Comparison of bias adjustment in meta-analysis using data-based and opinion-based methods.
JBI Evid Synth
; 22(3): 434-440, 2024 Mar 01.
Article
em En
| MEDLINE
| ID: mdl-38410861
ABSTRACT
INTRODUCTION:
Several methods exist for bias adjustment of meta-analysis results, but there has been no comprehensive comparison with unadjusted methods. We compare 6 bias-adjustment methods with 2 unadjusted methods to examine how these different methods perform.METHODS:
We re-analyzed a meta-analysis that included 10 randomized controlled trials. Two data-based methods (Welton's data-based approach and Doi's quality effects model) and 4 opinion-informed methods (opinion-based approach, opinion-based distributions combined statistically with data-based distributions, numerical opinions informed by data-based distributions, and opinions obtained by selecting areas from data-based distributions) were used to incorporate methodological quality information into the meta-analytical estimates. The results of these 6 methods were compared with 2 unadjusted models the DerSimonian-Laird random effects model and Doi's inverse variance heterogeneity model.RESULTS:
The 4 opinion-based methods returned the random effects model estimates with wider uncertainty. The data-based and quality effects methods returned different results and aligned with the inverse variance heterogeneity method with some minor downward bias adjustment.CONCLUSION:
Opinion-based methods seem to only add uncertainty rather than bias adjust.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Projetos de Pesquisa
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Viés
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article