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psborrow (version 0.2.3)

Bayesian Dynamic Borrowing with Propensity Score

Description

A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) that combines propensity score and Bayesian dynamic borrowing methods.

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Version

Install

install.packages('psborrow')

Monthly Downloads

669

Version

0.2.3

License

Apache License (>= 2)

Maintainer

Isaac Gravestock

Last Published

June 23rd, 2025

Functions in psborrow (0.2.3)

Simulate covariates

Specify prior distributions and predictors for MCMC methods

Specify parameters for enrollment time, drop-out pattern and analysis start time

Run MCMC for multiple scenarios with provided data with parallel processing

Simulate external trial indicator and treatment arm indicator

validate the operational parameters for MCMC method

Plot mean posterior hazard ratio between treatment and control

Simulate time-to-events for multiple scenarios

Generate summary statistics for the MCMC chains

Run MCMC for multiple scenarios with provided data

Set up time-to-events

c,.priorClass-method

Concatenate multiple .priorClasss class

Fit Dynamic Borrowing MCMC Model

Conditional Message

Fix Column Names

.clinClass-class

S4 Class for specifying parameters for enrollment time, drop-out pattern and analysis start time

plot_type1error

Plot type 1 error

Simulate survival times

.eventClass-class

S4 Class for setting parameters for time-to-events

Generate summary statistics of a simulation scenario

.priorClass-class

S4 Class for specifying prior distributions and predictors for MCMC methods

.covClass-class

S4 Class for setting up covariates

Simulate covariates with a multivariate normal distribution

Generating posterior samples from MCMC

c,.covClass-method

Concatenate multiple .covClasss classes

is_psborrow_dev

Check if user is in psborrow development environment

Plot mean squared error (MSE)

Set up covariates