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Published on: November 27, 2019
Bayesian evidence synthesis as a flexible alternative to meta-analysis: A simulation study and empirical
Elise van Wonderen1,2, Mariëlle Zondervan-Zwijnenburg3, Irene Klugkist3
1Amsterdam Center for Language and Communication, University of Amsterdam, Spuistraat 134, Amsterdam, 1012 VB, The Netherlands. e.vanwonderen@uva.nl.
Bayesian evidence synthesis offers a flexible alternative to meta-analysis for diverse studies. This method combines research at the hypothesis level, enabling synthesis when traditional meta-analysis is not feasible.
Area of Science:
- Psychology
- Linguistics
- Statistics
Background:
- Synthesizing research findings enhances scientific robustness.
- Meta-analysis is a common synthesis method but requires comparable effect sizes.
- Highly diverse study designs or variables can preclude traditional meta-analysis.
Purpose of the Study:
- Introduce Bayesian evidence synthesis as an alternative to meta-analysis.
- Demonstrate when Bayesian evidence synthesis diverges from meta-analysis using simulations.
- Provide guidance for researchers interpreting synthesis results.
Main Methods:
- Bayesian evidence synthesis combines studies at the hypothesis level.
- Simulations were conducted to compare Bayesian evidence synthesis with meta-analysis.
- The method was applied to a real-world meta-analysis on statistical learning in developmental language disorder.
Main Results:
- Bayesian evidence synthesis offers flexibility when studies are heterogeneous.
- Simulations identified conditions under which Bayesian evidence synthesis differs from meta-analysis.
- Empirical application demonstrated the practical use of Bayesian evidence synthesis.
Conclusions:
- Bayesian evidence synthesis is a viable alternative for synthesizing diverse scientific studies.
- Understanding the divergence between Bayesian evidence synthesis and meta-analysis is crucial for accurate interpretation.
- Further research should explore the strengths and weaknesses of Bayesian evidence synthesis.
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