Handling missing values in cost effectiveness analyses that use data from cluster randomized trials
Public policy makers use cost effectiveness analyses (CEAs) to decide which health and social care interventions to provide. Missing data are common in CEAs, but most studies use complete-case analysis. Appropriate methods have not been developed for handling missing data in complex settings, exempl...
Gespeichert in:
| Veröffentlicht in: | Journal of the Royal Statistical Society. Series A, Statistics in society Statistics in society, 2014-02, Vol.177 (2), p.457-474 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 474 |
|---|---|
| container_issue | 2 |
| container_start_page | 457 |
| container_title | Journal of the Royal Statistical Society. Series A, Statistics in society |
| container_volume | 177 |
| creator | Díaz-Ordaz, K. Kenward, Michael G. Grieve, Richard |
| description | Public policy makers use cost effectiveness analyses (CEAs) to decide which health and social care interventions to provide. Missing data are common in CEAs, but most studies use complete-case analysis. Appropriate methods have not been developed for handling missing data in complex settings, exemplified by CEAs that use data from cluster randomized trials. We present a multilevel multiple-imputation approach that recognizes the hierarchical structure of the data and is compatible with the bivariate multilevel models that are used to report cost effectiveness. We contrast this approach with single-level multiple imputation and complete-case analysis, in a CEA alongside a cluster randomized trial. The paper highlights the importance of adopting a principled approach to handling missing values in settings with complex data structures. |
| doi_str_mv | 10.1111/rssa.12016 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_1508432825</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>43965705</jstor_id><sourcerecordid>43965705</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4636-46e26a170681a30bed8550e5ea25bdc131dca20aa2bbcc20b3230170746a40123</originalsourceid><addsrcrecordid>eNp9kM1v1DAQxS0EEkvLhTuSJS4IKa0_Yjs5VitokbYgsSC4WRPHAS_ZpPU4he1fj9NADxw6l3eY3xvNe4S84OyE5zmNiHDCBeP6EVnxUpuirtS3x2TFal0WvK6rp-QZ4o7NY8yKuAsY2j4M3-k-IM56A_3kkYaBuhET9V3nXQo3fvCIFAboD5jX6QckOqGnLSSgXRz31PUTJh9pzBfHfbj1LU0xQI_H5EmXxT__q0fky7u3n9cXxebj-fv12aZwpZa6KLUXGrhhuuIgWePbSinmlQehmtZxyVsHggGIpnFOsEYKyTJuSg0l40IekdfL3as4XucMyeZMzvc9DH6c0HLFqlKKSqiMvvoP3Y1TzOHuqFwc19pk6s1CuTgiRt_Zqxj2EA-WMzv3bee-7V3fGeYL_Cv0_vAAaT9tt2f_PC8Xzw7TGO89pay1Mmx-s1j2ITf7-34P8afN7xllv344txu5vmSXbGuN_AMbFJut</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1501461667</pqid></control><display><type>article</type><title>Handling missing values in cost effectiveness analyses that use data from cluster randomized trials</title><source>Wiley Online Library Journals Frontfile Complete</source><source>EBSCOhost Business Source Complete</source><source>Jstor Complete Legacy</source><source>Oxford University Press Journals All Titles (1996-Current)</source><creator>Díaz-Ordaz, K. ; Kenward, Michael G. ; Grieve, Richard</creator><creatorcontrib>Díaz-Ordaz, K. ; Kenward, Michael G. ; Grieve, Richard</creatorcontrib><description>Public policy makers use cost effectiveness analyses (CEAs) to decide which health and social care interventions to provide. Missing data are common in CEAs, but most studies use complete-case analysis. Appropriate methods have not been developed for handling missing data in complex settings, exemplified by CEAs that use data from cluster randomized trials. We present a multilevel multiple-imputation approach that recognizes the hierarchical structure of the data and is compatible with the bivariate multilevel models that are used to report cost effectiveness. We contrast this approach with single-level multiple imputation and complete-case analysis, in a CEA alongside a cluster randomized trial. The paper highlights the importance of adopting a principled approach to handling missing values in settings with complex data structures.</description><identifier>ISSN: 0964-1998</identifier><identifier>EISSN: 1467-985X</identifier><identifier>DOI: 10.1111/rssa.12016</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>Bivariate models ; Clustered continuous data ; Cost analysis ; Cost effectiveness ; Effectiveness ; Health care ; Missing data ; Multidimensional analysis ; Multiple imputation ; Public policy ; Random sampling ; Social work ; Statistical analysis</subject><ispartof>Journal of the Royal Statistical Society. Series A, Statistics in society, 2014-02, Vol.177 (2), p.457-474</ispartof><rights>Copyright © 2014 The Royal Statistical Society and John Wiley & Sons Ltd.</rights><rights>2013 Royal Statistical Society</rights><rights>Copyright Blackwell Publishing Ltd. Feb 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4636-46e26a170681a30bed8550e5ea25bdc131dca20aa2bbcc20b3230170746a40123</citedby><cites>FETCH-LOGICAL-c4636-46e26a170681a30bed8550e5ea25bdc131dca20aa2bbcc20b3230170746a40123</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/43965705$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/43965705$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,778,782,801,1414,27911,27912,45561,45562,58004,58237</link.rule.ids></links><search><creatorcontrib>Díaz-Ordaz, K.</creatorcontrib><creatorcontrib>Kenward, Michael G.</creatorcontrib><creatorcontrib>Grieve, Richard</creatorcontrib><title>Handling missing values in cost effectiveness analyses that use data from cluster randomized trials</title><title>Journal of the Royal Statistical Society. Series A, Statistics in society</title><addtitle>J. R. Stat. Soc. A</addtitle><description>Public policy makers use cost effectiveness analyses (CEAs) to decide which health and social care interventions to provide. Missing data are common in CEAs, but most studies use complete-case analysis. Appropriate methods have not been developed for handling missing data in complex settings, exemplified by CEAs that use data from cluster randomized trials. We present a multilevel multiple-imputation approach that recognizes the hierarchical structure of the data and is compatible with the bivariate multilevel models that are used to report cost effectiveness. We contrast this approach with single-level multiple imputation and complete-case analysis, in a CEA alongside a cluster randomized trial. The paper highlights the importance of adopting a principled approach to handling missing values in settings with complex data structures.</description><subject>Bivariate models</subject><subject>Clustered continuous data</subject><subject>Cost analysis</subject><subject>Cost effectiveness</subject><subject>Effectiveness</subject><subject>Health care</subject><subject>Missing data</subject><subject>Multidimensional analysis</subject><subject>Multiple imputation</subject><subject>Public policy</subject><subject>Random sampling</subject><subject>Social work</subject><subject>Statistical analysis</subject><issn>0964-1998</issn><issn>1467-985X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp9kM1v1DAQxS0EEkvLhTuSJS4IKa0_Yjs5VitokbYgsSC4WRPHAS_ZpPU4he1fj9NADxw6l3eY3xvNe4S84OyE5zmNiHDCBeP6EVnxUpuirtS3x2TFal0WvK6rp-QZ4o7NY8yKuAsY2j4M3-k-IM56A_3kkYaBuhET9V3nXQo3fvCIFAboD5jX6QckOqGnLSSgXRz31PUTJh9pzBfHfbj1LU0xQI_H5EmXxT__q0fky7u3n9cXxebj-fv12aZwpZa6KLUXGrhhuuIgWePbSinmlQehmtZxyVsHggGIpnFOsEYKyTJuSg0l40IekdfL3as4XucMyeZMzvc9DH6c0HLFqlKKSqiMvvoP3Y1TzOHuqFwc19pk6s1CuTgiRt_Zqxj2EA-WMzv3bee-7V3fGeYL_Cv0_vAAaT9tt2f_PC8Xzw7TGO89pay1Mmx-s1j2ITf7-34P8afN7xllv344txu5vmSXbGuN_AMbFJut</recordid><startdate>201402</startdate><enddate>201402</enddate><creator>Díaz-Ordaz, K.</creator><creator>Kenward, Michael G.</creator><creator>Grieve, Richard</creator><general>Blackwell Publishing Ltd</general><general>John Wiley & Sons Ltd</general><general>Oxford University Press</general><scope>BSCLL</scope><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8BJ</scope><scope>8FD</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201402</creationdate><title>Handling missing values in cost effectiveness analyses that use data from cluster randomized trials</title><author>Díaz-Ordaz, K. ; Kenward, Michael G. ; Grieve, Richard</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4636-46e26a170681a30bed8550e5ea25bdc131dca20aa2bbcc20b3230170746a40123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Bivariate models</topic><topic>Clustered continuous data</topic><topic>Cost analysis</topic><topic>Cost effectiveness</topic><topic>Effectiveness</topic><topic>Health care</topic><topic>Missing data</topic><topic>Multidimensional analysis</topic><topic>Multiple imputation</topic><topic>Public policy</topic><topic>Random sampling</topic><topic>Social work</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Díaz-Ordaz, K.</creatorcontrib><creatorcontrib>Kenward, Michael G.</creatorcontrib><creatorcontrib>Grieve, Richard</creatorcontrib><collection>Istex</collection><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</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 the Royal Statistical Society. Series A, Statistics in society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Díaz-Ordaz, K.</au><au>Kenward, Michael G.</au><au>Grieve, Richard</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Handling missing values in cost effectiveness analyses that use data from cluster randomized trials</atitle><jtitle>Journal of the Royal Statistical Society. Series A, Statistics in society</jtitle><addtitle>J. R. Stat. Soc. A</addtitle><date>2014-02</date><risdate>2014</risdate><volume>177</volume><issue>2</issue><spage>457</spage><epage>474</epage><pages>457-474</pages><issn>0964-1998</issn><eissn>1467-985X</eissn><abstract>Public policy makers use cost effectiveness analyses (CEAs) to decide which health and social care interventions to provide. Missing data are common in CEAs, but most studies use complete-case analysis. Appropriate methods have not been developed for handling missing data in complex settings, exemplified by CEAs that use data from cluster randomized trials. We present a multilevel multiple-imputation approach that recognizes the hierarchical structure of the data and is compatible with the bivariate multilevel models that are used to report cost effectiveness. We contrast this approach with single-level multiple imputation and complete-case analysis, in a CEA alongside a cluster randomized trial. The paper highlights the importance of adopting a principled approach to handling missing values in settings with complex data structures.</abstract><cop>Oxford</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/rssa.12016</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0964-1998 |
| ispartof | Journal of the Royal Statistical Society. Series A, Statistics in society, 2014-02, Vol.177 (2), p.457-474 |
| issn | 0964-1998 1467-985X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1508432825 |
| source | Wiley Online Library Journals Frontfile Complete; EBSCOhost Business Source Complete; Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current) |
| subjects | Bivariate models Clustered continuous data Cost analysis Cost effectiveness Effectiveness Health care Missing data Multidimensional analysis Multiple imputation Public policy Random sampling Social work Statistical analysis |
| title | Handling missing values in cost effectiveness analyses that use data from cluster randomized trials |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T11%3A51%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Handling%20missing%20values%20in%20cost%20effectiveness%20analyses%20that%20use%20data%20from%20cluster%20randomized%20trials&rft.jtitle=Journal%20of%20the%20Royal%20Statistical%20Society.%20Series%20A,%20Statistics%20in%20society&rft.au=D%C3%ADaz-Ordaz,%20K.&rft.date=2014-02&rft.volume=177&rft.issue=2&rft.spage=457&rft.epage=474&rft.pages=457-474&rft.issn=0964-1998&rft.eissn=1467-985X&rft_id=info:doi/10.1111/rssa.12016&rft_dat=%3Cjstor_proqu%3E43965705%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1501461667&rft_id=info:pmid/&rft_jstor_id=43965705&rfr_iscdi=true |