| _version_ | 1867002295538941952 |
|---|---|
| author | Pär Villner Matteo Bottai |
| author_facet | Pär Villner Matteo Bottai Pär Villner Matteo Bottai |
| collection | Wiley Open Access |
| contents | A Review of Methods for Research Synthesis Pär Villner Matteo Bottai Statistics in Medicine ABSTRACT Meta‐analysis consists of a wide range of methods for summarizing existing research, often by aggregating summary statistics. The dominant methods are the fixed effect and the random effects models, which assume that all studies included in a meta‐analysis are similar. In many scenarios, the available studies differ in important ways, for example, in terms of research design and sample population. To handle this heterogeneity, more advanced methods are required. In this article, we review some of these methods that have been proposed in the past decades: hierarchical models, bias adjustment and quality weighting methods, Bayesian methods, and decision‐centered meta‐analysis. We aim to describe the theoretical rationale behind the methods and to give examples of applications. Each method has advantages and limitations, and we consider ways of combining methods. 10.1002/sim.70314 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/sim.70314 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70314 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | A Review of Methods for Research Synthesis Pär Villner Matteo Bottai Statistics in Medicine A Review of Methods for Research Synthesis Pär Villner Matteo Bottai Statistics in Medicine ABSTRACT Meta‐analysis consists of a wide range of methods for summarizing existing research, often by aggregating summary statistics. The dominant methods are the fixed effect and the random effects models, which assume that all studies included in a meta‐analysis are similar. In many scenarios, the available studies differ in important ways, for example, in terms of research design and sample population. To handle this heterogeneity, more advanced methods are required. In this article, we review some of these methods that have been proposed in the past decades: hierarchical models, bias adjustment and quality weighting methods, Bayesian methods, and decision‐centered meta‐analysis. We aim to describe the theoretical rationale behind the methods and to give examples of applications. Each method has advantages and limitations, and we consider ways of combining methods. 10.1002/sim.70314 http://creativecommons.org/licenses/by/4.0/ |
| title | A Review of Methods for Research Synthesis |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70314 |