psborrow2: Bayesian Dynamic Borrowing Analysis and Simulation

Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <>.

Depends: R (≥ 4.1.0)
Imports: checkmate, glue, methods, graphics, posterior, generics, Matrix, mvtnorm, future, simsurv
Suggests: cmdstanr, survival, flexsurv, testthat (≥ 3.0), usethis (≥ 2.1.5), vdiffr, tibble, xml2, knitr, rmarkdown, bayesplot, matrixcalc, WeightIt, MatchIt, BayesPPD, ggsurvfit, gbm, ggplot2, cobalt, table1, gt, gtsummary
Published: 2024-04-30
DOI: 10.32614/CRAN.package.psborrow2
Author: Matt Secrest ORCID iD [aut, cre], Isaac Gravestock [aut], Craig Gower-Page [ctb], Manoj Khanal [ctb], Mingyang Shan [ctb], Kexin Jin [ctb], Zhi Yang [ctb], Genentech, Inc. [cph, fnd]
Maintainer: Matt Secrest <secrestm at>
License: Apache License 2.0
NeedsCompilation: no
SystemRequirements: cmdstan
Language: en-US
Materials: NEWS
CRAN checks: psborrow2 results


Reference manual: psborrow2.pdf
Vignettes: 7. Data Simulation
2. Conduct a hybrid control analysis on a dataset using BDB
3. Specifying prior distributions
5. Incorporating propensity scores analysis in psborrow2
1. Getting started with psborrow2
4. Conduct a simulation study
6. Comparison of Fixed Weights


Package source: psborrow2_0.0.3.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): psborrow2_0.0.3.4.tgz, r-oldrel (arm64): psborrow2_0.0.3.4.tgz, r-release (x86_64): psborrow2_0.0.3.4.tgz, r-oldrel (x86_64): psborrow2_0.0.3.4.tgz
Old sources: psborrow2 archive


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