psborrow2 is an R package that for conducting Bayesian dynamic borrowing
analyses and simulation studies (Lewis et al 2019, Viele et al 2014)
psborrow2 helps the user:
Apply Bayesian dynamic borrowing methods. psborrow2 has a user-friendly interface for
conducting Bayesian dynamic borrowing analyses using the hierarchical commensurate prior approach
that handles the computationally-difficult MCMC sampling
on behalf of the user.
Conduct simulation studies of Bayesian dynamic borrowing methods. psborrow2 includes a
framework to compare different trial and borrowing characteristics in a unified way
in simulation studies to inform trial design.
Generate data for simulation studies. psborrow2 includes a set of functions to generate
data for simulation studies.
You can install the latest version of psborrow2 on CRAN with:
install.packages('psborrow2')
or you can install the development version with:
# remotes::install_github("Genentech/psborrow2")
install.packages("psborrow2", repos = c('https://genentech.r-universe.dev', 'https://cloud.r-project.org'))
Please note that cmdstanr is highly recommended, but will not be installed by default when installing psborrow2.
To install cmdstanr, follow the instructions outlined by the cmdstanr documentation or use:
install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev", getOption("repos")))
To learn how to use the psborrow2 R package, refer to the package website (https://genentech.github.io/psborrow2/).
psborrow vs. psborrow2
psborrow2 is the successor to
psborrow. psborrow
is still freely available on CRAN with the
same validated functionality; however, the package is not actively developed.
Major updates in psborrow2 include:
The name psborrow combines propensity scoring (ps) and Bayesian dynamic
borrowing. As one might expect, both psborrow and psborrow2 can be used to combine dynamic
borrowing and propensity-score adjustment/weighting methods.
Lewis CJ, Sarkar S, Zhu J, Carlin BP. Borrowing from historical control data in cancer drug development: a cautionary tale and practical guidelines. Statistics in biopharmaceutical research. 2019 Jan 2;11(1):67-78.
Viele K, Berry S, Neuenschwander B, Amzal B, Chen F, Enas N, Hobbs B, Ibrahim JG, Kinnersley N, Lindborg S, Micallef S. Use of historical control data for assessing treatment effects in clinical trials. Pharmaceutical statistics. 2014 Jan;13(1):41-54.