psborrow2

Overview

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:

1. 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.

2. 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.

3. Generate data for simulation studies. psborrow2 includes a set of functions to generate data for simulation studies.

Installation

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")))

Tutorial

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:

• 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 borrowing. As one might expect, both psborrow and psborrow2 can be used to combine dynamic borrowing and propensity-score adjustment/weighting methods.

Bibliography

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.