Show simple item record

dc.contributor.authorYeboah, Edmund
dc.contributor.authorMauer, Nicole Sibilla
dc.contributor.authorHufstedler, Heather
dc.contributor.authorCarr, Sinclair
dc.contributor.authorMatthay, Ellicott C
dc.contributor.authorMaxwell, Lauren
dc.contributor.authorRahman, Sabahat
dc.contributor.authorDebray, Thomas
dc.contributor.authorde Jong, Valentijn M T
dc.contributor.authorCampbell, Harlan
dc.contributor.authorGustafson, Paul
dc.contributor.authorJänisch, Thomas
dc.contributor.authorBärnighausen, Till
dc.date.accessioned2023-02-16T21:02:51Z
dc.date.available2023-02-16T21:02:51Z
dc.date.issued2021-11-12
dc.identifier.citationYeboah E, Mauer NS, Hufstedler H, Carr S, Matthay EC, Maxwell L, Rahman S, Debray T, de Jong VMT, Campbell H, Gustafson P, Jänisch T, Bärnighausen T. Current trends in the application of causal inference methods to pooled longitudinal non-randomised data: a protocol for a methodological systematic review. BMJ Open. 2021 Nov 12;11(11):e052969. doi: 10.1136/bmjopen-2021-052969. PMID: 34772754; PMCID: PMC8593733.en_US
dc.identifier.eissn2044-6055
dc.identifier.doi10.1136/bmjopen-2021-052969en_US
dc.identifier.pmid34772754
dc.identifier.urihttp://hdl.handle.net/20.500.14038/51705
dc.description.abstractIntroduction: Causal methods have been adopted and adapted across health disciplines, particularly for the analysis of single studies. However, the sample sizes necessary to best inform decision-making are often not attainable with single studies, making pooled individual-level data analysis invaluable for public health efforts. Researchers commonly implement causal methods prevailing in their home disciplines, and how these are selected, evaluated, implemented and reported may vary widely. To our knowledge, no article has yet evaluated trends in the implementation and reporting of causal methods in studies leveraging individual-level data pooled from several studies. We undertake this review to uncover patterns in the implementation and reporting of causal methods used across disciplines in research focused on health outcomes. We will investigate variations in methods to infer causality used across disciplines, time and geography and identify gaps in reporting of methods to inform the development of reporting standards and the conversation required to effect change. Methods and analysis: We will search four databases (EBSCO, Embase, PubMed, Web of Science) using a search strategy developed with librarians from three universities (Heidelberg University, Harvard University, and University of California, San Francisco). The search strategy includes terms such as 'pool*', 'harmoniz*', 'cohort*', 'observational', variations on 'individual-level data'. Four reviewers will independently screen articles using Covidence and extract data from included articles. The extracted data will be analysed descriptively in tables and graphically to reveal the pattern in methods implementation and reporting. This protocol has been registered with PROSPERO (CRD42020143148). Ethics and dissemination: No ethical approval was required as only publicly available data were used. The results will be submitted as a manuscript to a peer-reviewed journal, disseminated in conferences if relevant, and published as part of doctoral dissertations in Global Health at the Heidelberg University Hospital.en_US
dc.language.isoenen_US
dc.relation.ispartofBMJ Openen_US
dc.relation.urlhttps://doi.org/10.1136/bmjopen-2021-052969en_US
dc.rightsOpen access: This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:http:// creativecommons.org/ licenses/by-nc/4. 0/.en_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectpublic healthen_US
dc.subjectsocial medicineen_US
dc.subjectstatistics & research methodsen_US
dc.titleCurrent trends in the application of causal inference methods to pooled longitudinal non-randomised data: a protocol for a methodological systematic reviewen_US
dc.typeJournal Articleen_US
dc.source.journaltitleBMJ open
dc.source.volume11
dc.source.issue11
dc.source.beginpagee052969
dc.source.endpage
dc.source.countryEngland
dc.identifier.journalBMJ open
refterms.dateFOA2023-02-16T21:02:52Z
dc.contributor.departmentT.H. Chan School of Medicineen_US
dc.contributor.studentSabahat Rahman


Files in this item

Thumbnail
Name:
e052969.full.pdf
Size:
276.2Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

Open access: This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non-commercial. See:http:// creativecommons.org/ licenses/by-nc/4. 0/.
Except where otherwise noted, this item's license is described as Open access: This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:http:// creativecommons.org/ licenses/by-nc/4. 0/.