  Clinical trials often seek to determine the superiority, equivalence, or
non-inferiority of an experimental condition (e.g., a new drug) compared to a
control condition (e.g., a placebo or an already existing drug). The use of
frequentist statistical methods to analyze data for these types of designs is
ubiquitous even though they have several limitations. Bayesian inference
remedies many of these shortcomings and allows for intuitive interpretations.
In this article, we outline the frequentist conceptualization of superiority,
equivalence, and non-inferiority designs and discuss its disadvantages.
Subsequently, we explain how Bayes factors can be used to compare the relative
plausibility of competing hypotheses. We present baymedr, an R package and web
application, that provides user-friendly tools for the computation of Bayes
factors for superiority, equivalence, and non-inferiority designs. Instructions
on how to use baymedr are provided and an example illustrates how already
existing results can be reanalyzed with baymedr.
