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Published on: November 27, 2019
An Introduction to Individual Participant Data Meta-analysis.
Areti Angeliki Veroniki1, Georgios Seitidis2, Georgios Tsivgoulis2
1From the Knowledge Translation Program (A.A.V.), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto; Institute of Health Policy Management and Evaluation (A.A.V.), University of Toronto, Ontario, Canada; Department of Primary Education (G.S., D.M.), School of Education, University of Ioannina, Greece; Department of Neurology (G.T.), University of Tennessee Health Sciences Center, Memphis; Second Department of Neurology (G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece; Division of Neurology (A.H.K.), McMaster University/Population Health Research Institute, Hamilton, Ontario, Canada; and Faculté de Médecine (D.M.), Université Paris Descartes, France. a.veroniki@utoronto.ca.
Individual participant data meta-analysis (IPD-MA) offers superior insights over aggregate data meta-analysis by enabling detailed subgroup analysis and accounting for missing data. This gold standard approach strengthens evidence for clinical decision-making.
Area of Science:
- Medical research methodology
- Biostatistics
- Evidence synthesis
Background:
- Individual participant data meta-analysis (IPD-MA) is the gold standard for strengthening evidence in clinical decision-making.
- Aggregate data (AD) meta-analysis has limitations including potential confounding and aggregation bias.
Approach:
- Presents the importance, properties, and main approaches of conducting IPD-MA.
- Demonstrates obtaining subgroup effects via interaction term estimation.
- Utilizes 2-stage and 1-stage approaches for IPD-MA.
Key Points:
- IPD-MA allows standardization of outcome definitions, reanalysis with consistent models, and handling of missing data.
- Participant-level covariates can explore intervention-by-covariate interactions, tailoring effects to individual characteristics.
- IPD-MA enhances statistical analysis quality compared to AD reviews.
Conclusions:
- IPD-MA provides a robust method for exploring intervention-by-covariate interactions and tailoring treatment effects.
- A significant limitation is the challenge and resource intensity of retrieving individual participant data.
- Careful planning of time and resources is crucial before initiating an IPD-MA.
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