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Meta-Analysis of Odds Ratios: Current Good Practices.
Bei-Hung Chang1, David C Hoaglin
1Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA.
Medical Care
|February 8, 2017
Summary
Conventional meta-analysis methods for odds ratios (ORs) are unreliable due to approximations. Alternative methods, using raw trial data, offer more accurate results and are easily implemented in standard statistical software.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Medical Informatics
Background:
- Systematic reviews frequently employ meta-analyses of odds ratios (ORs).
- Traditional meta-analysis methods rely on approximations of the logarithm of ORs, leading to unreliable results.
- Despite documented issues, conventional approximate methods remain prevalent in statistical software.
Purpose of the Study:
- To promote awareness of alternative meta-analysis methods that avoid conventional approximations.
- To highlight methods applicable with widely available software for more reliable results.
- To encourage the discontinuation of unreliable conventional methods in meta-analysis.
Main Methods:
- Summarized fixed-effect and random-effects meta-analysis approaches.
- Described conventional approximate and alternative meta-analysis methods.
- Applied and compared both methods using a meta-analysis of 19 randomized trials on endoscopic sclerotherapy for cirrhosis patients.
Main Results:
- Point estimates and confidence intervals for the overall log-odds-ratio varied between conventional and alternative methods.
- Differences in results between methods can impact clinical inferences.
- Programming for alternative methods is straightforward in SAS, Stata, and R.
Conclusions:
- Alternative meta-analysis methods offer more reliable results by avoiding approximations.
- The minimal additional programming effort is not a barrier to adopting these superior methods.
- Conventional methods for meta-analysis of ORs should be abandoned due to their inherent unreliability.
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