Bayesian joint modeling for assessing the progression of chronic kidney disease in children
Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity...
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| Veröffentlicht in: | Statistical methods in medical research 2018-01, Vol.27 (1), p.298-311 |
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| container_title | Statistical methods in medical research |
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| creator | Armero, Carmen Forte, Anabel Perpiñán, Hèctor Sanahuja, María José Agustí, Silvia |
| description | Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate. |
| doi_str_mv | 10.1177/0962280216628560 |
| format | Article |
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The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate.</description><subject>Bayesian analysis</subject><subject>Children</subject><subject>Competing risks models</subject><subject>Correlation analysis</subject><subject>Dropping out</subject><subject>Error analysis</subject><subject>Evolution</subject><subject>Kidney diseases</subject><subject>Measurement</subject><subject>Missing data</subject><issn>0962-2802</issn><issn>1477-0334</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>7QJ</sourceid><recordid>eNp1kM1LAzEQxYMotlbvniTgxctqPjbZzVGLX1DwoicPS5rMtqnbpCbtof-9WVpFBE_D8H7zZuYhdE7JNaVVdUOUZKwmjErJaiHJARrSsqoKwnl5iIa9XPT6AJ2ktCCEVKRUx2jApKprxfkQvd_pLSSnPV4E59d4GSx0zs9wGyLWKUFKfbeeA17FMIt9HzwOLTbzGLwz-MNZD1tsXQKdADufFdfZCP4UHbW6S3C2ryP09nD_On4qJi-Pz-PbSWG4FOtCM0UUlVPFKlbWwggprDEltJW1isvSKABDajaVSky1Ap4pqoQlsmRGipKP0NXON1_4uYG0bpYuGeg67SFsUkNrJqUkPH88Qpd_0EXYRJ-va6iqKyVUts4U2VEmhpQitM0quqWO24aSpg---Rt8HrnYG2-mS7A_A99JZ6DYAUnP4NfW_wy_AKRrib0</recordid><startdate>201801</startdate><enddate>201801</enddate><creator>Armero, Carmen</creator><creator>Forte, Anabel</creator><creator>Perpiñán, Hèctor</creator><creator>Sanahuja, María José</creator><creator>Agustí, Silvia</creator><general>SAGE Publications</general><general>Sage Publications Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QJ</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>201801</creationdate><title>Bayesian joint modeling for assessing the progression of chronic kidney disease in children</title><author>Armero, Carmen ; Forte, Anabel ; Perpiñán, Hèctor ; Sanahuja, María José ; Agustí, Silvia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c365t-a290916b9272485c565dcc4ef7dd9364c9eec082b695ba9e3248195d0642c6543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Bayesian analysis</topic><topic>Children</topic><topic>Competing risks models</topic><topic>Correlation analysis</topic><topic>Dropping out</topic><topic>Error analysis</topic><topic>Evolution</topic><topic>Kidney diseases</topic><topic>Measurement</topic><topic>Missing data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Armero, Carmen</creatorcontrib><creatorcontrib>Forte, Anabel</creatorcontrib><creatorcontrib>Perpiñán, Hèctor</creatorcontrib><creatorcontrib>Sanahuja, María José</creatorcontrib><creatorcontrib>Agustí, Silvia</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</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>MEDLINE - Academic</collection><jtitle>Statistical methods in medical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Armero, Carmen</au><au>Forte, Anabel</au><au>Perpiñán, Hèctor</au><au>Sanahuja, María José</au><au>Agustí, Silvia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian joint modeling for assessing the progression of chronic kidney disease in children</atitle><jtitle>Statistical methods in medical research</jtitle><addtitle>Stat Methods Med Res</addtitle><date>2018-01</date><risdate>2018</risdate><volume>27</volume><issue>1</issue><spage>298</spage><epage>311</epage><pages>298-311</pages><issn>0962-2802</issn><eissn>1477-0334</eissn><abstract>Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. 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| ispartof | Statistical methods in medical research, 2018-01, Vol.27 (1), p.298-311 |
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| language | eng |
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| source | SAGE Complete; Applied Social Sciences Index & Abstracts (ASSIA) |
| subjects | Bayesian analysis Children Competing risks models Correlation analysis Dropping out Error analysis Evolution Kidney diseases Measurement Missing data |
| title | Bayesian joint modeling for assessing the progression of chronic kidney disease in children |
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