  There is currently a focus on statistical methods which can use historical
trial information to help accelerate the discovery, development and delivery of
medicine. Bayesian methods can be constructed so that the borrowing is
"dynamic" in the sense that the similarity of the data helps to determine how
much information is used. In the time to event setting with one historical data
set, a popular model for a range of baseline hazards is the piecewise
exponential model where the time points are fixed and a borrowing structure is
imposed on the model. Although convenient for implementation this approach
effects the borrowing capability of the model. We propose a Bayesian model
which allows the time points to vary and a dependency to be placed between the
baseline hazards. This serves to smooth the posterior baseline hazard improving
both model estimation and borrowing characteristics. We explore a variety of
prior structures for the borrowing within our proposed model and assess their
performance against established approaches. We demonstrate that this leads to
improved type I error in the presence of prior data conflict and increased
power. We have developed accompanying software which is freely available and
enables easy implementation of the approach.
