  The R package BayesPPD (Bayesian Power Prior Design) supports Bayesian power
and type I error calculation and model fitting after incorporating historical
data with the power prior and the normalized power prior for generalized linear
models (GLM). The package accommodates summary level data or subject level data
with covariate information. It supports use of multiple historical datasets as
well as design without historical data. Supported distributions for responses
include normal, binary (Bernoulli/binomial), Poisson and exponential. The power
parameter $a_0$ can be fixed or modeled as random using a normalized power
prior for each of these distributions. In addition, the package supports the
use of arbitrary sampling priors for computing Bayesian power and type I error
rates, and has specific features for GLMs that semi-automatically generate
sampling priors from historical data. Since sample size determination (SSD) for
GLMs is computationally intensive, an approximation method based on asymptotic
theory has been implemented to support applications using the power prior. In
addition to describing the statistical methodology and functions implemented in
the package to enable SSD, we also demonstrate the use of BayesPPD in two
comprehensive case studies.
