Heterogeneity in Meta-analysis of Data from Epidemiologic Studies: A Commentary

Colditz, Graham A.; Burdick, Elisabeth; Mosteller, Frederick

doi:10.1093/oxfordjournals.aje.a117644

Cited by 245Meta-analysis and systematic reviewsStatistical Methods in EpidemiologyAdvanced Causal Inference Techniques

Heterogeneity in Meta-analysis of Data from Epidemiologic Studies: A Commentary

Colditz, Graham A., Burdick, Elisabeth, Mosteller, Frederick·1995·American Journal of Epidemiology

DOI 10.1093/oxfordjournals.aje.a117644

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Graham A. Colditz, Elisabeth Burdick, Frederick Mosteller; Heterogeneity in Meta-analysis of Data from Epidemiologic Studies: A Commentary, American Journal of

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Two articles recommended stratifying by study design if heterogeneity is large and adjusting for study design if heterogeneity is small.
“Two articles recommended stratifying by study design if heterogeneity is large and adjusting for study design if heterogeneity is small [51, 77].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [77]
The majority of articles advised against the use of scales but 12 articles recommended their use.
“The majority of articles advised against the use of scales but 12 articles recommended their use [29, 30, 48–50, 52, 53, 56, 66, 77, 86, 91].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [77]
Some authors agreed that study design should be investigated as a potential source of between study heterogeneity, and others recommended presenting meta-analyses for each study type alongside results from an overall meta-analysis.
“Some authors agreed that study design should be investigated as a potential source of between study heterogeneity [8, 34, 42, 77], and others recommended presenting meta-analyses for each study type alongside results from an overall meta-analysis [42, 83].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [77]
One common recommendation was to explore heterogeneity in sensitivity analyses, comparing studies stratified by design or clinical features.
“One common recommendation was to explore heterogeneity in sensitivity analyses, comparing studies stratified by design or clinical features [12, 16, 29, 34, 39, 51, 55, 56, 66, 77, 85, 100, 102].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [77]
Many recommended to define these subgroups a priori to reduce the risk of false positive findings.
“Many recommended to define these subgroups a priori to reduce the risk of false positive findings [33, 52, 77, 106].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [77]

This test-based approach was often criticised because of low power to detect heterogeneity in case of few (< 10) included studies.
“This approach was, however, often criticised because of low power to detect heterogeneity in case of few (< 10) included studies [52, 77, 101, 104, 116, 118].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [77]
The use of a random-effects model for meta-analysis of observational studies by default was frequently recommended.
“Second, the use of a random-effects model for meta-analysis of observational studies by default was frequently recommended [8, 16, 29, 34, 70, 77, 100, 101].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [77]
A common misinterpretation of the random-effects assumption is that the studies themselves are sampled from a population of studies.
“A common misinterpretation of the random-effects assumption is that the studies themselves are sampled from a population of studies (Hedges, 1987; Anello and Fleiss, 1995; Colditz et al., 1995).”
From A Re-Evaluation of Random-Effects Meta-Analysis (2009) · cites this paper as Colditz et al., 1995
Interpreting the random-effects assumption as random sampling of studies is inappropriate because studies are individually designed dependent on previous results and the evolutionary process of scientific research.
“This implies that the types of participants, natures of exposures, design aspects, and so on, are randomly selected from a distribution of possibilities, which is an inappropriate interpretation because studies are individually designed dependent on the results of previous studies (Peto, 1987) and the evolutionary process of scientific research (Colditz et al., 1995).”
From A Re-Evaluation of Random-Effects Meta-Analysis (2009) · cites this paper as Colditz et al., 1995

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Authors: Colditz, G. A., Burdick, E., & Mosteller, F.
Affiliations: Harvard University (US)
Corresponding author: Colditz, Graham A.
ORCID: Colditz, Graham A.
Venue: American Journal of Epidemiology
Publisher: Oxford University Press (OUP)
Published: 1995-08-15
Volume: 142
Issue: 4
Pages: 371-382
ISSN: 0002-9262 (ISSN-L)
1476-6256
DOI: 10.1093/oxfordjournals.aje.a117644
OpenAlex: W2269058722
Cited by: 245
References: 52
Citations by year: 2026: 3 · 2025: 5 · 2024: 4 · 2023: 6 · 2022: 9 · 2021: 10 · 2020: 7 · 2019: 5 · 2018: 9 · 2017: 12 · 2016: 8 · 2015: 10 · 2014: 12 · 2013: 7 · 2012: 8
FWCI: 2.08
Type: journal article
Language: en
Open access: closed
Locations: American Journal of Epidemiology (publishedVersion)
PubMed (publishedVersion)
Funders: Agency for Healthcare Research and Quality (HS05936)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: heterogeneity, meta-analysis, epidemiological studies, study heterogeneity, pooled estimates
MeSH terms: Epidemiologic Methods, Models, Theoretical, Meta-Analysis as Topic

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Heterogeneity in Meta-analysis of Data from Epidemiologic Studies: A Commentary

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