Sample selection and validity of exposure–disease association estimates in cohort studies
BackgroundParticipants in cohort studies are frequently selected from restricted source populations. It has been recognised that such restriction may affect the study validity.ObjectivesTo assess the bias that may arise when analyses involve data from cohorts based on restricted source populations,...
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| Veröffentlicht in: | Journal of epidemiology and community health (1979) 2011-05, Vol.65 (5), p.407-411 |
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| container_title | Journal of epidemiology and community health (1979) |
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| creator | Pizzi, Costanza De Stavola, Bianca Merletti, Franco Bellocco, Rino dos Santos Silva, Isabel Pearce, Neil Richiardi, Lorenzo |
| description | BackgroundParticipants in cohort studies are frequently selected from restricted source populations. It has been recognised that such restriction may affect the study validity.ObjectivesTo assess the bias that may arise when analyses involve data from cohorts based on restricted source populations, an area little studied in quantitative terms.MethodsMonte Carlo simulations were used, based on a setting where the exposure and one risk factor for the outcome, which are not associated in the general population, influence selection into the cohort. All the parameters involved in the simulations (ie, prevalence and effects of exposure and risk factor on both the selection and outcome process, selection prevalence, baseline outcome incidence rate, and sample size) were allowed to vary to reflect real life settings.ResultsThe simulations show that when the exposure and risk factor are strongly associated with selection (ORs of 4 or 0.25) and the unmeasured risk factor is associated with a disease HR of 4, the bias in the estimated log HR for the exposure–disease association is ±0.15. When these associations decrease to values more commonly seen in epidemiological studies (eg, ORs and HRs of 2 or 0.5), the bias in the log HR drops to just ±0.02.ConclusionsUsing a restricted source population for a cohort study will, under a range of sensible scenarios, produce only relatively weak bias in estimates of the exposure–disease associations. |
| doi_str_mv | 10.1136/jech.2009.107185 |
| format | Article |
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It has been recognised that such restriction may affect the study validity.ObjectivesTo assess the bias that may arise when analyses involve data from cohorts based on restricted source populations, an area little studied in quantitative terms.MethodsMonte Carlo simulations were used, based on a setting where the exposure and one risk factor for the outcome, which are not associated in the general population, influence selection into the cohort. All the parameters involved in the simulations (ie, prevalence and effects of exposure and risk factor on both the selection and outcome process, selection prevalence, baseline outcome incidence rate, and sample size) were allowed to vary to reflect real life settings.ResultsThe simulations show that when the exposure and risk factor are strongly associated with selection (ORs of 4 or 0.25) and the unmeasured risk factor is associated with a disease HR of 4, the bias in the estimated log HR for the exposure–disease association is ±0.15. When these associations decrease to values more commonly seen in epidemiological studies (eg, ORs and HRs of 2 or 0.5), the bias in the log HR drops to just ±0.02.ConclusionsUsing a restricted source population for a cohort study will, under a range of sensible scenarios, produce only relatively weak bias in estimates of the exposure–disease associations.</description><identifier>ISSN: 0143-005X</identifier><identifier>ISSN: 1470-2738</identifier><identifier>EISSN: 1470-2738</identifier><identifier>DOI: 10.1136/jech.2009.107185</identifier><identifier>PMID: 20881022</identifier><identifier>CODEN: JECHDR</identifier><language>eng</language><publisher>London: BMJ Publishing Group Ltd</publisher><subject>Bias ; BIAS ME ; Biological and medical sciences ; Breast cancer ; Cancer screening ; Cohort Studies ; confounding ; Data processing ; Directed Acyclical Graphs ; Disease risk ; Environmental Exposure - adverse effects ; Environmental Exposure - statistics & numerical data ; Epidemiologic Methods ; Epidemiology ; epidemiology ME ; Estimates ; Estimation bias ; Exposure ; Family medical history ; General aspects ; Health risk assessment ; Humans ; Incidence ; Logistic Models ; Medical sciences ; Medicin och hälsovetenskap ; Miscellaneous ; Monte Carlo Method ; Monte Carlo simulation ; Monte Carlo Simulations ; Obesity ; Population ; Predisposing factors ; Prevalence ; Public health. Hygiene ; Public health. Hygiene-occupational medicine ; Research universities ; Restrictions ; Risk Assessment - methods ; Risk Factors ; Sample Size ; Sampling methods ; Selection Bias ; Simulation ; Studies ; Theory and methods ; Validity</subject><ispartof>Journal of epidemiology and community health (1979), 2011-05, Vol.65 (5), p.407-411</ispartof><rights>2011, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.</rights><rights>Copyright: © 2011 BMJ Publishing Group</rights><rights>2015 INIST-CNRS</rights><rights>Copyright: 2011 (c) 2011, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b578t-5ebb570338a8b68c845e013e3371bc25b57044ab33396a89dd35ba839a4e52ef3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://jech.bmj.com/content/65/5/407.full.pdf$$EPDF$$P50$$Gbmj$$H</linktopdf><linktohtml>$$Uhttps://jech.bmj.com/content/65/5/407.full$$EHTML$$P50$$Gbmj$$H</linktohtml><link.rule.ids>114,115,230,314,552,780,784,803,885,3196,23571,27924,27925,58017,58250,77600,77631</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24046030$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20881022$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-00585767$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:122329352$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Pizzi, Costanza</creatorcontrib><creatorcontrib>De Stavola, Bianca</creatorcontrib><creatorcontrib>Merletti, Franco</creatorcontrib><creatorcontrib>Bellocco, Rino</creatorcontrib><creatorcontrib>dos Santos Silva, Isabel</creatorcontrib><creatorcontrib>Pearce, Neil</creatorcontrib><creatorcontrib>Richiardi, Lorenzo</creatorcontrib><title>Sample selection and validity of exposure–disease association estimates in cohort studies</title><title>Journal of epidemiology and community health (1979)</title><addtitle>J Epidemiol Community Health</addtitle><description>BackgroundParticipants in cohort studies are frequently selected from restricted source populations. It has been recognised that such restriction may affect the study validity.ObjectivesTo assess the bias that may arise when analyses involve data from cohorts based on restricted source populations, an area little studied in quantitative terms.MethodsMonte Carlo simulations were used, based on a setting where the exposure and one risk factor for the outcome, which are not associated in the general population, influence selection into the cohort. All the parameters involved in the simulations (ie, prevalence and effects of exposure and risk factor on both the selection and outcome process, selection prevalence, baseline outcome incidence rate, and sample size) were allowed to vary to reflect real life settings.ResultsThe simulations show that when the exposure and risk factor are strongly associated with selection (ORs of 4 or 0.25) and the unmeasured risk factor is associated with a disease HR of 4, the bias in the estimated log HR for the exposure–disease association is ±0.15. When these associations decrease to values more commonly seen in epidemiological studies (eg, ORs and HRs of 2 or 0.5), the bias in the log HR drops to just ±0.02.ConclusionsUsing a restricted source population for a cohort study will, under a range of sensible scenarios, produce only relatively weak bias in estimates of the exposure–disease associations.</description><subject>Bias</subject><subject>BIAS ME</subject><subject>Biological and medical sciences</subject><subject>Breast cancer</subject><subject>Cancer screening</subject><subject>Cohort Studies</subject><subject>confounding</subject><subject>Data processing</subject><subject>Directed Acyclical Graphs</subject><subject>Disease risk</subject><subject>Environmental Exposure - adverse effects</subject><subject>Environmental Exposure - statistics & numerical data</subject><subject>Epidemiologic Methods</subject><subject>Epidemiology</subject><subject>epidemiology ME</subject><subject>Estimates</subject><subject>Estimation bias</subject><subject>Exposure</subject><subject>Family medical history</subject><subject>General aspects</subject><subject>Health risk assessment</subject><subject>Humans</subject><subject>Incidence</subject><subject>Logistic Models</subject><subject>Medical sciences</subject><subject>Medicin och hälsovetenskap</subject><subject>Miscellaneous</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo simulation</subject><subject>Monte Carlo Simulations</subject><subject>Obesity</subject><subject>Population</subject><subject>Predisposing factors</subject><subject>Prevalence</subject><subject>Public health. Hygiene</subject><subject>Public health. Hygiene-occupational medicine</subject><subject>Research universities</subject><subject>Restrictions</subject><subject>Risk Assessment - methods</subject><subject>Risk Factors</subject><subject>Sample Size</subject><subject>Sampling methods</subject><subject>Selection Bias</subject><subject>Simulation</subject><subject>Studies</subject><subject>Theory and methods</subject><subject>Validity</subject><issn>0143-005X</issn><issn>1470-2738</issn><issn>1470-2738</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>D8T</sourceid><recordid>eNqFkl1r1EAUhoModq3ee6MEihSRrPOZmbksq7XCooIfCF4Mk-QsO9tsZs1JanvX_-A_9Jc4MdtdFKRXc5jzvIfz8SbJY0qmlPL85QrK5ZQRYqaUKKrlnWRChSIZU1zfTSaECp4RIr8eJA8QVySGipn7yQEjWlPC2CT59tGtNzWkCDWUnQ9N6poqvXC1r3x3lYZFCpebgH0Lv65_Vh7BIaQOMZTe_cEBO792HWDqm7QMy9B2KXZ95QEfJvcWrkZ4tH0Pk8-nrz_NzrL5-zdvZyfzrJBKd5mEIgaEc-10ketSCwmEcuBc0aJkckgK4QrOucmdNlXFZeE0N06AZLDgh0k21sUfsOkLu2ljR-2VDc7b7dd5jMBKoXNDIm_-y2_aUO1FN0LKGGeGSxa1z0ft0tV_Cc9O5nb4izvWUuXqgkb2eGRj0e99XJRdeyyhrl0DoUdr4lhKcsZuJXUeD8Y0Gcijf8hV6NsmrtdSpUw8PNM8UmSkyjYgtrDYtUqJHZxjB-fYwTl2dE6UPN0W7os1VDvBjVUi8GwLOCxdvWhdU3rcc4KInPBhu09GboVdaHd5Qakkxoj9tTx2cLnLu_bc5oorad99mdlTQzWb5R_sq8i_GPlivbp9jN-W_fK6</recordid><startdate>20110501</startdate><enddate>20110501</enddate><creator>Pizzi, Costanza</creator><creator>De Stavola, Bianca</creator><creator>Merletti, Franco</creator><creator>Bellocco, Rino</creator><creator>dos Santos Silva, Isabel</creator><creator>Pearce, Neil</creator><creator>Richiardi, Lorenzo</creator><general>BMJ Publishing Group Ltd</general><general>BMJ Publishing Group</general><general>BMJ Publishing Group LTD</general><scope>BSCLL</scope><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>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>88I</scope><scope>8AF</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BTHHO</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M2P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>7U1</scope><scope>7U2</scope><scope>7U7</scope><scope>C1K</scope><scope>1XC</scope><scope>VOOES</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>ZZAVC</scope></search><sort><creationdate>20110501</creationdate><title>Sample selection and validity of exposure–disease association estimates in cohort studies</title><author>Pizzi, Costanza ; De Stavola, Bianca ; Merletti, Franco ; Bellocco, Rino ; dos Santos Silva, Isabel ; Pearce, Neil ; Richiardi, Lorenzo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b578t-5ebb570338a8b68c845e013e3371bc25b57044ab33396a89dd35ba839a4e52ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Bias</topic><topic>BIAS ME</topic><topic>Biological and medical sciences</topic><topic>Breast cancer</topic><topic>Cancer screening</topic><topic>Cohort Studies</topic><topic>confounding</topic><topic>Data processing</topic><topic>Directed Acyclical Graphs</topic><topic>Disease risk</topic><topic>Environmental Exposure - adverse effects</topic><topic>Environmental Exposure - statistics & numerical data</topic><topic>Epidemiologic Methods</topic><topic>Epidemiology</topic><topic>epidemiology ME</topic><topic>Estimates</topic><topic>Estimation bias</topic><topic>Exposure</topic><topic>Family medical history</topic><topic>General aspects</topic><topic>Health risk assessment</topic><topic>Humans</topic><topic>Incidence</topic><topic>Logistic Models</topic><topic>Medical sciences</topic><topic>Medicin och hälsovetenskap</topic><topic>Miscellaneous</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo simulation</topic><topic>Monte Carlo Simulations</topic><topic>Obesity</topic><topic>Population</topic><topic>Predisposing factors</topic><topic>Prevalence</topic><topic>Public health. Hygiene</topic><topic>Public health. Hygiene-occupational medicine</topic><topic>Research universities</topic><topic>Restrictions</topic><topic>Risk Assessment - methods</topic><topic>Risk Factors</topic><topic>Sample Size</topic><topic>Sampling methods</topic><topic>Selection Bias</topic><topic>Simulation</topic><topic>Studies</topic><topic>Theory and methods</topic><topic>Validity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pizzi, Costanza</creatorcontrib><creatorcontrib>De Stavola, Bianca</creatorcontrib><creatorcontrib>Merletti, Franco</creatorcontrib><creatorcontrib>Bellocco, Rino</creatorcontrib><creatorcontrib>dos Santos Silva, Isabel</creatorcontrib><creatorcontrib>Pearce, Neil</creatorcontrib><creatorcontrib>Richiardi, Lorenzo</creatorcontrib><collection>Istex</collection><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>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>British Nursing Database</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>BMJ Journals</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Toxicology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SwePub Articles full text</collection><jtitle>Journal of epidemiology and community health (1979)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pizzi, Costanza</au><au>De Stavola, Bianca</au><au>Merletti, Franco</au><au>Bellocco, Rino</au><au>dos Santos Silva, Isabel</au><au>Pearce, Neil</au><au>Richiardi, Lorenzo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sample selection and validity of exposure–disease association estimates in cohort studies</atitle><jtitle>Journal of epidemiology and community health (1979)</jtitle><addtitle>J Epidemiol Community Health</addtitle><date>2011-05-01</date><risdate>2011</risdate><volume>65</volume><issue>5</issue><spage>407</spage><epage>411</epage><pages>407-411</pages><issn>0143-005X</issn><issn>1470-2738</issn><eissn>1470-2738</eissn><coden>JECHDR</coden><abstract>BackgroundParticipants in cohort studies are frequently selected from restricted source populations. It has been recognised that such restriction may affect the study validity.ObjectivesTo assess the bias that may arise when analyses involve data from cohorts based on restricted source populations, an area little studied in quantitative terms.MethodsMonte Carlo simulations were used, based on a setting where the exposure and one risk factor for the outcome, which are not associated in the general population, influence selection into the cohort. All the parameters involved in the simulations (ie, prevalence and effects of exposure and risk factor on both the selection and outcome process, selection prevalence, baseline outcome incidence rate, and sample size) were allowed to vary to reflect real life settings.ResultsThe simulations show that when the exposure and risk factor are strongly associated with selection (ORs of 4 or 0.25) and the unmeasured risk factor is associated with a disease HR of 4, the bias in the estimated log HR for the exposure–disease association is ±0.15. When these associations decrease to values more commonly seen in epidemiological studies (eg, ORs and HRs of 2 or 0.5), the bias in the log HR drops to just ±0.02.ConclusionsUsing a restricted source population for a cohort study will, under a range of sensible scenarios, produce only relatively weak bias in estimates of the exposure–disease associations.</abstract><cop>London</cop><pub>BMJ Publishing Group Ltd</pub><pmid>20881022</pmid><doi>10.1136/jech.2009.107185</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Bias BIAS ME Biological and medical sciences Breast cancer Cancer screening Cohort Studies confounding Data processing Directed Acyclical Graphs Disease risk Environmental Exposure - adverse effects Environmental Exposure - statistics & numerical data Epidemiologic Methods Epidemiology epidemiology ME Estimates Estimation bias Exposure Family medical history General aspects Health risk assessment Humans Incidence Logistic Models Medical sciences Medicin och hälsovetenskap Miscellaneous Monte Carlo Method Monte Carlo simulation Monte Carlo Simulations Obesity Population Predisposing factors Prevalence Public health. Hygiene Public health. Hygiene-occupational medicine Research universities Restrictions Risk Assessment - methods Risk Factors Sample Size Sampling methods Selection Bias Simulation Studies Theory and methods Validity |
| title | Sample selection and validity of exposure–disease association estimates in cohort studies |
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