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")
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/).
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:
- New, more flexible user interface
- New MCMC software (STAN)
- Expanded functionality (e.g., more outcomes, more flexibility in priors, more flexibility in data generation, etc.)
The name psborrow
combines propensity scoring (ps
) and Bayesian dynamic
borrow
ing. 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.