Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.

ABSTRACT: Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions. Unfortunately, when different methods lead to contradicting conclusions, researchers can choose those methods that lead to a desired outcome. To avoid the condition-dependent, all-or-none choice between competing methods and conflicting results, we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1

SUBMITTER: Bartos F 

PROVIDER: S-EPMC10087723 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature