Outcome-blinded sample size re-estimation for externally controlled single-arm trials using baseline covariates
Abstract
Externally controlled single-arm trials provide an option when limited patient populations or ethical constraints make concurrent randomized controls impractical, including in rare diseases and investigator-initiated trials with limited recruitment. Standardization improves comparability by aligning external controls with the enrolled population, but does not by itself preserve planned power. Differences between anticipated and enrolled covariate distributions can change the precision of the standardized control estimate and leave a fixed-size trial underpowered. We propose an outcome-blinded, covariate-adaptive sample size re-estimation procedure that translates these changes in precision into updated recruitment targets. Historical-control data and accumulating active-arm baseline covariates are used to update external-control standardization and its estimated precision. Adaptation requires no active-arm outcomes and retains the prespecified clinically meaningful effect to be detected. We state sufficient conditions for type I error calibration and power after adaptive stopping, assess operating characteristics through simulations, and illustrate implementation using Alzheimer's Disease Cooperative Study data. In the primary simulations, adaptive recruitment restored power lost under standardized fixed designs, achieving at least the target power across the distribution-shift settings and outcome models examined. Type I error rates were close to nominal, although mild inflation remained with a smaller historical-control sample. The application illustrated how accumulating baseline information guided recruitment revisions. By adapting sample size to the precision of the population-standardized control estimate, the proposed procedure addresses a source of power loss that standardization alone does not resolve.
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BibTeX
@misc{hanada2026outcomeblinded,
title = {{Outcome-blinded sample size re-estimation for externally controlled single-arm trials using baseline covariates}},
author = {Keisuke Hanada and Masahiro Kojima},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.25159},
eprint = {2607.25159},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.25159},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Outcome-blinded sample size re-estimation for externally controlled single-arm trials using baseline covariates
AU - Keisuke Hanada
AU - Masahiro Kojima
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2607.25159
UR - https://arxiv.org/abs/2607.25159
AB - Externally controlled single-arm trials provide an option when limited patient populations or ethical constraints make concurrent randomized controls impractical, including in rare diseases and investigator-initiated trials with limited recruitment. Standardization improves comparability by aligning external controls with the enrolled population, but does not by itself preserve planned power. Differences between anticipated and enrolled covariate distributions can change the precision of the standardized control estimate and leave a fixed-size trial underpowered. We propose an outcome-blinded, covariate-adaptive sample size re-estimation procedure that translates these changes in precision into updated recruitment targets. Historical-control data and accumulating active-arm baseline covariates are used to update external-control standardization and its estimated precision. Adaptation requires no active-arm outcomes and retains the prespecified clinically meaningful effect to be detected. We state sufficient conditions for type I error calibration and power after adaptive stopping, assess operating characteristics through simulations, and illustrate implementation using Alzheimer's Disease Cooperative Study data. In the primary simulations, adaptive recruitment restored power lost under standardized fixed designs, achieving at least the target power across the distribution-shift settings and outcome models examined. Type I error rates were close to nominal, although mild inflation remained with a smaller historical-control sample. The application illustrated how accumulating baseline information guided recruitment revisions. By adapting sample size to the precision of the population-standardized control estimate, the proposed procedure addresses a source of power loss that standardization alone does not resolve.
ER -