# 1. Getting started with psborrow2

#### Matt Secrest and Isaac Gravestock

In this vignette, you’ll learn about the scope of `psborrow2` and where to find additional information on how to implement analyses in `psborrow2`.

## Introduction

While the randomized controlled trial (RCT) comparing experimental and control arms remains the gold standard for evaluating the efficacy of a novel therapy, one may want to leverage relevant existing external control data to inform the study outcome. External control data can help increase study power and thereby shorten trial duration or reduce the number of subjects needed. However, analysis of external control data can also introduce bias. One method for incorporating external control data to mitigate bias is Bayesian dynamic borrowing (BDB), in which external control data is borrowed to the extent that the external and RCT control arms have similar outcomes. See @viele2014use for a summary.

Implementing BDB is computationally involved and requires Markov chain Monte Carlo (MCMC) sampling methods, which in turn may require knowledge of MCMC sampling software. To overcome these technical barriers and we developed `psborrow2`, an R package which facilitates the use of the MCMC sampling program Stan (via CMD Stan).

`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.

`psborrow2` supports time-to-event, binary, and continuous endpoints.

### 1. Apply Bayesian dynamic borrowing methods

`psborrow2` can implement BDB in a scenario wherein a two-arm RCT is supplemented with external data on the control arm. Three arms are required to implement BDB in `psborrow2`. They are:

- The internal control arm from within the RCT
- The external control arm using observational data or experimental data from another trial with the same intervention/treatment and population as the internal control arm
- The internal experimental arm from within the RCT

Such scenarios are common in drug development because the comparator arm for a novel therapy is often the standard of care, for which data exists from electronic health care records or from previous phase III registrational trials.

Refer to the “dataset” article for more information on how to implement BDB analyses on your own data: (https://genentech.github.io/psborrow2/articles/dataset.html)[https://genentech.github.io/psborrow2/articles/dataset.html]

### 2. Conduct simulation studies of Bayesian dynamic borrowing methods

Refer to the “simulation study” article for more information on how to create a simulation study involving BDB and other innovative trial designs: https://genentech.github.io/psborrow2/articles/simulation_study.html

### 3. Generate data for simulation studies

Refer to the “data generation” article for more information on how to generate data for simulation studies: https://genentech.github.io/psborrow2/articles/data_simulation.html

## Additional articles

Please refer to https://genentech.github.io/psborrow2/articles/index.html for additional articles on `psborrow2` functionality.

## Installing `cmdstanr`

`cmdstanr` is highly recommended for use with `psborrow2`. To install `cmdstanr`, follow the instructions outlined by the `cmdstanr` documentation or use:

    install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

## References