Multivariate meta-analysis for non-linear and other multi-parameter associations
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In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure-response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure.
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City-specific estimates across seven cities were combined using a univariate random-effects meta-analysis to estimate the average risk of ozone-related mortality, accounting for variations across cities.
“In the second stage, city-specific estimates across seven cities were combined to estimate the average risk of ozone-related mortality using a univariate random-effects meta-analysis (Gasparrini et al., 2012).”
From Acute effect of ozone exposure on daily mortality in seven cities of Jiangsu Province, China: no clear evidence for threshold (2017) · cites this paper as Gasparrini et al., 2012
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- Authors
- Gasparrini, A., Armstrong, B., & Kenward, M. G.
- Affiliations
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Gasparrini, A.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2012-07-16
- Volume
- 31
- Issue
- 29
- Pages
- 3821-3839
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- PMC
- PMC3546395
- OpenAlex
- W2162768498
- Cited by
- 772
- References
- 78
- Citations by year
- 2026: 49 · 2025: 57 · 2024: 125 · 2023: 84 · 2022: 84 · 2021: 68 · 2020: 81 · 2019: 49 · 2018: 46 · 2017: 48 · 2016: 32 · 2015: 28 · 2014: 15 · 2013: 4 · 2012: 1
- FWCI
- 17.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Statistics in Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Funders
- Medical Research Council (G1002296, G0701030)
- 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 › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Keywords
- multivariate meta-analysis, meta-regression, nonlinear relationship, two-stage analysis, exposure-response relationship, temperature-mortality relationship, non-accidental mortality, time series
- MeSH terms
- Environmental Exposure, Humans, Temperature, Midwestern United States, Meta-Analysis as Topic, Relative Value Scales, Multivariate Analysis, Linear Models
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