Random-Effects Meta-analysis of Inconsistent Effects: A Time for Change
Abstract
A primary goal of meta-analysis is to improve the estimation of treatment effects by pooling results of similar studies. This article explains how the most widely used method for pooling heterogeneous studies--the Der Simonian-Laird (DL) estimator--can produce biased estimates with falsely high precision. A classic example is presented to show that use of the DL estimator can lead to erroneous conclusions. Particular problems with the DL estimator are discussed, and several alternative methods for summarizing heterogeneous evidence are presented. The authors support replacing universal use of the DL estimator with analyses based on a critical synthesis that recognizes the uncertainty in the evidence,focuses on describing and explaining the probable sources of variation in the evidence, and uses random-effects estimates that provide more accurate confidence limits than the DL estimator.
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The Knapp-Hartung approach (SJ) is known to suit systematic reviews including few studies, particularly when dealing with 5 or fewer trials.
“The Knapp-Hartung approach (SJ) is known to suit systematic reviews including few studies, particularly when dealing with 5 or fewer trials [49].”
From Mindfulness- And acceptance-based interventions for patients with fibromyalgia - A systematic review and meta-analyses (2019) · cites this paper as [49]
Many frequentist estimation methods (including REML) sometimes fail to produce reliable confidence or prediction intervals.
“Furthermore, many frequentist estimation methods (including REML) sometimes fail to produce reliable confidence or prediction intervals.”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [47]
When heterogeneity is small and study sizes are mixed, the Hartung-Knapp method produces confidence intervals that are too wide.
“when the heterogeneity is small and the study sizes are mixed, the Hartung–Knapp method produces confidence intervals that are too wide.46,47”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [47]
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- Authors
- Cornell, J. E., Mulrow, C. D., Localio, R., Stack, C. B., Meibohm, A. R., Guallar, E., & Goodman, S. N.
- Affiliations
- Johns Hopkins University (US)
- From University of Texas Health Science Center at San Antonio, San Antonio, Texas; University of Pennsylvania and American College of Physicians, Philadelphia, Pennsylvania; Johns Hopkins School of Public Health, Baltimore, Maryland; and Stanford University School of Medicine, Stanford, California.
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2014-02-18
- Volume
- 160
- Issue
- 4
- Pages
- 267-270
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W2129670822
- Cited by
- 453
- References
- 28
- Citations by year
- 2026: 17 · 2025: 34 · 2024: 30 · 2023: 17 · 2022: 26 · 2021: 54 · 2020: 34 · 2019: 58 · 2018: 42 · 2017: 41 · 2016: 51 · 2015: 28 · 2014: 20
- FWCI
- 12.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Keywords
- random-effects meta-analysis, heterogeneity, treatment effect estimation, confidence limits, critical synthesis
- MeSH terms
- Data Interpretation, Statistical, Software, Meta-Analysis as Topic, Confidence Intervals
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