A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies
Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to...
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Veröffentlicht in: | Journal of biopharmaceutical statistics 2015-09, Vol.25 (5), p.1077-1099 |
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creator | Ganjali, M. Baghfalaki, T. |
description | Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset. |
doi_str_mv | 10.1080/10543406.2014.971584 |
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Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. 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Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.</description><subject>Acquired Immunodeficiency Syndrome - diagnosis</subject><subject>Acquired Immunodeficiency Syndrome - epidemiology</subject><subject>Acquired Immunodeficiency Syndrome - mortality</subject><subject>Acquired Immunodeficiency Syndrome - therapy</subject><subject>Algorithms</subject><subject>Biomedical Research - statistics & numerical data</subject><subject>Clinical trials</subject><subject>Computer Simulation</subject><subject>Copula models</subject><subject>Data Interpretation, Statistical</subject><subject>Disease Progression</subject><subject>Expectation-maximization algorithm</subject><subject>Fittings</subject><subject>Gaussian</subject><subject>HIV Long-Term Survivors - statistics & numerical data</subject><subject>Humans</subject><subject>Kaplan-Meier Estimate</subject><subject>Longitudinal model</subject><subject>Longitudinal Studies</subject><subject>Mathematical models</subject><subject>Measurement</subject><subject>Models, Statistical</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo simulation</subject><subject>Non-ignorability</subject><subject>Normal distribution</subject><subject>Numerical Analysis, Computer-Assisted</subject><subject>Parameter estimation</subject><subject>Prognosis</subject><subject>Regression</subject><subject>Research Design - statistics & numerical data</subject><subject>Shared parameter model</subject><subject>Statistics</subject><subject>Survival</subject><subject>Time Factors</subject><subject>Time to event model</subject><issn>1054-3406</issn><issn>1520-5711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkstu1DAUhiMEohd4A4QssWGTwXcnKzQaChTNiMW068jjnLSuEnuwnV54mb4qTtOyYIG6sn38_ee3j_6ieEfwguAKfyJYcMaxXFBM-KJWRFT8RXFIBMWlUIS8zPuMlBNzUBzFeIUxEarir4sDKpjKMnVY3C_Ryu_HXqPlfh-8NpcoefTDW5fQxrfQW3eBfIfW3l3YNLbW6R5tQMcxwAAuRaRdi7ZjuLbX-ebMDhDReZxUG-8SoJUOvUcnt3swSSfrXbnRt3awvx8O6Mamy8m6t2YuZPfl6Zct2k5mEN8UrzrdR3j7uB4X519Pzlbfy_XPb6er5bo0gqpUCikZ10QT6JiWQDqBW05JVSuOaUcZMKPb_GsQFHZGiooaBbDTqha6Nm3NjouPc988hF8jxNQMNhroe-3Aj7EhSlKS54fpM1CqpMKVqp6BYibrmhCZ0Q__oFd-DHnaDxRnFZd0eiafKRN8jAG6Zh_soMNdQ3AzxaJ5ikUzxaKZY5Fl7x-bj7sB2r-ipxxk4PMMWNf5MOgbH_q2Sfqu96EL2hkbG_Zfiz_Dpsat</recordid><startdate>20150903</startdate><enddate>20150903</enddate><creator>Ganjali, M.</creator><creator>Baghfalaki, T.</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</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>7X8</scope><scope>7U9</scope><scope>H94</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20150903</creationdate><title>A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies</title><author>Ganjali, M. ; Baghfalaki, T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c527t-56634a1a1ef3a6e1f50d421897402f23e3cad253e52ebc6582c7eeba795a9cd93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Acquired Immunodeficiency Syndrome - diagnosis</topic><topic>Acquired Immunodeficiency Syndrome - epidemiology</topic><topic>Acquired Immunodeficiency Syndrome - mortality</topic><topic>Acquired Immunodeficiency Syndrome - therapy</topic><topic>Algorithms</topic><topic>Biomedical Research - statistics & numerical data</topic><topic>Clinical trials</topic><topic>Computer Simulation</topic><topic>Copula models</topic><topic>Data Interpretation, Statistical</topic><topic>Disease Progression</topic><topic>Expectation-maximization algorithm</topic><topic>Fittings</topic><topic>Gaussian</topic><topic>HIV Long-Term Survivors - statistics & numerical data</topic><topic>Humans</topic><topic>Kaplan-Meier Estimate</topic><topic>Longitudinal model</topic><topic>Longitudinal Studies</topic><topic>Mathematical models</topic><topic>Measurement</topic><topic>Models, Statistical</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo methods</topic><topic>Monte Carlo simulation</topic><topic>Non-ignorability</topic><topic>Normal distribution</topic><topic>Numerical Analysis, Computer-Assisted</topic><topic>Parameter estimation</topic><topic>Prognosis</topic><topic>Regression</topic><topic>Research Design - statistics & numerical data</topic><topic>Shared parameter model</topic><topic>Statistics</topic><topic>Survival</topic><topic>Time Factors</topic><topic>Time to event model</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ganjali, M.</creatorcontrib><creatorcontrib>Baghfalaki, T.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Virology and AIDS Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science 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><jtitle>Journal of biopharmaceutical statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ganjali, M.</au><au>Baghfalaki, T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies</atitle><jtitle>Journal of biopharmaceutical statistics</jtitle><addtitle>J Biopharm Stat</addtitle><date>2015-09-03</date><risdate>2015</risdate><volume>25</volume><issue>5</issue><spage>1077</spage><epage>1099</epage><pages>1077-1099</pages><issn>1054-3406</issn><eissn>1520-5711</eissn><abstract>Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.</abstract><cop>England</cop><pub>Taylor & Francis</pub><pmid>25372017</pmid><doi>10.1080/10543406.2014.971584</doi><tpages>23</tpages></addata></record> |
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subjects | Acquired Immunodeficiency Syndrome - diagnosis Acquired Immunodeficiency Syndrome - epidemiology Acquired Immunodeficiency Syndrome - mortality Acquired Immunodeficiency Syndrome - therapy Algorithms Biomedical Research - statistics & numerical data Clinical trials Computer Simulation Copula models Data Interpretation, Statistical Disease Progression Expectation-maximization algorithm Fittings Gaussian HIV Long-Term Survivors - statistics & numerical data Humans Kaplan-Meier Estimate Longitudinal model Longitudinal Studies Mathematical models Measurement Models, Statistical Monte Carlo Method Monte Carlo methods Monte Carlo simulation Non-ignorability Normal distribution Numerical Analysis, Computer-Assisted Parameter estimation Prognosis Regression Research Design - statistics & numerical data Shared parameter model Statistics Survival Time Factors Time to event model |
title | A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies |
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