A Primer on Bayesian Model-Averaged Meta-Analysis

  title={A Primer on Bayesian Model-Averaged Meta-Analysis},
  author={Quentin F. Gronau and Daniel W. Heck and Sophie Wilhelmina Berkhout and Julia M. Haaf and Eric-Jan Wagenmakers},
  journal={Advances in Methods and Practices in Psychological Science},
Meta-analysis is the predominant approach for quantitatively synthesizing a set of studies. If the studies themselves are of high quality, meta-analysis can provide valuable insights into the current scientific state of knowledge about a particular phenomenon. In psychological science, the most common approach is to conduct frequentist meta-analysis. In this primer, we discuss an alternative method, Bayesian model-averaged meta-analysis. This procedure combines the results of four Bayesian meta… 

Figures and Tables from this paper

Benefits of Bayesian Model Averaging for Mixed-Effects Modeling

Bayes factors allow researchers to test the effects of experimental manipulations in within-subjects designs using mixed-effects models. van Doorn et al. (2021) showed that such hypothesis tests can

Adjusting for Publication Bias in JASP and R: Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis

This tutorial demonstrates how to conduct a publication-bias-adjusted meta-analysis in JASP and R and introduces robust Bayesian meta- analysis, a Bayesian approach that simultaneously considers both PET-PEESE and selection models.

Bayesian model‐averaged meta‐analysis in medicine

A Bayesian model-averaged (BMA) meta-analysis for standardized mean differences is outlined in order to quantify evidence for both treatment effectiveness δ and across-study heterogeneity τ and to propose specific empirical prior distributions.

Assessing inter-rater reliability with heterogeneous variance components models: Flexible approach accounting for contextual variables

Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables such as the rater’s or ratee’s gender, major, or experience.

Central questions about meta-analyses in psychological research: An annotated reading list

A large amount of literature is available for researchers who are interested in performing meta-analyses in psychology. However, due to a large number of available sources and meta-analytic

Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue

Meta-analyses that statistically synthesize evidence from multiple research studies can play an important role in advancing evidence-informed prevention science. When done in the context of a

Informed Bayesian survival analysis

Background We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to

How best to quantify replication success? A simulation study on the comparison of replication success metrics

Generally, meta-analytic approaches seem to slightly outperform metrics that evaluate single studies, except in the scenario of extreme publication bias, where this pattern reverses.

Bayes factors and posterior estimation: Two sides of the very same coin

Recently, several researchers have claimed that conclusions obtained from a Bayes factor may contradict those obtained from Bayesian estimation. In this short paper, we wish to point out that no such

Comparing the evidential strength for psychotropic drugs: a Bayesian meta-analysis

Although most drugs were supported by strong evidence at the time of approval, some only had moderate or ambiguous evidence, and results show the need for more systematic quantification and classification of statistical evidence for psychotropic drugs.



Beyond overall effects: A Bayesian approach to finding constraints in meta-analysis.

This work presents an alternative goal for meta-analysis, and proposes 4 models: all studies are truly null; all studies share a single true nonzero effect; studies differ, but all true effects are in the same direction; and some study effects are truly positive, whereas others are truly negative.

A re-evaluation of random-effects meta-analysis

It is suggested that random-effects meta-analyses as currently conducted often fail to provide the key results, and the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods is investigated.

Fixed- and random-effects models in meta-analysis.

There are 2 families of statistical procedures in meta-analysis: fixed- and randomeffects procedures. They were developed for somewhat different inference goals: making inferences about the effect

Bayesian approaches to random-effects meta-analysis: a comparative study.

It is described how a full Bayesian analysis can deal with unresolved issues, such as the choice between fixed- and random-effects models, the choice of population distribution in a random- effects analysis, the treatment of small studies and extreme results, and incorporation of study-specific covariates.

Robust Bayesian Meta-Analysis: Addressing Publication Bias with Model-Averaging

It is demonstrated that RoBMA finds evidence for the absence of publication bias in Registered Replication Reports and reliably avoids false positives, and is relatively robust to model misspecification and simulations show that it outperforms existing methods.

Bayesian Mixture Modeling of Significant p Values: A Meta-Analytic Method to Estimate the Degree of Contamination From

Inspired by statistical research on false-discovery rates, a Bayesian mixture model analysis of the p-curve is proposed that assumes that significant p values arise either from the null-hypothesis or from the alternative hypothesis when their distribution is accounted for by a simple parametric model.

Meta-analysis using effect size distributions of only statistically significant studies.

Publication bias threatens the validity of meta-analytic results and leads to overestimation of the effect size in traditional meta-analysis. This particularly applies to meta-analyses that feature

Adjusting for Publication Bias in JASP — Selection Models and Robust Bayesian Meta-Analysis

This tutorial demonstrates how to conduct a publication bias adjusted meta-analysis in JASP and introduces Robust Bayesian Meta-Analysis (RoBMA), a Bayesian extension of the frequentist selection models.

A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: the case of felt power

ABSTRACTEarlier work found that – compared to participants who adopted constrictive body postures – participants who adopted expansive body postures reported feeling more powerful, showed an increase

A Conceptual Introduction to Bayesian Model Averaging

In this conceptual introduction, the principles of BMA are explained, its advantages over all-or-none model selection are described, and its utility is showcased in three examples: analysis of covariance, meta-analysis, and network analysis.