Bayesian evaluation of informative hypotheses in cluster-randomized trials.

ABSTRACT: Researchers often have informative hypotheses in mind when comparing means across treatment groups, such as H₁ : μ_A < μ_B < μ_C and H₂ : μ_B < μ_A < μ_C, and want to compare these hypotheses to each other directly. This can be done by means of Bayesian inference. This article discusses the disadvantages of the frequentist approach to null hypothesis testing and the advantages of the Bayesian approach. It demonstrates how to use the Bayesian approach to hypothesis testing in the setting of cluster-randomized trials. The data from a school-based smoking prevention intervention with four treatment groups are used to illustrate the Bayesian approach. The main advantage of the Bayesian approach is that it provide

SUBMITTER: Moerbeek M 

PROVIDER: S-EPMC6420439 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature