Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines

Heather Hufstedler*, Nicole Mauer, Edmund Yeboah, Sinclair Carr, Sabahat Rahman, Alexander M. Danzer, Thomas P.A. Debray, Valentijn M.T. de Jong, Harlan Campbell, Paul Gustafson, Lauren Maxwell, Thomas Jaenisch, Ellicott C. Matthay, Till Bärnighausen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Article number91
Pages (from-to)1-12
Number of pages12
JournalBMC Medical Research Methodology
Volume24
Issue number1
DOIs
Publication statusPublished - 19 Apr 2024

Keywords

  • Causal inference
  • Cohort studies
  • Individual participant data
  • Longitudinal observational data
  • Meta-analysis
  • Pooling

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