DESCRIPTION:
Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many scientific areas including medicine and health, social sciences, education, psychology, ecology and economics. Recent years have seen an explosion of methods for handling complexities in meta-analysis, including explained and unexplained heterogeneity between studies, publication bias, and sparse data.
At the same time, meta-analysis has been extended beyond simple two-group comparisons of continuous and binary outcomes to comparing and ranking the outcomes from multiple groups, to complex observational studies, to assessing heterogeneity of effects, and to survival and multivariate outcomes. Many of these methods are statistically complex and are tailored to specific types of data.
Key features:
- Rigorous coverage of the full range of current statistical methodology used in meta-analysis
- Comprehensive, coherent and unified overview of the statistical foundations behind meta-analysis
- Detailed description of the primary methods for both univariate and multivariate data
- Computer code to reproduce examples in chapters
- Thorough review of the literature with thousands of references
- Applications to specific types of biomedical and social science data
This book is for a broad audience of graduate students, researchers and practitioners interested in the theory and application of statistical methods for meta-analysis. It is written at the level of graduate courses in statistics, but will be of interest to and readable for quantitative scientists from a range of disciplines.
The book can be used as a graduate level textbook, as a general reference for methods, or as an introduction to specialized topics using state-of-the art methods.
CONTENTS:
1. Introduction to systematic review and meta-analysis
2. General themes in meta-analysis
3. Choice of effect measure and issues in extracting outcome data
4. Analysis of univariate study-level summary data using normal models
5. Exact likelihood methods for group-based summaries
6. Bayesian methods for meta-analysis
7. Meta-regression
8. Individual participant data meta-analysis
9. Multivariate meta-analysis
10. Network meta-analysis
11. Model Checking in meta-analysis
12. Handling internal and external biases: quality and relevance of studies
13. Publication and outcome reporting bias
14. Control risk regression
15. Multivariate meta-analysis of survival proportions
16. Meta-analysis of correlations, correlation matrices and their functions
17. The meta-analysis of genetic studies
18. Meta-analysis of dose-response relationships
19. Meta-analysis of diagnostic tests
20. Meta-analytic approach to evaluation of surrogate endpoints
21. Meta-analysis of epidemiological data, with a focus on individual participant data
22. Meta-analysis of prediction models
23. Using meta-analysis to plan further research