Using the Whole Cohort in the Analysis of Case-Cohort Data
Case-cohort data analyses often ignore valuable information on cohort members not sampled as cases or controls. The Atherosclerosis Risk in Communities (ARIC) study investigators, for example, typically report data for just the 10%-15% of subjects sampled for substudies of their cohort of 15,972 par...
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| Veröffentlicht in: | American journal of epidemiology 2009-06, Vol.169 (11), p.1398-1405 |
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| container_title | American journal of epidemiology |
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| creator | Breslow, Norman E. Lumley, Thomas Ballantyne, Christie M. Chambless, Lloyd E. Kulich, Michal |
| description | Case-cohort data analyses often ignore valuable information on cohort members not sampled as cases or controls. The Atherosclerosis Risk in Communities (ARIC) study investigators, for example, typically report data for just the 10%-15% of subjects sampled for substudies of their cohort of 15,972 participants. Remaining subjects contribute to stratified sampling weights only. Analysis methods implemented in the freely available R statistical system (http://cran.r-project.org/) make better use of the data through adjustment of the sampling weights via calibration or estimation. By reanalyzing data from an ARIC study of coronary heart disease and simulations based on data from the National Wilms Tumor Study, the authors demonstrate that such adjustment can dramatically improve the precision of hazard ratios estimated for baseline covariates known for all subjects. Adjustment can also improve precision for partially missing covariates, those known for substudy participants only, when their values may be imputed with reasonable accuracy for the remaining cohort members. Links are provided to software, data sets, and tutorials showing in detail the steps needed to carry out the adjusted analyses. Epidemiologists are encouraged to consider use of these methods to enhance the accuracy of results reported from case-cohort analyses. |
| doi_str_mv | 10.1093/aje/kwp055 |
| format | Article |
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For permissions, please e-mail: journals.permissions@oxfordjournals.org. 2009</rights><rights>2009 INIST-CNRS</rights><rights>American Journal of Epidemiology © The Author 2009. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c560t-77cd5d3fcb4ddad374ddd6dff9fa782107adba3e3eec7f5751dd6018c499542a3</citedby><cites>FETCH-LOGICAL-c560t-77cd5d3fcb4ddad374ddd6dff9fa782107adba3e3eec7f5751dd6018c499542a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,1578,27903,27904</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21615512$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19357328$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Breslow, Norman E.</creatorcontrib><creatorcontrib>Lumley, Thomas</creatorcontrib><creatorcontrib>Ballantyne, Christie M.</creatorcontrib><creatorcontrib>Chambless, Lloyd E.</creatorcontrib><creatorcontrib>Kulich, Michal</creatorcontrib><title>Using the Whole Cohort in the Analysis of Case-Cohort Data</title><title>American journal of epidemiology</title><addtitle>Am J Epidemiol</addtitle><description>Case-cohort data analyses often ignore valuable information on cohort members not sampled as cases or controls. The Atherosclerosis Risk in Communities (ARIC) study investigators, for example, typically report data for just the 10%-15% of subjects sampled for substudies of their cohort of 15,972 participants. Remaining subjects contribute to stratified sampling weights only. Analysis methods implemented in the freely available R statistical system (http://cran.r-project.org/) make better use of the data through adjustment of the sampling weights via calibration or estimation. By reanalyzing data from an ARIC study of coronary heart disease and simulations based on data from the National Wilms Tumor Study, the authors demonstrate that such adjustment can dramatically improve the precision of hazard ratios estimated for baseline covariates known for all subjects. Adjustment can also improve precision for partially missing covariates, those known for substudy participants only, when their values may be imputed with reasonable accuracy for the remaining cohort members. Links are provided to software, data sets, and tutorials showing in detail the steps needed to carry out the adjusted analyses. Epidemiologists are encouraged to consider use of these methods to enhance the accuracy of results reported from case-cohort analyses.</description><subject>Analysis. Health state</subject><subject>Biological and medical sciences</subject><subject>Biomarkers - analysis</subject><subject>Calibration</subject><subject>Cohort Studies</subject><subject>Coronary Artery Disease - epidemiology</subject><subject>Coronary Artery Disease - ethnology</subject><subject>Coronary Artery Disease - genetics</subject><subject>Epidemiologic Methods</subject><subject>Epidemiology</subject><subject>Female</subject><subject>General aspects</subject><subject>Genotype</subject><subject>Humans</subject><subject>Linear Models</subject><subject>Male</subject><subject>Medical research</subject><subject>Medical sciences</subject><subject>Miscellaneous</subject><subject>Observation</subject><subject>Practice of Epidemiology</subject><subject>Proportional Hazards Models</subject><subject>Public health. Hygiene</subject><subject>Public health. Hygiene-occupational medicine</subject><subject>Research methodology</subject><subject>Risk Factors</subject><subject>Sampling Studies</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><issn>0002-9262</issn><issn>1476-6256</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkctKAzEUhoMoWi8bH0AGQRfCaC5N0nEhSL2C4EZxGU5zsVOnk5rMKL690Q71stDVgfwf30nyI7RN8CHBBTuCiT16ep1hzpdQj_SlyAXlYhn1MMY0L6iga2g9xgnGhBQcr6I1UjAuGR300PF9LOvHrBnb7GHsK5sN_diHJivrz7PTGqq3WMbMu2wI0eZdfAYNbKIVB1W0W93cQPcX53fDq_zm9vJ6eHqTay5wk0upDTfM6VHfGDBMpmGEca5wIAeUYAlmBMwya7V0XHKSYkwGul8UvE-BbaCTuXfWjqbWaFs3ASo1C-UUwpvyUKqfSV2O1aN_UVSKQZIkwX4nCP65tbFR0zJqW1VQW99GJSSlgmH5L0ixEFxSnsDdX-DEtyH9VWIYLwhl7MN2MId08DEG6xZXJlh9FKdScWpeXIJ3vj_yC-2aSsBeB0DUULkAtS7jgqNEEM7T4gXn29lfC98BTeCt9g</recordid><startdate>20090601</startdate><enddate>20090601</enddate><creator>Breslow, Norman E.</creator><creator>Lumley, Thomas</creator><creator>Ballantyne, Christie M.</creator><creator>Chambless, Lloyd E.</creator><creator>Kulich, Michal</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>IQODW</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>7QP</scope><scope>7T2</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7U1</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20090601</creationdate><title>Using the Whole Cohort in the Analysis of Case-Cohort Data</title><author>Breslow, Norman E. ; Lumley, Thomas ; Ballantyne, Christie M. ; Chambless, Lloyd E. ; Kulich, Michal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c560t-77cd5d3fcb4ddad374ddd6dff9fa782107adba3e3eec7f5751dd6018c499542a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Analysis. Health state</topic><topic>Biological and medical sciences</topic><topic>Biomarkers - analysis</topic><topic>Calibration</topic><topic>Cohort Studies</topic><topic>Coronary Artery Disease - epidemiology</topic><topic>Coronary Artery Disease - ethnology</topic><topic>Coronary Artery Disease - genetics</topic><topic>Epidemiologic Methods</topic><topic>Epidemiology</topic><topic>Female</topic><topic>General aspects</topic><topic>Genotype</topic><topic>Humans</topic><topic>Linear Models</topic><topic>Male</topic><topic>Medical research</topic><topic>Medical sciences</topic><topic>Miscellaneous</topic><topic>Observation</topic><topic>Practice of Epidemiology</topic><topic>Proportional Hazards Models</topic><topic>Public health. Hygiene</topic><topic>Public health. Hygiene-occupational medicine</topic><topic>Research methodology</topic><topic>Risk Factors</topic><topic>Sampling Studies</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Breslow, Norman E.</creatorcontrib><creatorcontrib>Lumley, Thomas</creatorcontrib><creatorcontrib>Ballantyne, Christie M.</creatorcontrib><creatorcontrib>Chambless, Lloyd E.</creatorcontrib><creatorcontrib>Kulich, Michal</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>Risk Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>American journal of epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Breslow, Norman E.</au><au>Lumley, Thomas</au><au>Ballantyne, Christie M.</au><au>Chambless, Lloyd E.</au><au>Kulich, Michal</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using the Whole Cohort in the Analysis of Case-Cohort Data</atitle><jtitle>American journal of epidemiology</jtitle><addtitle>Am J Epidemiol</addtitle><date>2009-06-01</date><risdate>2009</risdate><volume>169</volume><issue>11</issue><spage>1398</spage><epage>1405</epage><pages>1398-1405</pages><issn>0002-9262</issn><eissn>1476-6256</eissn><coden>AJEPAS</coden><abstract>Case-cohort data analyses often ignore valuable information on cohort members not sampled as cases or controls. 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| source | MEDLINE; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Analysis. Health state Biological and medical sciences Biomarkers - analysis Calibration Cohort Studies Coronary Artery Disease - epidemiology Coronary Artery Disease - ethnology Coronary Artery Disease - genetics Epidemiologic Methods Epidemiology Female General aspects Genotype Humans Linear Models Male Medical research Medical sciences Miscellaneous Observation Practice of Epidemiology Proportional Hazards Models Public health. Hygiene Public health. Hygiene-occupational medicine Research methodology Risk Factors Sampling Studies Statistical analysis Statistical methods |
| title | Using the Whole Cohort in the Analysis of Case-Cohort Data |
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