Robust Bayesian Meta-Analysis: Model-Averaging Across Complementary Publication Bias Adjustment Methods

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00559478" target="_blank" >RIV/67985807:_____/23:00559478 - isvavai.cz</a>

Result on the web

<a href="https://dx.doi.org/10.1002/jrsm.1594" target="_blank" >https://dx.doi.org/10.1002/jrsm.1594</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/jrsm.1594" target="_blank" >10.1002/jrsm.1594</a>

Result language

angličtina

Original language name

Robust Bayesian Meta-Analysis: Model-Averaging Across Complementary Publication Bias Adjustment Methods

Original language description

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) selection models of p-values and (2) models adjusting for small-study effects. The resulting model ensemble weights the estimates and the evidence for the absence/presence of the effect from the competing approaches with the support they receive from the data. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of complementary publication bias adjustment methods.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Research Synthesis Methods

ISSN

1759-2879

e-ISSN

1759-2887

Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

99-116

UT code for WoS article

000837024800001

EID of the result in the Scopus database

2-s2.0-85135529330