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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Research Synthesis Methods

ISSN

1759-2879

e-ISSN

1759-2887

Svazek periodika

14

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

99-116

Kód UT WoS článku

000837024800001

EID výsledku v databázi Scopus

2-s2.0-85135529330