Prediction intervals for random-effects meta-analysis based on confidence distributions and Edgington's method.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Prediction intervals for random-effects meta-analysis based on confidence distributions and Edgington's method.
Συγγραφείς:	Kronthaler D; Epidemiology, Biostatistics and Prevention Institute, Department of Biostatistics, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland., Held L; Epidemiology, Biostatistics and Prevention Institute, Department of Biostatistics, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland.
Πηγή:	Biometrics [Biometrics] 2026 Oct 07; Vol. 82 (4).
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
Imprint Name(s):	Publication: March 2024- : [Oxford] : Oxford University Press Original Publication: Alexandria Va : Biometric Society
Ιατρικοί όροι (MeSH):	Meta-Analysis as Topic* , Models, Statistical*, Biometry/methods ; Computer Simulation ; Confidence Intervals ; Humans ; Uncertainty ; Prediction Algorithms ; Data Interpretation, Statistical
Περίληψη:	Statistical inference about the average effect in random-effects meta-analysis has been considered insufficient in the presence of substantial between-study heterogeneity. Predictive distributions are well suited for quantifying heterogeneity since they are interpretable on the effect scale and provide clinically relevant information about future events. We construct predictive distributions accounting for uncertainty through confidence distributions from Edgington's p-value combination method and the generalized heterogeneity statistic. Simulation results suggest that 95% prediction intervals typically achieve nominal coverage when more than three studies are available and effectively reflect skewness of effect estimates in scenarios with 20 or fewer studies. Formulations that ignore uncertainty in heterogeneity estimation typically fail to achieve correct coverage, underscoring the need for this adjustment in random-effects meta-analysis. (© The Author(s) 2026. Published by Oxford University Press on behalf of The International Biometric Society.)
Contributed Indexing:	Keywords: p-value function; confidence distribution; estimation uncertainty; heterogeneity; meta-analysis; prediction
Entry Date(s):	Date Created: 20261007 Date Completed: 20261007 Latest Revision: 20261008
Update Code:	20261009
DOI:	10.1093/biomtc/ujag167
PMID:	42842299
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1541-0420
DOI:	10.1093/biomtc/ujag167