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Quantifying heterogeneity in a meta‐analysis
Julian P. T. Higgins, Simon G. Thompson. Statistics in Medicine, 2002-05-21
Topic: Risk, prevention and care in population studies
The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity.
Why it matters
A statistics paper proposing measures, H and I2, of how much the impact of differences between studies (heterogeneity) affects a meta-analysis. It is not about dementia; the authors recommend that one or both be presented in published meta-analyses.
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How this study scored 49/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on strength of evidence, so that was left out: it earned 47 of the 95 points we could judge it on, which is 49 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Statistics in Medicine) |
| Journal's citation record | 3 / 4 | Statistics in Medicine: journal h-index 309 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | — / 5 | No evidence rating recorded — left out of this score |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2002 — older than 20 years, still counted |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 7 / 7 | Cited 36,602 times (OpenAlex, 2026-10-10) |
| Citations per year | 5 / 5 | 1,407.8 citations a year over 26 year(s) since 2002 |
| Impact for its field and year | 6 / 6 | Cited 156.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 4 / 4 | Cited 7,808 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Simon G. Thompson: h-index 44 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | MRC Biostatistics Unit: institution h-index 415 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 12111919) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 47 / 95 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 47 of the 95 points we could judge it on is 49 out of 100. |
| Total | 49 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta‐analysis. Statistics in Medicine. https://doi.org/10.1002/sim.1186
Vancouver
Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta‐analysis. Statistics in Medicine. 2002. doi:10.1002/sim.1186. PMID: 12111919.
BibTeX
@article{higgins2002-12111919,
author = {Higgins, Julian P. T. and Thompson, Simon G.},
title = {{Quantifying heterogeneity in a meta‐analysis}},
journal = {Statistics in Medicine},
year = {2002},
doi = {10.1002/sim.1186},
pmid = {12111919},
url = {https://doi.org/10.1002/sim.1186}
}
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