Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials
Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data...
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Veröffentlicht in: | Contemporary clinical trials 2024-06, Vol.141, p.107514-107514, Article 107514 |
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creator | Sydes, Matthew R. Murray, Macey L. Ahmed, Saiam Apostolidou, Sophia Bliss, Judith M. Bloomfield, Claire Cannings-John, Rebecca Carpenter, James Clayton, Tim Clout, Madeleine Cosgriff, Rebecca Farrin, Amanda J. Gentry-Maharaj, Aleksandra Gilbert, Duncan C. Harper, Charlie James, Nicholas D. Langley, Ruth E. Lessels, Sarah Lugg-Widger, Fiona Mackenzie, Isla S. Mafham, Marion Menon, Usha Mintz, Harriet Pinches, Heather Robling, Michael Wright-Hughes, Alexandra Yorke-Edwards, Victoria Love, Sharon B. |
description | Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. “Data Utility Comparison Studies” (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.
Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at “patient-level” or “trial-level”, depending on the item of interest and trial status.
DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them. |
doi_str_mv | 10.1016/j.cct.2024.107514 |
format | Article |
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Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at “patient-level” or “trial-level”, depending on the item of interest and trial status.
DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</description><identifier>ISSN: 1551-7144</identifier><identifier>EISSN: 1559-2030</identifier><identifier>DOI: 10.1016/j.cct.2024.107514</identifier><identifier>PMID: 38537901</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Data utility ; Data utility comparison studies ; DUCkS ; Electronic health records ; Health policy ; Healthcare systems data ; RCTs ; Real world data ; Routinely-collected data ; Routinely-collected healthcare data</subject><ispartof>Contemporary clinical trials, 2024-06, Vol.141, p.107514-107514, Article 107514</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c348t-f00c4cec756ce6ceb0a421effbba6a7181eb4a79233abdc485a145a48f0ff78c3</cites><orcidid>0000-0001-7270-9762 ; 0000-0003-1583-6268 ; 0000-0003-3890-6206 ; 0000-0001-6418-0854 ; 0000-0002-6695-5390 ; 0000-0001-7645-1199 ; 0000-0001-7543-5031 ; 0000-0002-3680-7127 ; 0000-0003-3708-1732 ; 0000-0002-1004-036X ; 0000-0002-6465-7330 ; 0000-0002-9323-1371 ; 0000-0003-1859-7012 ; 0000-0001-8839-6756 ; 0000-0002-1266-3288 ; 0000-0002-2876-0584 ; 0000-0002-0134-9832 ; 0000-0002-7314-8204 ; 0000-0003-0029-9703 ; 0000-0001-5235-6517 ; 0000-0002-7373-3067 ; 0000-0003-2659-0451 ; 0000-0001-7957-7424</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cct.2024.107514$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,46000</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38537901$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sydes, Matthew R.</creatorcontrib><creatorcontrib>Murray, Macey L.</creatorcontrib><creatorcontrib>Ahmed, Saiam</creatorcontrib><creatorcontrib>Apostolidou, Sophia</creatorcontrib><creatorcontrib>Bliss, Judith M.</creatorcontrib><creatorcontrib>Bloomfield, Claire</creatorcontrib><creatorcontrib>Cannings-John, Rebecca</creatorcontrib><creatorcontrib>Carpenter, James</creatorcontrib><creatorcontrib>Clayton, Tim</creatorcontrib><creatorcontrib>Clout, Madeleine</creatorcontrib><creatorcontrib>Cosgriff, Rebecca</creatorcontrib><creatorcontrib>Farrin, Amanda J.</creatorcontrib><creatorcontrib>Gentry-Maharaj, Aleksandra</creatorcontrib><creatorcontrib>Gilbert, Duncan C.</creatorcontrib><creatorcontrib>Harper, Charlie</creatorcontrib><creatorcontrib>James, Nicholas D.</creatorcontrib><creatorcontrib>Langley, Ruth E.</creatorcontrib><creatorcontrib>Lessels, Sarah</creatorcontrib><creatorcontrib>Lugg-Widger, Fiona</creatorcontrib><creatorcontrib>Mackenzie, Isla S.</creatorcontrib><creatorcontrib>Mafham, Marion</creatorcontrib><creatorcontrib>Menon, Usha</creatorcontrib><creatorcontrib>Mintz, Harriet</creatorcontrib><creatorcontrib>Pinches, Heather</creatorcontrib><creatorcontrib>Robling, Michael</creatorcontrib><creatorcontrib>Wright-Hughes, Alexandra</creatorcontrib><creatorcontrib>Yorke-Edwards, Victoria</creatorcontrib><creatorcontrib>Love, Sharon B.</creatorcontrib><title>Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials</title><title>Contemporary clinical trials</title><addtitle>Contemp Clin Trials</addtitle><description>Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. “Data Utility Comparison Studies” (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.
Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at “patient-level” or “trial-level”, depending on the item of interest and trial status.
DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. 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Murray, Macey L. ; Ahmed, Saiam ; Apostolidou, Sophia ; Bliss, Judith M. ; Bloomfield, Claire ; Cannings-John, Rebecca ; Carpenter, James ; Clayton, Tim ; Clout, Madeleine ; Cosgriff, Rebecca ; Farrin, Amanda J. ; Gentry-Maharaj, Aleksandra ; Gilbert, Duncan C. ; Harper, Charlie ; James, Nicholas D. ; Langley, Ruth E. ; Lessels, Sarah ; Lugg-Widger, Fiona ; Mackenzie, Isla S. ; Mafham, Marion ; Menon, Usha ; Mintz, Harriet ; Pinches, Heather ; Robling, Michael ; Wright-Hughes, Alexandra ; Yorke-Edwards, Victoria ; Love, Sharon B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-f00c4cec756ce6ceb0a421effbba6a7181eb4a79233abdc485a145a48f0ff78c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Data utility</topic><topic>Data utility comparison studies</topic><topic>DUCkS</topic><topic>Electronic health records</topic><topic>Health policy</topic><topic>Healthcare systems data</topic><topic>RCTs</topic><topic>Real world data</topic><topic>Routinely-collected data</topic><topic>Routinely-collected healthcare data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sydes, Matthew R.</creatorcontrib><creatorcontrib>Murray, Macey L.</creatorcontrib><creatorcontrib>Ahmed, Saiam</creatorcontrib><creatorcontrib>Apostolidou, Sophia</creatorcontrib><creatorcontrib>Bliss, Judith M.</creatorcontrib><creatorcontrib>Bloomfield, Claire</creatorcontrib><creatorcontrib>Cannings-John, Rebecca</creatorcontrib><creatorcontrib>Carpenter, James</creatorcontrib><creatorcontrib>Clayton, Tim</creatorcontrib><creatorcontrib>Clout, Madeleine</creatorcontrib><creatorcontrib>Cosgriff, Rebecca</creatorcontrib><creatorcontrib>Farrin, Amanda J.</creatorcontrib><creatorcontrib>Gentry-Maharaj, Aleksandra</creatorcontrib><creatorcontrib>Gilbert, Duncan C.</creatorcontrib><creatorcontrib>Harper, Charlie</creatorcontrib><creatorcontrib>James, Nicholas D.</creatorcontrib><creatorcontrib>Langley, Ruth E.</creatorcontrib><creatorcontrib>Lessels, Sarah</creatorcontrib><creatorcontrib>Lugg-Widger, Fiona</creatorcontrib><creatorcontrib>Mackenzie, Isla S.</creatorcontrib><creatorcontrib>Mafham, Marion</creatorcontrib><creatorcontrib>Menon, Usha</creatorcontrib><creatorcontrib>Mintz, Harriet</creatorcontrib><creatorcontrib>Pinches, Heather</creatorcontrib><creatorcontrib>Robling, Michael</creatorcontrib><creatorcontrib>Wright-Hughes, Alexandra</creatorcontrib><creatorcontrib>Yorke-Edwards, Victoria</creatorcontrib><creatorcontrib>Love, Sharon B.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Contemporary clinical trials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sydes, Matthew R.</au><au>Murray, Macey L.</au><au>Ahmed, Saiam</au><au>Apostolidou, Sophia</au><au>Bliss, Judith M.</au><au>Bloomfield, Claire</au><au>Cannings-John, Rebecca</au><au>Carpenter, James</au><au>Clayton, Tim</au><au>Clout, Madeleine</au><au>Cosgriff, Rebecca</au><au>Farrin, Amanda J.</au><au>Gentry-Maharaj, Aleksandra</au><au>Gilbert, Duncan C.</au><au>Harper, Charlie</au><au>James, Nicholas D.</au><au>Langley, Ruth E.</au><au>Lessels, Sarah</au><au>Lugg-Widger, Fiona</au><au>Mackenzie, Isla S.</au><au>Mafham, Marion</au><au>Menon, Usha</au><au>Mintz, Harriet</au><au>Pinches, Heather</au><au>Robling, Michael</au><au>Wright-Hughes, Alexandra</au><au>Yorke-Edwards, Victoria</au><au>Love, Sharon B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials</atitle><jtitle>Contemporary clinical trials</jtitle><addtitle>Contemp Clin Trials</addtitle><date>2024-06-01</date><risdate>2024</risdate><volume>141</volume><spage>107514</spage><epage>107514</epage><pages>107514-107514</pages><artnum>107514</artnum><issn>1551-7144</issn><eissn>1559-2030</eissn><abstract>Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. “Data Utility Comparison Studies” (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.
Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at “patient-level” or “trial-level”, depending on the item of interest and trial status.
DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>38537901</pmid><doi>10.1016/j.cct.2024.107514</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-7270-9762</orcidid><orcidid>https://orcid.org/0000-0003-1583-6268</orcidid><orcidid>https://orcid.org/0000-0003-3890-6206</orcidid><orcidid>https://orcid.org/0000-0001-6418-0854</orcidid><orcidid>https://orcid.org/0000-0002-6695-5390</orcidid><orcidid>https://orcid.org/0000-0001-7645-1199</orcidid><orcidid>https://orcid.org/0000-0001-7543-5031</orcidid><orcidid>https://orcid.org/0000-0002-3680-7127</orcidid><orcidid>https://orcid.org/0000-0003-3708-1732</orcidid><orcidid>https://orcid.org/0000-0002-1004-036X</orcidid><orcidid>https://orcid.org/0000-0002-6465-7330</orcidid><orcidid>https://orcid.org/0000-0002-9323-1371</orcidid><orcidid>https://orcid.org/0000-0003-1859-7012</orcidid><orcidid>https://orcid.org/0000-0001-8839-6756</orcidid><orcidid>https://orcid.org/0000-0002-1266-3288</orcidid><orcidid>https://orcid.org/0000-0002-2876-0584</orcidid><orcidid>https://orcid.org/0000-0002-0134-9832</orcidid><orcidid>https://orcid.org/0000-0002-7314-8204</orcidid><orcidid>https://orcid.org/0000-0003-0029-9703</orcidid><orcidid>https://orcid.org/0000-0001-5235-6517</orcidid><orcidid>https://orcid.org/0000-0002-7373-3067</orcidid><orcidid>https://orcid.org/0000-0003-2659-0451</orcidid><orcidid>https://orcid.org/0000-0001-7957-7424</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Data utility Data utility comparison studies DUCkS Electronic health records Health policy Healthcare systems data RCTs Real world data Routinely-collected data Routinely-collected healthcare data |
title | Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials |
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