  There is currently a focus on statistical methods which can use external
trial information to help accelerate the discovery, development and delivery of
medicine. Bayesian methods facilitate borrowing which is "dynamic" in the sense
that the similarity of the data helps to determine how much information is
used. We propose a Bayesian semiparameteric model, which allows the baseline
hazard to take any form through an ensemble average. We introduce priors to
smooth the posterior baseline hazard improving both model estimation and
borrowing characteristics. A "lump-and-smear" borrowing prior accounts for
non-exchangable historical data and helps reduce the maximum type I error in
the presence of prior-data conflict. In this article, we present
BayesFBHborrow, an R package, which enables the user to perform Bayesian
borrowing with a historical control dataset in a semiparameteric time-to-event
model. User-defined hyperparameters smooth an ensemble averaged posterior
baseline hazard. The model offers the specification of lump-and-smear priors on
the commensurability parameter where the associated hyperparameters can be
chosen according to the users tolerance for difference between the log baseline
hazards. We demonstrate the performance of our Bayesian flexible baseline
hazard model on a simulated and real world dataset.
