A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention
Purpose. To conduct a cost-effectiveness analysis of HIV prevention when costs and effects cannot be measured directly. To quantify the total estimation of uncertainty due to sampling variability as well as inexact knowledge of HIV transmission parameters. Methods. The authors focus on estimating th...
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| Veröffentlicht in: | Medical decision making 2004-11, Vol.24 (6), p.634-653 |
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| container_title | Medical decision making |
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| creator | Johnson-Masotti, Ana P. Laud, Purushottam W. Hoffmann, Raymond G. Hayat, Matthew J. Pinkerton, Steven D. |
| description | Purpose.
To conduct a cost-effectiveness analysis of HIV prevention when costs and effects cannot be measured directly. To quantify the total estimation of uncertainty due to sampling variability as well as inexact knowledge of HIV transmission parameters.
Methods.
The authors focus on estimating the incremental net health benefit (INHB) in a randomized trial of HIV prevention with intervention and control conditions. Using a Bernoulli model of HIV transmission, changes in the participants’ risk behaviors are converted into the number of HIV infections averted. A sampling model is used to account for variation in the behavior measurements. Bayes’s theorem and Monte Carlo methods are used to attain the stated objectives.
Results.
The authors obtained a positive mean INHB of 0.0008, indicating that advocacy training is just slightly favored over the control condition for men, assuming a $50,000 per quality-adjusted life year (QALY) threshold. To be confident of a positive INHB, the decision maker would need to spend more than $100,000 per QALY. |
| doi_str_mv | 10.1177/0272989X04271040 |
| format | Article |
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To conduct a cost-effectiveness analysis of HIV prevention when costs and effects cannot be measured directly. To quantify the total estimation of uncertainty due to sampling variability as well as inexact knowledge of HIV transmission parameters.
Methods.
The authors focus on estimating the incremental net health benefit (INHB) in a randomized trial of HIV prevention with intervention and control conditions. Using a Bernoulli model of HIV transmission, changes in the participants’ risk behaviors are converted into the number of HIV infections averted. A sampling model is used to account for variation in the behavior measurements. Bayes’s theorem and Monte Carlo methods are used to attain the stated objectives.
Results.
The authors obtained a positive mean INHB of 0.0008, indicating that advocacy training is just slightly favored over the control condition for men, assuming a $50,000 per quality-adjusted life year (QALY) threshold. To be confident of a positive INHB, the decision maker would need to spend more than $100,000 per QALY.</description><identifier>ISSN: 0272-989X</identifier><identifier>EISSN: 1552-681X</identifier><identifier>DOI: 10.1177/0272989X04271040</identifier><identifier>PMID: 15534344</identifier><language>eng</language><publisher>Thousand Oaks, CA: Sage Publications</publisher><subject>Bayes Theorem ; Cost-Benefit Analysis ; Decision Support Techniques ; Female ; HIV Infections - economics ; HIV Infections - prevention & control ; HIV Infections - transmission ; Humans ; Male ; Monte Carlo Method ; Patient Education as Topic ; Quality-Adjusted Life Years ; Randomized Controlled Trials as Topic ; Risk-Taking ; Uncertainty</subject><ispartof>Medical decision making, 2004-11, Vol.24 (6), p.634-653</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c302t-9d54fff44c49b1a100b431b74fd961a130a5dbef76180f2075c59ba74e49ecfc3</citedby><cites>FETCH-LOGICAL-c302t-9d54fff44c49b1a100b431b74fd961a130a5dbef76180f2075c59ba74e49ecfc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/0272989X04271040$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/0272989X04271040$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,776,780,21798,27901,27902,43597,43598</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15534344$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Johnson-Masotti, Ana P.</creatorcontrib><creatorcontrib>Laud, Purushottam W.</creatorcontrib><creatorcontrib>Hoffmann, Raymond G.</creatorcontrib><creatorcontrib>Hayat, Matthew J.</creatorcontrib><creatorcontrib>Pinkerton, Steven D.</creatorcontrib><title>A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention</title><title>Medical decision making</title><addtitle>Med Decis Making</addtitle><description>Purpose.
To conduct a cost-effectiveness analysis of HIV prevention when costs and effects cannot be measured directly. To quantify the total estimation of uncertainty due to sampling variability as well as inexact knowledge of HIV transmission parameters.
Methods.
The authors focus on estimating the incremental net health benefit (INHB) in a randomized trial of HIV prevention with intervention and control conditions. Using a Bernoulli model of HIV transmission, changes in the participants’ risk behaviors are converted into the number of HIV infections averted. A sampling model is used to account for variation in the behavior measurements. Bayes’s theorem and Monte Carlo methods are used to attain the stated objectives.
Results.
The authors obtained a positive mean INHB of 0.0008, indicating that advocacy training is just slightly favored over the control condition for men, assuming a $50,000 per quality-adjusted life year (QALY) threshold. To be confident of a positive INHB, the decision maker would need to spend more than $100,000 per QALY.</description><subject>Bayes Theorem</subject><subject>Cost-Benefit Analysis</subject><subject>Decision Support Techniques</subject><subject>Female</subject><subject>HIV Infections - economics</subject><subject>HIV Infections - prevention & control</subject><subject>HIV Infections - transmission</subject><subject>Humans</subject><subject>Male</subject><subject>Monte Carlo Method</subject><subject>Patient Education as Topic</subject><subject>Quality-Adjusted Life Years</subject><subject>Randomized Controlled Trials as Topic</subject><subject>Risk-Taking</subject><subject>Uncertainty</subject><issn>0272-989X</issn><issn>1552-681X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kF9LwzAUxYMobk7ffZJ8gepNmy6Nb52oG8w_Dyp7K2l6s3V0aUk6Yd_elg4EwafL4ZzfgXsIuWZwy5gQdxCKUCZyBTwUDDickDGL4zCYJmx1Ssa9HfT-iFx4vwVgXCb8nIy6UMQjzsfEpHSmDuhLZWnaNK5WekPbmr5iS-eoqnZDZ2jRlK2_p6mli6ra-9aptqwtVbbooarUg-64l7rAqrRrOl980XeH32h765KcGVV5vDreCfl8evx4mAfLt-fFQ7oMdARhG8gi5sYYzjWXOVMMIOcRywU3hZx2OgIVFzkaMWUJmBBErGOZK8GRS9RGRxMCQ692tfcOTda4cqfcIWOQ9ZNlfyfrkJsBafb5Dotf4LhRFwiGgFdrzLb13tnuhf8LfwCQunQB</recordid><startdate>200411</startdate><enddate>200411</enddate><creator>Johnson-Masotti, Ana P.</creator><creator>Laud, Purushottam W.</creator><creator>Hoffmann, Raymond G.</creator><creator>Hayat, Matthew J.</creator><creator>Pinkerton, Steven D.</creator><general>Sage Publications</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></search><sort><creationdate>200411</creationdate><title>A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention</title><author>Johnson-Masotti, Ana P. ; Laud, Purushottam W. ; Hoffmann, Raymond G. ; Hayat, Matthew J. ; Pinkerton, Steven D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c302t-9d54fff44c49b1a100b431b74fd961a130a5dbef76180f2075c59ba74e49ecfc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Bayes Theorem</topic><topic>Cost-Benefit Analysis</topic><topic>Decision Support Techniques</topic><topic>Female</topic><topic>HIV Infections - economics</topic><topic>HIV Infections - prevention & control</topic><topic>HIV Infections - transmission</topic><topic>Humans</topic><topic>Male</topic><topic>Monte Carlo Method</topic><topic>Patient Education as Topic</topic><topic>Quality-Adjusted Life Years</topic><topic>Randomized Controlled Trials as Topic</topic><topic>Risk-Taking</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Johnson-Masotti, Ana P.</creatorcontrib><creatorcontrib>Laud, Purushottam W.</creatorcontrib><creatorcontrib>Hoffmann, Raymond G.</creatorcontrib><creatorcontrib>Hayat, Matthew J.</creatorcontrib><creatorcontrib>Pinkerton, Steven D.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><jtitle>Medical decision making</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Johnson-Masotti, Ana P.</au><au>Laud, Purushottam W.</au><au>Hoffmann, Raymond G.</au><au>Hayat, Matthew J.</au><au>Pinkerton, Steven D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention</atitle><jtitle>Medical decision making</jtitle><addtitle>Med Decis Making</addtitle><date>2004-11</date><risdate>2004</risdate><volume>24</volume><issue>6</issue><spage>634</spage><epage>653</epage><pages>634-653</pages><issn>0272-989X</issn><eissn>1552-681X</eissn><abstract>Purpose.
To conduct a cost-effectiveness analysis of HIV prevention when costs and effects cannot be measured directly. To quantify the total estimation of uncertainty due to sampling variability as well as inexact knowledge of HIV transmission parameters.
Methods.
The authors focus on estimating the incremental net health benefit (INHB) in a randomized trial of HIV prevention with intervention and control conditions. Using a Bernoulli model of HIV transmission, changes in the participants’ risk behaviors are converted into the number of HIV infections averted. A sampling model is used to account for variation in the behavior measurements. Bayes’s theorem and Monte Carlo methods are used to attain the stated objectives.
Results.
The authors obtained a positive mean INHB of 0.0008, indicating that advocacy training is just slightly favored over the control condition for men, assuming a $50,000 per quality-adjusted life year (QALY) threshold. To be confident of a positive INHB, the decision maker would need to spend more than $100,000 per QALY.</abstract><cop>Thousand Oaks, CA</cop><pub>Sage Publications</pub><pmid>15534344</pmid><doi>10.1177/0272989X04271040</doi><tpages>20</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0272-989X |
| ispartof | Medical decision making, 2004-11, Vol.24 (6), p.634-653 |
| issn | 0272-989X 1552-681X |
| language | eng |
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| source | MEDLINE; SAGE Complete |
| subjects | Bayes Theorem Cost-Benefit Analysis Decision Support Techniques Female HIV Infections - economics HIV Infections - prevention & control HIV Infections - transmission Humans Male Monte Carlo Method Patient Education as Topic Quality-Adjusted Life Years Randomized Controlled Trials as Topic Risk-Taking Uncertainty |
| title | A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention |
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