Hierarchical likelihood inference on clustered competing risks data
The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events...
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| Veröffentlicht in: | Statistics in medicine 2016-01, Vol.35 (2), p.251-267 |
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| creator | Christian, Nicholas J. Ha, Il Do Jeong, Jong-Hyeon |
| description | The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchical likelihood method is proposed to infer the cause‐specific hazard frailty model for clustered competing risks data. The hierarchical likelihood incorporates fixed effects as well as random effects into an extended likelihood function, so that the method does not require intensive numerical methods to find the marginal distribution. Simulation studies are performed to assess the behavior of the estimators for the regression coefficients and the correlation structure among the bivariate frailty distribution for competing events. The proposed method is illustrated with a breast cancer dataset. Copyright © 2015 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/sim.6628 |
| format | Article |
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However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchical likelihood method is proposed to infer the cause‐specific hazard frailty model for clustered competing risks data. The hierarchical likelihood incorporates fixed effects as well as random effects into an extended likelihood function, so that the method does not require intensive numerical methods to find the marginal distribution. Simulation studies are performed to assess the behavior of the estimators for the regression coefficients and the correlation structure among the bivariate frailty distribution for competing events. The proposed method is illustrated with a breast cancer dataset. Copyright © 2015 John Wiley & Sons, Ltd.</description><identifier>ISSN: 0277-6715</identifier><identifier>EISSN: 1097-0258</identifier><identifier>DOI: 10.1002/sim.6628</identifier><identifier>PMID: 26278918</identifier><identifier>CODEN: SMEDDA</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Biostatistics - methods ; Breast Neoplasms - drug therapy ; cause-specific hazard ; Clinical Trials, Phase III as Topic - statistics & numerical data ; clustered data ; competing risks ; Computer Simulation ; Correlation analysis ; Databases, Factual ; Female ; Frailty ; frailty models ; hierarchical likelihood ; Humans ; Likelihood Functions ; Markov Chains ; Models, Statistical ; Monte Carlo Method ; Proportional Hazards Models ; Randomized Controlled Trials as Topic - statistics & numerical data ; Regression Analysis ; Risk ; Simulation ; Statistical inference ; Survival analysis</subject><ispartof>Statistics in medicine, 2016-01, Vol.35 (2), p.251-267</ispartof><rights>Copyright © 2015 John Wiley & Sons, Ltd.</rights><rights>Copyright Wiley Subscription Services, Inc. Jan 30, 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c6128-23f32a3b308f70624b7446642cac8aa7c746323163739a480100725d5437a873</citedby><cites>FETCH-LOGICAL-c6128-23f32a3b308f70624b7446642cac8aa7c746323163739a480100725d5437a873</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fsim.6628$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fsim.6628$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26278918$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Christian, Nicholas J.</creatorcontrib><creatorcontrib>Ha, Il Do</creatorcontrib><creatorcontrib>Jeong, Jong-Hyeon</creatorcontrib><title>Hierarchical likelihood inference on clustered competing risks data</title><title>Statistics in medicine</title><addtitle>Statist. Med</addtitle><description>The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchical likelihood method is proposed to infer the cause‐specific hazard frailty model for clustered competing risks data. The hierarchical likelihood incorporates fixed effects as well as random effects into an extended likelihood function, so that the method does not require intensive numerical methods to find the marginal distribution. Simulation studies are performed to assess the behavior of the estimators for the regression coefficients and the correlation structure among the bivariate frailty distribution for competing events. The proposed method is illustrated with a breast cancer dataset. Copyright © 2015 John Wiley & Sons, Ltd.</description><subject>Algorithms</subject><subject>Biostatistics - methods</subject><subject>Breast Neoplasms - drug therapy</subject><subject>cause-specific hazard</subject><subject>Clinical Trials, Phase III as Topic - statistics & numerical data</subject><subject>clustered data</subject><subject>competing risks</subject><subject>Computer Simulation</subject><subject>Correlation analysis</subject><subject>Databases, Factual</subject><subject>Female</subject><subject>Frailty</subject><subject>frailty models</subject><subject>hierarchical likelihood</subject><subject>Humans</subject><subject>Likelihood Functions</subject><subject>Markov Chains</subject><subject>Models, Statistical</subject><subject>Monte Carlo Method</subject><subject>Proportional Hazards Models</subject><subject>Randomized Controlled Trials as Topic - statistics & numerical data</subject><subject>Regression Analysis</subject><subject>Risk</subject><subject>Simulation</subject><subject>Statistical inference</subject><subject>Survival analysis</subject><issn>0277-6715</issn><issn>1097-0258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kV1LHDEUQIO01K0W_AUy0Je-jM33zb4IslgV_EBc8DFkMxk3bmayJjNt_feNuF200KcQcjjcm4PQAcFHBGP6PfvuSEqqdtCE4CnUmAr1AU0wBaglELGLPuf8iDEhgsIntEslBTUlaoJm594lk-zSWxOq4Fcu-GWMTeX71iXXW1fFvrJhzEO5NpWN3doNvn-oks-rXDVmMPvoY2tCdl825x6a_zidz87ry5uzi9nJZW0loaqmrGXUsAXDqgUsKV8A51Jyao1VxoAFLhllRDJgU8MVLqsBFY3gDIwCtoeOX7XrcdG5xrp-SCbodfKdSc86Gq_fv_R-qR_iTy0ACOeiCL5tBCk-jS4PuvPZuhBM7-KYNQGJVYEpLejXf9DHOKa-bFcogTl7L7Qp5pxcux2GYP0SRpcw-iVMQQ_fDr8F_5YoQP0K_PLBPf9XpO8urjbCDe9Lmd9b3qSVlsBA6PvrM31bPv76jgs9Z38ApAOkwg</recordid><startdate>20160130</startdate><enddate>20160130</enddate><creator>Christian, Nicholas J.</creator><creator>Ha, Il Do</creator><creator>Jeong, Jong-Hyeon</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</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>K9.</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160130</creationdate><title>Hierarchical likelihood inference on clustered competing risks data</title><author>Christian, Nicholas J. ; Ha, Il Do ; Jeong, Jong-Hyeon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6128-23f32a3b308f70624b7446642cac8aa7c746323163739a480100725d5437a873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Biostatistics - methods</topic><topic>Breast Neoplasms - drug therapy</topic><topic>cause-specific hazard</topic><topic>Clinical Trials, Phase III as Topic - statistics & numerical data</topic><topic>clustered data</topic><topic>competing risks</topic><topic>Computer Simulation</topic><topic>Correlation analysis</topic><topic>Databases, Factual</topic><topic>Female</topic><topic>Frailty</topic><topic>frailty models</topic><topic>hierarchical likelihood</topic><topic>Humans</topic><topic>Likelihood Functions</topic><topic>Markov Chains</topic><topic>Models, Statistical</topic><topic>Monte Carlo Method</topic><topic>Proportional Hazards Models</topic><topic>Randomized Controlled Trials as Topic - statistics & numerical data</topic><topic>Regression Analysis</topic><topic>Risk</topic><topic>Simulation</topic><topic>Statistical inference</topic><topic>Survival analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Christian, Nicholas J.</creatorcontrib><creatorcontrib>Ha, Il Do</creatorcontrib><creatorcontrib>Jeong, Jong-Hyeon</creatorcontrib><collection>Istex</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 Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Statistics in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Christian, Nicholas J.</au><au>Ha, Il Do</au><au>Jeong, Jong-Hyeon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hierarchical likelihood inference on clustered competing risks data</atitle><jtitle>Statistics in medicine</jtitle><addtitle>Statist. Med</addtitle><date>2016-01-30</date><risdate>2016</risdate><volume>35</volume><issue>2</issue><spage>251</spage><epage>267</epage><pages>251-267</pages><issn>0277-6715</issn><eissn>1097-0258</eissn><coden>SMEDDA</coden><abstract>The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchical likelihood method is proposed to infer the cause‐specific hazard frailty model for clustered competing risks data. The hierarchical likelihood incorporates fixed effects as well as random effects into an extended likelihood function, so that the method does not require intensive numerical methods to find the marginal distribution. Simulation studies are performed to assess the behavior of the estimators for the regression coefficients and the correlation structure among the bivariate frailty distribution for competing events. The proposed method is illustrated with a breast cancer dataset. Copyright © 2015 John Wiley & Sons, Ltd.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>26278918</pmid><doi>10.1002/sim.6628</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Statistics in medicine, 2016-01, Vol.35 (2), p.251-267 |
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| subjects | Algorithms Biostatistics - methods Breast Neoplasms - drug therapy cause-specific hazard Clinical Trials, Phase III as Topic - statistics & numerical data clustered data competing risks Computer Simulation Correlation analysis Databases, Factual Female Frailty frailty models hierarchical likelihood Humans Likelihood Functions Markov Chains Models, Statistical Monte Carlo Method Proportional Hazards Models Randomized Controlled Trials as Topic - statistics & numerical data Regression Analysis Risk Simulation Statistical inference Survival analysis |
| title | Hierarchical likelihood inference on clustered competing risks data |
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