title: Frequentist performance of Bayesian confidence intervals for comparing proportions in 2 x 2 contingency tables.
abstract: This article investigates the performance, in a frequentist sense, of Bayesian confidence intervals (CIs) for the difference of proportions, relative risk, and odds ratio in 2 x 2 contingency tables. We consider beta priors, logit-normal priors, and related correlated priors for the two binomial parameters. The goal was to analyze whether certain settings for prior parameters tend to provide good coverage performance regardless of the true association parameter values. For the relative risk and odds ratio, we recommend tail intervals over highest posterior density (HPD) intervals, for invariance reasons. To protect against potentially very poor coverage probabilities when the effect is large, it is best to use a diffuse prior, and we recommend the Jeffreys prior. Otherwise, with relatively small samples, Bayesian CIs using more informative (even uniform) priors tend to have poorer performance than the frequentist CIs based on inverting score tests, which perform uniformly quite well for these parameters.
authors: Department of Statistics, University of Florida, Gainesville, Florida 32611, USA. aa@stat.ufl.edu
authors: Alan
authors: A
authors: Agresti
authors: Yongyi
authors: Y
authors: Min
mesh_terms: D001499:Bayes Theorem; D016010:Binomial Distribution; D001699:Biometry / Q000379:methods*; D019295:Computational Biology / Q000379:methods; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D015233:Models, Statistical; D016017:Odds Ratio; D011336:Probability; D012306:Risk