Artikel ; Online: Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework.
2022 Band 14, Heft 1, Seite(n) 68–78
Abstract: Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need ... ...
Abstract | Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately-which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups. |
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Mesh-Begriff(e) | Humans ; Research Design ; Bias |
Sprache | Englisch |
Erscheinungsdatum | 2022-07-28 |
Erscheinungsland | England |
Dokumenttyp | Meta-Analysis ; Journal Article |
ZDB-ID | 2548499-0 |
ISSN | 1759-2887 ; 1759-2879 |
ISSN (online) | 1759-2887 |
ISSN | 1759-2879 |
DOI | 10.1002/jrsm.1590 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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