Thinking big: large-scale collaborative research in observational epidemiology
Abstract
Efforts to identify risk factors for chronic diseases have tended to involve observational studies characterised by relatively few disease outcomes. In the absence of individual studies of sufficiently large size, synthesis of available evidence from multiple smaller studies can help enhance statistical power and aid appropriate interpretation. While meta-analyses of published findings can help prioritize research hypotheses, they are inherently limited by the scale of the evidence available for review and by vulnerability to potential reporting biases. By contrast, collaborative analyses of individual participant data from a comprehensive set of relevant epidemiological studies can offer several advantages over moderately sized individual studies or meta-analyses of aggregated data. This review describes those advantages with reference to selected examples.
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Collaboration and pooling of individual participant data across research centers can enable much larger sample sizes, can afford subgroup analyses, and is more effective than meta-analyses.
“Collaboration and pooling of individual participant data across research centers can enable much larger sample sizes, can afford subgroup analyses, and is more effective than meta-analyses (3).”
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“The three main mechanisms generating pain are [1] nociceptive, [2] inflammatory, and [3] neuropathic or centrally generated pain, although it is likely that a combination of these processes occurs in many patients.”
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- Authors
- Thompson, A.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Thompson, Alexander
- Venue
- European Journal of Epidemiology
- Published
- 2009-12
- Volume
- 24
- Issue
- 12
- Pages
- 727-731
- ISSN
- 0393-2990 (ISSN-L)
- 1573-7284
- OpenAlex
- W2151577907
- Cited by
- 58
- References
- 25
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 2 · 2022: 3 · 2021: 5 · 2020: 5 · 2019: 2 · 2018: 3 · 2017: 3 · 2016: 4 · 2015: 3 · 2014: 5 · 2013: 1 · 2012: 2
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- European Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- individual patient data, collaborative analysis, observational epidemiology, meta-analysis limitations, reporting bias, statistical power, chronic disease risk factors
- MeSH terms
- Chronic Disease, Cooperative Behavior, Humans, Risk Factors, Meta-Analysis as Topic, Epidemiologic Studies, Observation
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