Bayesian analysis and design for comparison of effect-sizes. (English) Zbl 0989.62015
Summary: Comparison of effect-sizes, or more generally, of non-centrality parameters of non-central \(t\) distributions, is a common problem, especially in meta-analysis. The usual simplifying assumptions of either identical or non-related effect-sizes are often too restrictive to be appropriate. In this paper, the effect-sizes are modeled as random effects with \(t\) distributions. Bayesian hierarchical models are used both to design and analyze experiments. The main goal is to compare effect-sizes. Sample sizes are chosen so as to make accurate inferences about the difference of effect-sizes and also to convincingly solve the testing of equality of effect-sizes if such is the goal.
MSC:
| 62F15 | Bayesian inference |
Keywords:
hierarchical models; optimal sample sizes; predictive distributions; non-centrality parameters; meta-analysisReferences:
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