  Basket designs are prospective clinical trials that are devised with the
hypothesis that the presence of selected molecular features determine a
patient's subsequent response to a particular "targeted" treatment strategy.
Basket trials are designed to enroll multiple clinical subpopulations to which
it is assumed that the therapy in question offers beneficial efficacy in the
presence of the targeted molecular profile. The treatment, however, may not
offer acceptable efficacy to all subpopulations enrolled. Moreover, for rare
disease settings, such as oncology wherein these trials have become popular,
marginal measures of statistical evidence are difficult to interpret for
sparsely enrolled subpopulations. Consequently, basket trials pose challenges
to the traditional paradigm for trial design, which assumes inter-patient
exchangeability. The R-package \pkg{basket} facilitates the analysis of basket
trials by implementing multi-source exchangeability models. By evaluating all
possible pairwise exchangeability relationships, this hierarchical modeling
framework facilitates Bayesian posterior shrinkage among a collection of
discrete and pre-specified subpopulations. Analysis functions are provided to
implement posterior inference of the response rates and all possible
exchangeability relationships between subpopulations. In addition, the package
can identify "poolable" subsets of and report their response characteristics.
The functionality of the package is demonstrated using data from an oncology
study with subpopulations defined by tumor histology.
