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dc.contributor.authorLiu, Shao-Hsien
dc.contributor.authorUlbricht, Christine M.
dc.contributor.authorChrysanthopoulou, Stavroula A.
dc.contributor.authorLapane, Kate L
dc.date2022-08-11T08:09:45.000
dc.date.accessioned2022-08-23T16:41:53Z
dc.date.available2022-08-23T16:41:53Z
dc.date.issued2016-07-20
dc.date.submitted2016-08-10
dc.identifier.citationBMC Res Notes. 2016 Jul 20;9(1):354. doi: 10.1186/s13104-016-2163-7. <a href="http://dx.doi.org/10.1186/s13104-016-2163-7">Link to article on publisher's site</a>
dc.identifier.issn1756-0500 (Linking)
dc.identifier.doi10.1186/s13104-016-2163-7
dc.identifier.pmid27439301
dc.identifier.urihttp://hdl.handle.net/20.500.14038/39977
dc.description<p>Shao-Hsien Liu is a student in the Clinical and Population Health Research Program of the Graduate School of Biomedical Sciences at UMass Medical School.</p>
dc.description.abstractBACKGROUND: Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown. METHODS: We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized. RESULTS: Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce. CONCLUSIONS: Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=27439301&dopt=Abstract">Link to Article in PubMed</a></p>
dc.rightsCopyright © The Author(s) 2016. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCausal inference
dc.subjectCausal mediation analysis
dc.subjectCausality
dc.subjectSystematic review
dc.subjectClinical Epidemiology
dc.subjectEpidemiology
dc.subjectHealth Services Research
dc.titleImplementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies
dc.typeJournal Article
dc.source.journaltitleBMC research notes
dc.source.volume9
dc.source.issue1
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=3792&amp;context=oapubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/oapubs/2787
dc.identifier.contextkey8956661
refterms.dateFOA2022-08-23T16:41:54Z
html.description.abstract<p>BACKGROUND: Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown.</p> <p>METHODS: We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized.</p> <p>RESULTS: Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce.</p> <p>CONCLUSIONS: Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.</p>
dc.identifier.submissionpathoapubs/2787
dc.contributor.departmentMorningside Graduate School of Biomedical Sciences
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages354
dc.description.thesisprogramClinical and Population Health Research


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Copyright © The Author(s) 2016. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as Copyright © The Author(s) 2016. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.