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Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor
Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The cha...
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| Published in: | Statistical methods in medical research 2015-12, Vol.24 (6), p.769-787 |
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| container_title | Statistical methods in medical research |
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| creator | van den Hout, Ardo Fox, Jean-Paul Klein Entink, Rinke H |
| description | Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used to screen for cognitive impairment. The illness-death model will be used to identify and to explore the relationship between occasion-specific cognitive function and stroke. Combining a multi-state model with the latent growth model defines a joint model which extends current statistical inference regarding disease progression and cognitive function. Markov chain Monte Carlo methods are used for Bayesian inference. Data stem from the Medical Research Council Cognitive Function and Ageing Study in the UK (1991–2005). |
| doi_str_mv | 10.1177/0962280211426359 |
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An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used to screen for cognitive impairment. The illness-death model will be used to identify and to explore the relationship between occasion-specific cognitive function and stroke. Combining a multi-state model with the latent growth model defines a joint model which extends current statistical inference regarding disease progression and cognitive function. Markov chain Monte Carlo methods are used for Bayesian inference. Data stem from the Medical Research Council Cognitive Function and Ageing Study in the UK (1991–2005).</description><identifier>ISSN: 0962-2802</identifier><identifier>EISSN: 1477-0334</identifier><identifier>DOI: 10.1177/0962280211426359</identifier><identifier>PMID: 22080595</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Aged ; Aging ; Bayes Theorem ; Bayesian analysis ; Candidates ; Cognition ; Cognition & reasoning ; Cognition Disorders - etiology ; Cognition Disorders - mortality ; Cognitive ability ; Cognitive functioning ; Cognitive impairment ; Computer simulation ; Death ; Death & dying ; Economic models ; Historical perspectives ; Humans ; Illnesses ; Markov analysis ; Markov Chains ; Medical research ; Minimental State Examination ; Models, Statistical ; Monte Carlo Method ; Monte Carlo simulation ; Public health ; Random effects ; Risk analysis ; Risk Factors ; Statistical inference ; Statistical methods ; Stroke ; Stroke - complications ; Stroke - mortality ; Time dependence ; Time Factors</subject><ispartof>Statistical methods in medical research, 2015-12, Vol.24 (6), p.769-787</ispartof><rights>The Author(s) 2011</rights><rights>The Author(s) 2011.</rights><rights>The Author(s) 2011 2011 SAGE Publications</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c462t-9d57e82c7a56f795e72bda2aa6566f448d36248c45fdbc8779a3ce87003d9bd53</citedby><cites>FETCH-LOGICAL-c462t-9d57e82c7a56f795e72bda2aa6566f448d36248c45fdbc8779a3ce87003d9bd53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1736556277?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,776,780,881,12825,21373,21374,27901,27902,30976,33588,33589,34507,34508,43709,44091</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22080595$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>van den Hout, Ardo</creatorcontrib><creatorcontrib>Fox, Jean-Paul</creatorcontrib><creatorcontrib>Klein Entink, Rinke H</creatorcontrib><title>Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor</title><title>Statistical methods in medical research</title><addtitle>Stat Methods Med Res</addtitle><description>Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used to screen for cognitive impairment. The illness-death model will be used to identify and to explore the relationship between occasion-specific cognitive function and stroke. Combining a multi-state model with the latent growth model defines a joint model which extends current statistical inference regarding disease progression and cognitive function. Markov chain Monte Carlo methods are used for Bayesian inference. Data stem from the Medical Research Council Cognitive Function and Ageing Study in the UK (1991–2005).</description><subject>Aged</subject><subject>Aging</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Candidates</subject><subject>Cognition</subject><subject>Cognition & reasoning</subject><subject>Cognition Disorders - etiology</subject><subject>Cognition Disorders - mortality</subject><subject>Cognitive ability</subject><subject>Cognitive functioning</subject><subject>Cognitive impairment</subject><subject>Computer simulation</subject><subject>Death</subject><subject>Death & dying</subject><subject>Economic models</subject><subject>Historical perspectives</subject><subject>Humans</subject><subject>Illnesses</subject><subject>Markov analysis</subject><subject>Markov Chains</subject><subject>Medical research</subject><subject>Minimental State Examination</subject><subject>Models, Statistical</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo simulation</subject><subject>Public health</subject><subject>Random effects</subject><subject>Risk analysis</subject><subject>Risk Factors</subject><subject>Statistical inference</subject><subject>Statistical methods</subject><subject>Stroke</subject><subject>Stroke - complications</subject><subject>Stroke - mortality</subject><subject>Time dependence</subject><subject>Time Factors</subject><issn>0962-2802</issn><issn>1477-0334</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><sourceid>7QJ</sourceid><sourceid>ALSLI</sourceid><sourceid>HEHIP</sourceid><sourceid>M2S</sourceid><recordid>eNp1kc2PFCEQxYnRuOPq3ZMh8eKlFWig6IuJbvxKNvGiZ8JA9Sy73TBCj2b_e2ln3aybeCLU-9WjikfIc85ecw7whg1aCMME51LoXg0PyIZLgI71vXxINqvcrfoJeVLrJWMMmBwekxMhmGFqUBuS3rtrrNElGtOIBZNHOuZC18I0Jay1C-iWCzrngNMfqS4lXyH9FVvV512KS8yJukodndyCaaFLnLG17TGF9VpivaKj80suT8mj0U0Vn92cp-T7xw_fzj53518_fTl7d955qcXSDUEBGuHBKT3CoBDENjjhnFZaj1Ka0GshjZdqDFtvAAbXezTAWB-GbVD9KXl79N0ftjMG38YobrL7EmdXrm120f6rpHhhd_mnlVobMLwZvLoxKPnHAeti51g9TpNLmA_VcuhBQvtz09CX99DLfCiprbdSWiktABrFjpQvudaC4-0wnNk1THs_zNby4u4Stw1_02tAdwSq2-GdV_9n-BtoR6hE</recordid><startdate>201512</startdate><enddate>201512</enddate><creator>van den Hout, Ardo</creator><creator>Fox, Jean-Paul</creator><creator>Klein Entink, Rinke H</creator><general>SAGE Publications</general><general>Sage Publications Ltd</general><scope>AFRWT</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>0-V</scope><scope>3V.</scope><scope>7QJ</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>HEHIP</scope><scope>JQ2</scope><scope>K9.</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M2S</scope><scope>M7S</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>POGQB</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PRQQA</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201512</creationdate><title>Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor</title><author>van den Hout, Ardo ; Fox, Jean-Paul ; Klein Entink, Rinke H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c462t-9d57e82c7a56f795e72bda2aa6566f448d36248c45fdbc8779a3ce87003d9bd53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Aged</topic><topic>Aging</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Candidates</topic><topic>Cognition</topic><topic>Cognition & reasoning</topic><topic>Cognition Disorders - etiology</topic><topic>Cognition Disorders - mortality</topic><topic>Cognitive ability</topic><topic>Cognitive functioning</topic><topic>Cognitive impairment</topic><topic>Computer simulation</topic><topic>Death</topic><topic>Death & dying</topic><topic>Economic models</topic><topic>Historical perspectives</topic><topic>Humans</topic><topic>Illnesses</topic><topic>Markov analysis</topic><topic>Markov Chains</topic><topic>Medical research</topic><topic>Minimental State Examination</topic><topic>Models, Statistical</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo simulation</topic><topic>Public health</topic><topic>Random effects</topic><topic>Risk analysis</topic><topic>Risk Factors</topic><topic>Statistical inference</topic><topic>Statistical methods</topic><topic>Stroke</topic><topic>Stroke - complications</topic><topic>Stroke - mortality</topic><topic>Time dependence</topic><topic>Time Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>van den Hout, Ardo</creatorcontrib><creatorcontrib>Fox, Jean-Paul</creatorcontrib><creatorcontrib>Klein Entink, Rinke H</creatorcontrib><collection>SAGE Journals Open Access</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 Social Sciences Premium Collection【Remote access available】</collection><collection>ProQuest Central (Corporate)</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>Sociology Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Science Journals (ProQuest Database)</collection><collection>Sociology Database (ProQuest)</collection><collection>Engineering Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest Health & Medical Research Collection</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest Sociology & Social Sciences Collection</collection><collection>ProQuest One Health & Nursing</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Social Sciences</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Statistical methods in medical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>van den Hout, Ardo</au><au>Fox, Jean-Paul</au><au>Klein Entink, Rinke H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor</atitle><jtitle>Statistical methods in medical research</jtitle><addtitle>Stat Methods Med Res</addtitle><date>2015-12</date><risdate>2015</risdate><volume>24</volume><issue>6</issue><spage>769</spage><epage>787</epage><pages>769-787</pages><issn>0962-2802</issn><eissn>1477-0334</eissn><abstract>Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used to screen for cognitive impairment. The illness-death model will be used to identify and to explore the relationship between occasion-specific cognitive function and stroke. Combining a multi-state model with the latent growth model defines a joint model which extends current statistical inference regarding disease progression and cognitive function. Markov chain Monte Carlo methods are used for Bayesian inference. Data stem from the Medical Research Council Cognitive Function and Ageing Study in the UK (1991–2005).</abstract><cop>London, England</cop><pub>SAGE Publications</pub><pmid>22080595</pmid><doi>10.1177/0962280211426359</doi><tpages>19</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | Applied Social Sciences Index & Abstracts (ASSIA); Social Science Premium Collection (Proquest) (PQ_SDU_P3); SAGE:Jisc Collections:SAGE Journals Read and Publish 2023-2024:2025 extension (reading list); Sociology Collection |
| subjects | Aged Aging Bayes Theorem Bayesian analysis Candidates Cognition Cognition & reasoning Cognition Disorders - etiology Cognition Disorders - mortality Cognitive ability Cognitive functioning Cognitive impairment Computer simulation Death Death & dying Economic models Historical perspectives Humans Illnesses Markov analysis Markov Chains Medical research Minimental State Examination Models, Statistical Monte Carlo Method Monte Carlo simulation Public health Random effects Risk analysis Risk Factors Statistical inference Statistical methods Stroke Stroke - complications Stroke - mortality Time dependence Time Factors |
| title | Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor |
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