These techniques allow for the (1) quantification of evidence, both for and against the absence of an effect (2) monitoring of evidence as new studies accumulate over time (3) graceful and principled “model-averaged” combination between fixed-effect and random-effects meta-analysis and (4) principled planning of a new study in order maximize the probability that it will lead to a worthwhile gain in knowledge. These techniques include (1) an application of bridge sampling to obtain Bayes factors for random-effects meta-analysis (2) the computation of Bayes factors for fixed-effect versus random-effects meta-analysis (3) proposal of an informed prior on study heterogeneity based on a comprehensive literature search (4) model-averaged evidence across fixed-effect and random-effects meta-analyses, thereby accounting for model-uncertainty and (5) a proposal for a running power analysis in the field of meta-analysis.
Bayesian in comprehensive meta analysis series#
Gronau, and Felix Schönbrodt developed a suite of meta-analytic techniques for Bayesian evidence synthesis, addressing a series of challenges that currently constrain classical meta-analytic procedures.
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Total of 15 references identified with 5 external and 10 internal. This comprehensive network meta-analysis (NMA) examined a large number of biologics for the treatment of moderate.
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Eric-Jan Wagenmakers, Raoul Grasman, Quentin F. A comprehensive systematic literature search was carried out to identify suitable studies. We have proposed a fully integrated Bayesian model for meta-analysis of gene set enrichment using multiple genomic studies, and developed an efficient Gibbs.