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Protocol: Machine learning for selecting moderators in meta-analysis: A systematic review of methods and their
Jens Dietrichson1, Rasmus Klokker1, Trine Filges1
1Quantitative Methods, VIVE-The Danish Center for Social Science Research Copenhagen Denmark.
Campbell Systematic Reviews
|December 12, 2024
Summary
This systematic review identifies machine learning (ML) methods for moderator meta-analysis and their applications in health, medical, and social sciences. It compares ML approaches to traditional meta-regression for hypothesis generation and method selection.
Area of Science:
- Methodology in Health and Social Sciences
- Statistical Learning and Data Science
Background:
- Systematic reviews are crucial for synthesizing research evidence.
- Identifying moderators in meta-analysis enhances understanding of intervention effects.
- Machine learning (ML) offers advanced analytical capabilities for complex data.
Purpose of the Study:
- To systematically identify and describe ML methods applicable to moderator meta-analysis.
- To document the application of these ML methods in health, medical, and social science research.
- To evaluate the utility of ML methods for hypothesis generation and comparison with traditional meta-regression.
Main Methods:
- A systematic meta-review following Campbell Collaboration (MECCIR) guidelines.
- Identification and description of ML techniques for moderator meta-analysis.
- Application of identified ML methods to tutoring intervention data for performance comparison.
Main Results:
- A curated list of ML methods designed for moderator meta-analysis will be produced.
- Examples of ML method applications in health, medical, and social science meta-analyses will be detailed.
- Comparative analysis of ML methods against standard meta-regression techniques will be performed using empirical data.
Conclusions:
- ML methods hold potential for advancing moderator meta-analysis in scientific research.
- This review will provide researchers with insights into novel analytical tools for evidence synthesis.
- The findings will aid in selecting appropriate methods for hypothesis generation and moderator analysis.
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