English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce the BREASE framework for the Bayesian analysis of randomizedcontrolled trials with a binary treatment and a binary outcome. Approaching theproblem from a causal inference perspective, we propose parameterizing thelikelihood in terms of the baseline risk, efficacy, and adverse side effects ofthe treatment, along with a flexible, yet intuitive and tractable jointlyindependent beta prior distribution on these parameters, which we show to be ageneralization of the Dirichlet prior for the joint distribution of potentialoutcomes. Our approach has a number of desirable characteristics when comparedto current mainstream alternatives: (i) it naturally induces prior dependencebetween expected outcomes in the treatment and control groups; (ii) as thebaseline risk, efficacy and risk of adverse side effects are quantitiescommonly present in the clinicians' vocabulary, the hyperparameters of theprior are directly interpretable, thus facilitating the elicitation of priorknowledge and sensitivity analysis; and (iii) we provide analytical formulaefor the marginal likelihood, Bayes factor, and other posterior quantities, aswell as exact posterior sampling via simulation, in cases where traditionalMCMC fails. Empirical examples demonstrate the utility of our methods forestimation, hypothesis testing, and sensitivity analysis of treatment effects.

Causally Sound Priors for Binary Experiments