6533b86dfe1ef96bd12ca7ed

RESEARCH PRODUCT

Bayesian analysis and design for comparison of effect-sizes

Maria J. BayarriA.m. Mayoral

subject

Statistics and ProbabilityApplied MathematicsBayesian probabilityPosterior probabilityBayes factorRandom effects modelBlock designSample size determinationPrior probabilityStatisticsStatistics Probability and UncertaintyAlgorithmStatistical hypothesis testingMathematics

description

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.

yearjournalcountryeditionlanguage
2002-04-01Journal of Statistical Planning and Inference
https://doi.org/10.1016/s0378-3758(01)00223-3