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Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and
Nicola Orsini1, Ruifeng Li, Alicja Wolk
1Unit of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. nicola.orsini@ki.se
American Journal of Epidemiology
|December 3, 2011
Summary
Two meta-analysis methods for exposure-response data were compared. While generally similar to using original data, assuming no correlation between risk estimates can bias results, especially with confounding.
Area of Science:
- Epidemiology
- Biostatistics
- Meta-analysis
Background:
- Meta-analysis of published data is crucial for synthesizing evidence.
- Log-linear exposure-response relations are common in epidemiological studies.
- Accurate estimation of relative risk is essential for public health.
Purpose of the Study:
- To compare two meta-analytic methods for estimating relative risk in ordinal exposure-response data.
- To investigate the validity of these methods when using summarized published data versus individual participant data.
- To extend methods for nonlinear exposure-response relations.
Main Methods:
- Comparison of meta-analysis results from published data with pooled analysis of original data.
- Investigation of conditions leading to breakdown of meta-analytic approximations.
- Extension of methods to accommodate nonlinear exposure-response relationships.
- Illustration using data on alcohol consumption and cancer risks.
Main Results:
- Differences between meta-analysis of summarized data and pooled analysis of original data were generally small.
- Incorrectly assuming no correlation between risk estimates led to biased confidence intervals and P values, particularly with confounding.
- Methods were extended to handle nonlinear exposure-response relations.
Conclusions:
- Meta-analysis of summarized exposure-response data can provide reliable estimates, but assumptions about correlation are critical.
- Confounding can significantly bias results if not properly addressed in meta-analytic models.
- User-friendly software (Stata, SAS) can implement these advanced meta-analytic techniques.
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