InfluenceBorrowing: Adaptive Influence-Based Borrowing for Hybrid Control Trials

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.

Version:	0.1.0
Imports:	KRLS, stats
Published:	2026-04-23
DOI:	10.32614/CRAN.package.InfluenceBorrowing (may not be active yet)
Author:	Jile Chaoge [aut, cre], Peng Wu [aut], Shu Yang [aut]
Maintainer:	Jile Chaoge <chogjill at 126.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	InfluenceBorrowing results

Documentation:

Reference manual:

InfluenceBorrowing.html , InfluenceBorrowing.pdf

Downloads:

Package source:	InfluenceBorrowing_0.1.0.tar.gz
Windows binaries:	r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): InfluenceBorrowing_0.1.0.tgz, r-oldrel (arm64): InfluenceBorrowing_0.1.0.tgz, r-release (x86_64): InfluenceBorrowing_0.1.0.tgz, r-oldrel (x86_64): InfluenceBorrowing_0.1.0.tgz

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