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InfluenceBorrowing  

Adaptive Influence-Based Borrowing for Hybrid Control Trials
View on CRAN: Click here


Download and install InfluenceBorrowing package within the R console
Install from CRAN:
install.packages("InfluenceBorrowing")

Install from Github:
library("remotes")
install_github("cran/InfluenceBorrowing")

Install by package version:
library("remotes")
install_version("InfluenceBorrowing", "0.1.0")



Attach the package and use:
library("InfluenceBorrowing")
Maintained by
Jile Chaoge
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-23
Latest Update: 2026-04-23
Description:
Implements the adaptive influence-based borrowing framework proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper “Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls" <doi:10.48550/arXiv.2604.13973> for augmenting Randomized Controlled Trials (RCTs) with External Control (EC) data. This package provides a comprehensive workflow to: (1) quantify the comparability of external control samples using influence scores approximated via the influence function of the M-estimator; (2) construct candidate borrowing subsets and select the optimal subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic differences in external outcomes using R-learner methods implemented via Ordinary Least Squares or Kernel Ridge Regression.
How to cite:
Jile Chaoge (2026). InfluenceBorrowing: Adaptive Influence-Based Borrowing for Hybrid Control Trials. R package version 0.1.0, https://cran.r-project.org/web/packages/InfluenceBorrowing. Accessed 28 Oct. 2026.
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Functions, R codes and Examples using the InfluenceBorrowing R package
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