When should meta‐analysis avoid making hidden normality assumptions?

DOI10.1002/bimj.201800071zbMath1412.62157OpenAlexW2886282933WikidataQ90657287 ScholiaQ90657287MaRDI QIDQ4622547

Dan Jackson, Ian R. White

Publication date: 12 February 2019

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201800071

zbMATH Keywords

central limit theoremrandom effects modelsnormal approximationdistributional assumptions

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Central limit and other weak theorems (60F05) Foundations and philosophical topics in statistics (62A01) Research exposition (monographs, survey articles) pertaining to statistics (62-02)

Related Items (8)

On the exact null-distribution of a test for homogeneity of the risk ratio in meta-analysis of studies with rare events ⋮ Jointly pooling aggregated effect sizes and their standard errors from studies with continuous clinical outcomes ⋮ Frequentist performances of Bayesian prediction intervals for random‐effects meta‐analysis ⋮ A bias‐corrected meta‐analysis model for combining, studies of different types and quality ⋮ Bounds for the weight of external data in shrinkage estimation ⋮ Random‐effects meta‐analysis models for the odds ratio in the case of rare events under different data‐generating models: A simulation study ⋮ Contribution to the discussion of “When should meta‐analysis avoid making hidden normality assumptions?” ⋮ Investigating heterogeneity in meta-analysis of studies with rare events. Estimating the amount of heterogeneity

Uses Software

• Stata
• metafor
• METANDI

Cites Work

• Hans van Houwelingen and the Art of Summing up
• Integrated Likelihood Inference in Small Sample Meta-analysis for Continuous Outcomes
• Testing for Homogeneity in Meta-Analysis I. The One-Parameter Case: Standardized Mean Difference
• Modeling between-trial variance structure in mixed treatment comparisons
• Meta‐analysis of clinical trials with rare events
• Meta-analysis of Binary Data Using Profile Likelihood
• A modified random-effect procedure for combining risk difference in sets of 2×2 tables from clinical trials
• Introduction to Meta‐Analysis
• Nonparametric estimation of heterogeneity variance for the standardised difference used in meta-analysis
• Recent Developments in Computer-Assisted Analysis of Mixtures
• Valid Inference in Random Effects Meta‐Analysis
• Consensus Values and Weighting Factors
• An Alternative Method for Meta-Analysis
• Assessing the Amount of Heterogeneity in Random‐Effects Meta‐Analysis
• On random-effects meta-analysis
• Some general points in estimating heterogeneity variance with the DerSimonian-Laird estimator

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