Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Bayesian hypothesis testing and estimation under the marginalized random-effects meta-analysis model

View through CrossRef
AbstractMeta-analysis methods are used to synthesize results of multiple studies on the same topic. The most frequently used statistical model in meta-analysis is the random-effects model containing parameters for the overall effect, between-study variance in primary study’s true effect size, and random effects for the study-specific effects. We propose Bayesian hypothesis testing and estimation methods using the marginalized random-effects meta-analysis (MAREMA) model where the study-specific true effects are regarded as nuisance parameters which are integrated out of the model. We propose using a flat prior distribution on the overall effect size in case of estimation and a proper unit information prior for the overall effect size in case of hypothesis testing. For the between-study variance (which can attain negative values under the MAREMA model), a proper uniform prior is placed on the proportion of total variance that can be attributed to between-study variability. Bayes factors are used for hypothesis testing that allow testing point and one-sided hypotheses. The proposed methodology has several attractive properties. First, the proposed MAREMA model encompasses models with a zero, negative, and positive between-study variance, which enables testing a zero between-study variance as it is not a boundary problem. Second, the methodology is suitable for default Bayesian meta-analyses as it requires no prior information about the unknown parameters. Third, the proposed Bayes factors can even be used in the extreme case when only two studies are available because Bayes factors are not based on large sample theory. We illustrate the developed methods by applying it to two meta-analyses and introduce easy-to-use software in the R package to compute the proposed Bayes factors.
Title: Bayesian hypothesis testing and estimation under the marginalized random-effects meta-analysis model
Description:
AbstractMeta-analysis methods are used to synthesize results of multiple studies on the same topic.
The most frequently used statistical model in meta-analysis is the random-effects model containing parameters for the overall effect, between-study variance in primary study’s true effect size, and random effects for the study-specific effects.
We propose Bayesian hypothesis testing and estimation methods using the marginalized random-effects meta-analysis (MAREMA) model where the study-specific true effects are regarded as nuisance parameters which are integrated out of the model.
We propose using a flat prior distribution on the overall effect size in case of estimation and a proper unit information prior for the overall effect size in case of hypothesis testing.
For the between-study variance (which can attain negative values under the MAREMA model), a proper uniform prior is placed on the proportion of total variance that can be attributed to between-study variability.
Bayes factors are used for hypothesis testing that allow testing point and one-sided hypotheses.
The proposed methodology has several attractive properties.
First, the proposed MAREMA model encompasses models with a zero, negative, and positive between-study variance, which enables testing a zero between-study variance as it is not a boundary problem.
Second, the methodology is suitable for default Bayesian meta-analyses as it requires no prior information about the unknown parameters.
Third, the proposed Bayes factors can even be used in the extreme case when only two studies are available because Bayes factors are not based on large sample theory.
We illustrate the developed methods by applying it to two meta-analyses and introduce easy-to-use software in the R package to compute the proposed Bayes factors.

Related Results

Evaluation of decay times in coupled spaces: Bayesian decay model selection
Evaluation of decay times in coupled spaces: Bayesian decay model selection
This paper applies Bayesian probability theory to determination of the decay times in coupled spaces. A previous paper [N. Xiang and P. M. Goggans, J. Acoust. Soc. Am. 110, 1415–14...
Bayesian Estimation of Stimulus Responses in Poisson Spike Trains
Bayesian Estimation of Stimulus Responses in Poisson Spike Trains
A Bayesian method is developed for estimating neural responses to stimuli, using likelihood functions incorporating the assumption that spike trains follow either pure Poisson stat...
Evaluation of decay times in coupled spaces: Reliability analysis of Bayeisan decay time estimation
Evaluation of decay times in coupled spaces: Reliability analysis of Bayeisan decay time estimation
This paper discusses quantitative tools to evaluate the reliability of “decay time estimates” and inter-relationships between multiple decay times for estimates made within a Bayes...
Bayesian decay time analysis in coupled spaces using a proper decay model
Bayesian decay time analysis in coupled spaces using a proper decay model
Acoustically coupled spaces have recently been drawing more and more attention in the architectural acoustics community. Determination of decay times in these coupled spaces from m...
Bayesian Spiking Neurons II: Learning
Bayesian Spiking Neurons II: Learning
In the companion letter in this issue (“Bayesian Spiking Neurons I: Inference”), we showed that the dynamics of spiking neurons can be interpreted as a form of Bayesian integration...
After the paint has dried: a review of testing techniques for studying the mechanical properties of artists’ paint
After the paint has dried: a review of testing techniques for studying the mechanical properties of artists’ paint
AbstractWhile the chemistry of artists’ paints has previously been studied and reviewed, these studies only capture a portion of the properties affecting the response of paint mate...
Effects of Visual-Spatial Added Cues on Fourth-Graders' Melodic Discrimination
Effects of Visual-Spatial Added Cues on Fourth-Graders' Melodic Discrimination
A videotaped music-listening test was developed in which 30 brief melodies were paired with visual-spatial representations through vertical and horizontal hand motions across a cha...
Does Recalling Moral Behavior Change the Perception of Brightness?
Does Recalling Moral Behavior Change the Perception of Brightness?
Banerjee, Chatterjee, and Sinha (2012) recently reported that recalling unethical behavior led participants to see the room as darker and to desire more light-emitting products (e....

Back to Top