Author
Listed:
- Jessica Gronsbell
(Department of Statistical Sciences, University of Toronto, Torronto, ON M5S 1A1, Canada)
- Zachary R. McCaw
(Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)
- Timothy Regis
(Department of Statistical Sciences, University of Toronto, Torronto, ON M5S 1A1, Canada)
- Lu Tian
(Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA)
Abstract
Meta-analysis aggregates information across related studies to provide more reliable statistical inference and has been a vital tool for assessing the safety and efficacy of many high-profile pharmaceutical products. A key challenge in conducting a meta-analysis is that the number of related studies is typically small. Applying classical methods that are asymptotic in the number of studies can compromise the validity of inference, particularly when heterogeneity across studies is present. Moreover, serious adverse events are often rare and can result in one or more studies with no events in at least one study arm. Practitioners remove studies in which no events have occurred in one or both arms or apply arbitrary continuity corrections (e.g., adding one event to arms with zero events) to stabilize or define effect estimates in such settings, which can further invalidate subsequent inference. To address these significant practical issues, we introduce an exact inference method for random effects meta-analysis of a treatment effect in the two-sample setting with rare events, which we coin “XRRmeta”. In contrast to existing methods, XRRmeta provides valid inference for meta-analysis in the presence of between-study heterogeneity and when the event rates, number of studies, and/or the within-study sample sizes are small. Extensive numerical studies indicate that XRRmeta does not yield overly conservative inference. We apply our proposed method to two real-data examples using our open-source R package.
Suggested Citation
Jessica Gronsbell & Zachary R. McCaw & Timothy Regis & Lu Tian, 2025.
"Exact Inference for Random Effects Meta-Analyses for Small, Sparse Data,"
Stats, MDPI, vol. 8(1), pages 1-17, January.
Handle:
RePEc:gam:jstats:v:8:y:2025:i:1:p:5-:d:1561797
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:5-:d:1561797. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.