CARB: A Covariate-Assessed Robust Borrowing Strategy with Literature-Informed Prior Weights for External Data
Abstract
Borrowing external control data can improve the efficiency of clinical trials, particularly when patient accrual is difficult. A persistent challenge is how to prespecify the degree of borrowing systematically and transparently. In practice, prior weights are often selected heuristically or calibrated through simulation to achieve desired operating characteristics, making them difficult to justify scientifically. Moreover, patient-level covariate data from external sources are rarely available when the new trial is designed, precluding patient-level adjustment methods. We propose CARB (Covariate-Assessed Robust Borrowing), a framework that formalizes prior-weight specification as a design-stage assessment of baseline compatibility. Using only aggregate information, CARB quantifies discrepancies in prespecified baseline covariates between the new trial and each external source, without using outcome data from the new trial. A prespecified mapping translates the resulting dissimilarity measure into a source-specific prior weight on the exchangeable component of a robust borrowing model. Simulation studies show that CARB reduces bias and type I error inflation relative to fixed borrowing under observed and unobserved incompatibility, while improving efficiency when external controls are compatible. An application to advanced melanoma trials illustrates covariate-informed borrowing from multiple historical sources. An apparent discrepancy in reported baseline characteristics serves as a warning signal that reduces borrowing. CARB provides a transparent and reproducible way to borrow cautiously when patient-level covariate data from external sources are unavailable.
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BibTeX
@misc{liang2026carb,
title = {{CARB: A Covariate-Assessed Robust Borrowing Strategy with Literature-Informed Prior Weights for External Data}},
author = {Jinping Liang and Guannan Gong and Satrajit Roychoudhury and Wei Wei},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28810},
eprint = {2608.28810},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28810},
}
RIS
TY - UNPB
TI - CARB: A Covariate-Assessed Robust Borrowing Strategy with Literature-Informed Prior Weights for External Data
AU - Jinping Liang
AU - Guannan Gong
AU - Satrajit Roychoudhury
AU - Wei Wei
PY - 2026
DA - 2026/08/28
PB - arXiv
DO - 10.48550/arxiv.2608.28810
UR - https://arxiv.org/abs/2608.28810
AB - Borrowing external control data can improve the efficiency of clinical trials, particularly when patient accrual is difficult. A persistent challenge is how to prespecify the degree of borrowing systematically and transparently. In practice, prior weights are often selected heuristically or calibrated through simulation to achieve desired operating characteristics, making them difficult to justify scientifically. Moreover, patient-level covariate data from external sources are rarely available when the new trial is designed, precluding patient-level adjustment methods. We propose CARB (Covariate-Assessed Robust Borrowing), a framework that formalizes prior-weight specification as a design-stage assessment of baseline compatibility. Using only aggregate information, CARB quantifies discrepancies in prespecified baseline covariates between the new trial and each external source, without using outcome data from the new trial. A prespecified mapping translates the resulting dissimilarity measure into a source-specific prior weight on the exchangeable component of a robust borrowing model. Simulation studies show that CARB reduces bias and type I error inflation relative to fixed borrowing under observed and unobserved incompatibility, while improving efficiency when external controls are compatible. An application to advanced melanoma trials illustrates covariate-informed borrowing from multiple historical sources. An apparent discrepancy in reported baseline characteristics serves as a warning signal that reduces borrowing. CARB provides a transparent and reproducible way to borrow cautiously when patient-level covariate data from external sources are unavailable.
ER -