Results We identified 33 studies describing methods for elicitation in a Bayesian context. Elicitation occurred in cross-sectional studies ( n = 30, 89%), to derive point estimates with individual-level variation ( n = 19; 58%). Although 64% ( n = 21) considered validity, 24% ( n = 8) reliability, 12% ( n = 4) responsiveness of the elicitation methods, only 12% ( n = 4) formally tested validity, 6% ( n = 2) tested reliability, and none tested responsiveness. Conclusions We have summarized methods of belief elicitation for Bayesian priors. The validity, reliability, and responsiveness of elicitation methods have been infrequently evaluated. Until comparative studies are performed, strategies to reduce the effects of bias on the elicitation should be used.</description><identifier>ISSN: 0895-4356</identifier><identifier>EISSN: 1878-5921</identifier><identifier>DOI: 10.1016/j.jclinepi.2009.06.003</identifier><identifier>PMID: 19716263</identifier><language>eng</language><publisher>New York, NY: Elsevier Inc</publisher><subject>Bayes Theorem ; Bayesian ; Belief elicitation ; Bias ; Biological and medical sciences ; Clinical trial. Drug monitoring ; Clinical Trials as Topic ; Comparative studies ; Decision Making ; Epidemiology ; General pharmacology ; Humans ; Internal Medicine ; Medical sciences ; Pharmacology. Drug treatments ; Priors ; Probability ; Probability distribution ; Reliability ; Reproducibility of Results ; Research Personnel - psychology ; Treatment Outcome ; Validity</subject><ispartof>Journal of clinical epidemiology, 2010-04, Vol.63 (4), p.355-369</ispartof><rights>Elsevier Inc.</rights><rights>2010 Elsevier Inc.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright 2010 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c546t-574d687c5b037b8bc09271c68ccb93726ddbc835483040990d8bf15568e4bd043</citedby><cites>FETCH-LOGICAL-c546t-574d687c5b037b8bc09271c68ccb93726ddbc835483040990d8bf15568e4bd043</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1033203258?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22509993$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19716263$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Johnson, Sindhu R</creatorcontrib><creatorcontrib>Tomlinson, George A</creatorcontrib><creatorcontrib>Hawker, Gillian A</creatorcontrib><creatorcontrib>Granton, John T</creatorcontrib><creatorcontrib>Feldman, Brian M</creatorcontrib><title>Methods to elicit beliefs for Bayesian priors: a systematic review</title><title>Journal of clinical epidemiology</title><addtitle>J Clin Epidemiol</addtitle><description>Abstract Objective Bayesian analysis can incorporate clinicians' beliefs about treatment effectiveness into models that estimate treatment effects. Many elicitation methods are available, but it is unclear if any confer advantages based on principles of measurement science. We review belief-elicitation methods for Bayesian analysis and determine if any of them had an incremental value over the others based on its validity, reliability, and responsiveness. Study Design and Setting A systematic review was performed. MEDLINE, EMBASE, CINAHL, Health and Psychosocial Instruments, Current Index to Statistics, MathSciNet, and Zentralblatt Math were searched using the terms (prior OR prior probability distribution) AND (beliefs OR elicitation) AND (Bayes OR Bayesian). Studies were evaluated on: design, question stem, response options, analysis, consideration of validity, reliability, and responsiveness. Results We identified 33 studies describing methods for elicitation in a Bayesian context. Elicitation occurred in cross-sectional studies ( n = 30, 89%), to derive point estimates with individual-level variation ( n = 19; 58%). Although 64% ( n = 21) considered validity, 24% ( n = 8) reliability, 12% ( n = 4) responsiveness of the elicitation methods, only 12% ( n = 4) formally tested validity, 6% ( n = 2) tested reliability, and none tested responsiveness. Conclusions We have summarized methods of belief elicitation for Bayesian priors. The validity, reliability, and responsiveness of elicitation methods have been infrequently evaluated. Until comparative studies are performed, strategies to reduce the effects of bias on the elicitation should be used.</description><subject>Bayes Theorem</subject><subject>Bayesian</subject><subject>Belief elicitation</subject><subject>Bias</subject><subject>Biological and medical sciences</subject><subject>Clinical trial. Drug monitoring</subject><subject>Clinical Trials as Topic</subject><subject>Comparative studies</subject><subject>Decision Making</subject><subject>Epidemiology</subject><subject>General pharmacology</subject><subject>Humans</subject><subject>Internal Medicine</subject><subject>Medical sciences</subject><subject>Pharmacology. Drug treatments</subject><subject>Priors</subject><subject>Probability</subject><subject>Probability distribution</subject><subject>Reliability</subject><subject>Reproducibility of Results</subject><subject>Research Personnel - psychology</subject><subject>Treatment Outcome</subject><subject>Validity</subject><issn>0895-4356</issn><issn>1878-5921</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkk1v1DAQQC0EokvhL1SREOKUMLbjLw4IWrWAVMQBOFuOMxEO2Xixs6D99zjahUq94Mtc3oxn3gwhFxQaClS-GpvRT2HGXWgYgGlANgD8AdlQrXQtDKMPyQa0EXXLhTwjT3IeAagCJR6TM2oUlUzyDbn8hMv32OdqiRVOwYel6krEIVdDTNWlO2AObq52KcSUX1euyoe84NYtwVcJfwX8_ZQ8GtyU8dkpnpNvN9dfrz7Ut5_ff7x6d1t70cqlFqrtpVZedMBVpzsPhinqpfa-M1wx2fed11y0mkMLxkCvu4EKITW2XQ8tPycvj3V3Kf7cY17sNmSP0-RmjPtsFS-PARWFfH6PHOM-zaU5S2FlOBO6UPJI-RRzTjjYMuTWpUOB7CrZjvavZLtKtiBtkVwSL07l990W-7u0k9UCvDgBLns3DcnNPuR_HGOizGdW7u2Rw6KtqEw2-4Czxz4k9IvtY_h_L2_ulVipUH79gWV1d3PbzCzYL-tJrBcBZr0GYfgf_puwkQ</recordid><startdate>20100401</startdate><enddate>20100401</enddate><creator>Johnson, Sindhu R</creator><creator>Tomlinson, George A</creator><creator>Hawker, Gillian A</creator><creator>Granton, John T</creator><creator>Feldman, Brian M</creator><general>Elsevier Inc</general><general>Elsevier</general><general>Elsevier Limited</general><scope>IQODW</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>3V.</scope><scope>7QL</scope><scope>7QP</scope><scope>7RV</scope><scope>7T2</scope><scope>7T7</scope><scope>7TK</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>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>M7N</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20100401</creationdate><title>Methods to elicit beliefs for Bayesian priors: a systematic review</title><author>Johnson, Sindhu R ; Tomlinson, George A ; Hawker, Gillian A ; Granton, John T ; Feldman, Brian M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c546t-574d687c5b037b8bc09271c68ccb93726ddbc835483040990d8bf15568e4bd043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Bayes Theorem</topic><topic>Bayesian</topic><topic>Belief elicitation</topic><topic>Bias</topic><topic>Biological and medical sciences</topic><topic>Clinical trial. Drug monitoring</topic><topic>Clinical Trials as Topic</topic><topic>Comparative studies</topic><topic>Decision Making</topic><topic>Epidemiology</topic><topic>General pharmacology</topic><topic>Humans</topic><topic>Internal Medicine</topic><topic>Medical sciences</topic><topic>Pharmacology. Drug treatments</topic><topic>Priors</topic><topic>Probability</topic><topic>Probability distribution</topic><topic>Reliability</topic><topic>Reproducibility of Results</topic><topic>Research Personnel - psychology</topic><topic>Treatment Outcome</topic><topic>Validity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Johnson, Sindhu R</creatorcontrib><creatorcontrib>Tomlinson, George A</creatorcontrib><creatorcontrib>Hawker, Gillian A</creatorcontrib><creatorcontrib>Granton, John T</creatorcontrib><creatorcontrib>Feldman, Brian M</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of clinical epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Johnson, Sindhu R</au><au>Tomlinson, George A</au><au>Hawker, Gillian A</au><au>Granton, John T</au><au>Feldman, Brian M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Methods to elicit beliefs for Bayesian priors: a systematic review</atitle><jtitle>Journal of clinical epidemiology</jtitle><addtitle>J Clin Epidemiol</addtitle><date>2010-04-01</date><risdate>2010</risdate><volume>63</volume><issue>4</issue><spage>355</spage><epage>369</epage><pages>355-369</pages><issn>0895-4356</issn><eissn>1878-5921</eissn><abstract>Abstract Objective Bayesian analysis can incorporate clinicians' beliefs about treatment effectiveness into models that estimate treatment effects. Many elicitation methods are available, but it is unclear if any confer advantages based on principles of measurement science. We review belief-elicitation methods for Bayesian analysis and determine if any of them had an incremental value over the others based on its validity, reliability, and responsiveness. Study Design and Setting A systematic review was performed. MEDLINE, EMBASE, CINAHL, Health and Psychosocial Instruments, Current Index to Statistics, MathSciNet, and Zentralblatt Math were searched using the terms (prior OR prior probability distribution) AND (beliefs OR elicitation) AND (Bayes OR Bayesian). Studies were evaluated on: design, question stem, response options, analysis, consideration of validity, reliability, and responsiveness. Results We identified 33 studies describing methods for elicitation in a Bayesian context. Elicitation occurred in cross-sectional studies ( n = 30, 89%), to derive point estimates with individual-level variation ( n = 19; 58%). Although 64% ( n = 21) considered validity, 24% ( n = 8) reliability, 12% ( n = 4) responsiveness of the elicitation methods, only 12% ( n = 4) formally tested validity, 6% ( n = 2) tested reliability, and none tested responsiveness. Conclusions We have summarized methods of belief elicitation for Bayesian priors. The validity, reliability, and responsiveness of elicitation methods have been infrequently evaluated. Until comparative studies are performed, strategies to reduce the effects of bias on the elicitation should be used.</abstract><cop>New York, NY</cop><pub>Elsevier Inc</pub><pmid>19716263</pmid><doi>10.1016/j.jclinepi.2009.06.003</doi><tpages>15</tpages></addata></record> |
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| subjects | Bayes Theorem Bayesian Belief elicitation Bias Biological and medical sciences Clinical trial. Drug monitoring Clinical Trials as Topic Comparative studies Decision Making Epidemiology General pharmacology Humans Internal Medicine Medical sciences Pharmacology. Drug treatments Priors Probability Probability distribution Reliability Reproducibility of Results Research Personnel - psychology Treatment Outcome Validity |
| title | Methods to elicit beliefs for Bayesian priors: a systematic review |
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