noirritchandra / CAPPMx

A fast C++ based implementation of the CA-PPMx by Chandra et al. (2023+).

Common Atoms Product Partition Model on Covariates for Generating Synthetic Controls in Single Arm Clinical Trials

Noirrit Kiran Chandra 2023-06-12

An implementation of the CA-PPMx model proposed by (Chandra et al. 2023; Müller, Chandra, and Sarkar 2023). Please cite our works.

Installation

The package has been tried and tested in Ubuntu and macOS.

Required UNIX Packages

• libgsl
• openmp
• R (>= 4.2.2)

Installation from Github

devtools::install_github("noirritchandra/CAPPMx", build_vignettes = TRUE)

Must set build_vignettes = TRUE to install the vignette files containing illustrations.

library(CAPPMx)

Check the vignette files of the package for the reference manual:

vignette("Intro_to_CAPPMx","CAPPMx")

Glioblastoma Data

A resampled version of the Glioblastoma dataset used by Chandra et al. (2023) is included in the CAPPMx package which can be accessed using the following:

data("MDACC_reproduced")
head(MDACC_reproduced)

##   Surgery Reason Histologic Grade EOR Gender ATRX MGMT CT SOC RT Dose KPS Age
## 1              1                1   1      1   NA    0  0   1      NA   0   0
## 2              1                1   0      0    1    0  0   1       1   0   1
## 3              1                1   0      1   NA    0  0   1       0   0   1
## 4              1                1   1      1    1    0  1   1       1   0   0
## 5              1                1   1      1    1    0  1   1       1   0   0
## 6              1                1   0      1   NA    0  1   1       1   0   1
##     endpts surv_inds
## 1 30.71429     FALSE
## 2 78.00000     FALSE
## 3 10.56548      TRUE
## 4 64.70833     FALSE
## 5 44.85119     FALSE
## 6 47.42857     FALSE

References

Chandra, N. K., A. Sarkar, J de Groot, Y. Yuan, and P. and Müller. 2023. “Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials.” arXiv Preprint arXiv:2201.00068.

Müller, P., N. K. Chandra, and A. Sarkar. 2023. “Bayesian Approaches to Include Real-World Data in Clinical Studies.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381: 20220158.