Combining Risk Estimates from Observational Studies with Different Exposure Cutpoints: A Meta-analysis on Body Mass Index and Diabetes Type 2
Open accessAbstract
Studies on a dose-response relation often report separate relative risks for several risk classes compared with a referent class. When performing a meta-analysis of such studies, one has to convert these relative risks into an overall relative risk for a continuous effect. Apart from taking the dependence between separate relative risks into account, this implies assigning an exposure level to each risk factor class and allowing for the nonlinearity of the dose-response relation. The authors describe a relatively simple method solving these problems. As an illustration, they applied this method in a meta-analysis of the association between body mass index and diabetes type 2, restricted to results of follow-up studies (n=31). Results were compared with a more ad hoc method of assigning exposure levels and with a method in which the nonlinearity of the dose-response method was not taken into account. Differences with the ad hoc method were larger in studies with fewer categories. Not incorporating the nonlinearity of the dose response leads to an overestimation of the pooled relative risk, but this bias is relatively small.
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Other topics included meta-regression, while few articles focused on more specific statistical issues, such as dose-response analysis, credibility ceilings, bias adjustment, hierarchical models, or regression bias.
“Other topics included meta-regression, while few articles focused on more specific statistical issues, such as dose-response analysis [81, 109, 111, 113], credibility ceilings [110], bias adjustment [112], hierarchical models [117], or regression bias [108].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [109]
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- Authors
- Hartemink, N., Boshuizen, H. C., Nagelkerke, N. J. D., Jacobs, M. A. M., & van Houwelingen, H. C.
- Affiliations
- National Institute for Public Health and the Environment (NL)
- United Arab Emirates University (AE)
- Corresponding author
- Hartemink, Nienke
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-04-12
- Volume
- 163
- Issue
- 11
- Pages
- 1042-1052
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2066017793
- Cited by
- 226
- References
- 42
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 13 · 2023: 11 · 2022: 12 · 2021: 18 · 2020: 13 · 2019: 18 · 2018: 16 · 2017: 15 · 2016: 12 · 2015: 15 · 2014: 13 · 2013: 12 · 2012: 14
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Rivm Repository (Netherlands National Institute for Public Health and the Environment) (submittedVersion)
- Rivm (National Institute for Public Health and the Environment) (submittedVersion, other-oa)
- 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
- body mass index, type 2 diabetes, dose-response relationship, observational studies
- MeSH terms
- Data Interpretation, Statistical, Diabetes Mellitus, Type 2, Humans, Risk Factors, Body Mass Index, Risk Assessment
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