  The BayesPPDSurv (Bayesian Power Prior Design for Survival Data) R package
supports Bayesian power and type I error calculations and model fitting using
the power and normalized power priors incorporating historical data with for
the analysis of time-to-event outcomes. The package implements the stratified
proportional hazards regression model with piecewise constant hazard within
each stratum. The package allows the historical data to inform the treatment
effect parameter, parameter effects for other covariates in the regression
model, as well as the baseline hazard parameters. The use of multiple
historical datasets is supported. A novel algorithm is developed for
computationally efficient use of the normalized power prior. In addition, the
package supports the use of arbitrary sampling priors for computing Bayesian
power and type I error rates, and has built-in features that semi-automatically
generate sampling priors from the historical data. We demonstrate the use of
BayesPPDSurv in a comprehensive case study for a melanoma clinical trial
design.
