학술논문
Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines.
Document Type
Academic Journal
Author
Hufstedler H; Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany. heather.hufstedler@uni-heidelberg.de.; Mauer N; Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.; Yeboah E; Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.; Carr S; Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.; Center for Interdisciplinary Addiction Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.; Rahman S; University of Massachusetts Medical School, University of Massachusetts, Worcester, USA, MA.; Danzer AM; KU Eichstätt-Ingolstadt, Ingolstadt School of Management and Economics (WFI), Ingolstadt, Germany.; IZA, Bonn, Germany.; CESifo, Munich, Germany.; Debray TPA; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.; Smart Data Analysis and Statistics B.V, Utrecht, The Netherlands.; de Jong VMT; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.; Campbell H; Department of Statistics, University of British Columbia, Vancouver, Canada, BC.; Gustafson P; Department of Statistics, University of British Columbia, Vancouver, Canada, BC.; Maxwell L; Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.; Jaenisch T; Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.; Center for Global Health, Colorado School of Public Health, Aurora, USA, CO.; Department of Epidemiology, Colorado School of Public Health, Aurora, USA.; Matthay EC; Department of Population Health, New York University Grossman School of Medicine, New York City, USA, NY.; Bärnighausen T; Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.; Harvard T H Chan School of Public Health, Harvard University, Boston, USA, MA.
Source
Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE
Subject
Language
English
Abstract
Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.
(© 2024. The Author(s).)
(© 2024. The Author(s).)