  Phase II basket trials are popular tools to evaluate efficacy of a new
treatment targeting genetic alteration common to a set of different cancer
histologies. Efficient designs are obtained by pooling data from the different
arms (e.g., cancer histologies) via Bayesian hierarchical modelling, with a
variance parameter controlling the strength of shrinkage of each arm treatment
effect to the overall treatment effect. One critical aspect of this approach is
that prior choice on the variance plays a major role in determining the
strength of shrinkage and impacts the operating characteristics of the design.
We review the priors most commonly adopted in previous works and compare them
with the recently introduced penalized complexity (PC) priors. Our simulation
study shows comparable behaviour for the PC prior and the gold standard choice
half-t prior, with the former performing better in the homogeneous scenario
where all histologies respond similarly to the treatment. We argue that PC
priors offer advantages over other priors because they allow the user to handle
the degree of shrinkage by means of only one parameter and can be elicited
based on clinical opinion when available.
