Abstract

Covariate-adaptive randomization (CAR) is widely adopted in clini

cal trials to balance baseline covariates across treatment arms. When outcomes

are binary or other types of discrete data, generalized linear models (GLMs) are

typically used as working models to improve the efficiency of treatment effect

estimation. However, in high-dimensional settings, limited sample sizes may lead

to unstable estimation and invalid inference.

Moreover, when the underlying

data-generating mechanism is misspecified relative to the GLM working model,

efficiency gains over the unadjusted estimator are no longer guaranteed.

To

address these challenges, we first propose a high-dimensional GLM adjustment

approach using ℓ1 regularization, resulting in a GLM-adjusted treatment effect

estimator under CAR. When the target trial has a limited sample size, we further

develop a transfer learning estimator that incorporates information from a large

external trial regarded as source data. As long as the source and target populations are sufficiently similar, the transfer learning estimator requires weaker

sparsity conditions and a smaller target sample size than the GLM-adjusted

estimator to achieve asymptotic normality.

To guarantee efficiency improvement relative to the unadjusted estimator, we additionally introduce a projection

method. With this step, both the GLM-adjusted and transfer learning estimators

achieve efficiency gains. Simulation studies and a semi-synthetic data application

demonstrate the effectiveness of our transfer learning strategy and the projection

method.

Key words and phrases: covariate-adaptive randomization, generalized linear models, projection method, robustness, transfer learning

Information

Preprint No.SS-2025-0480
Manuscript IDSS-2025-0480
Complete AuthorsYujia Gu, Hanzhong Liu, Wei Ma
Corresponding AuthorsWei Ma
Emailsmawei@ruc.edu.cn

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Acknowledgments

We thank the Associate Editor and the reviewers for their valuable comments and suggestions. Wei Ma was supported by the National Natural

Science Foundation of China (Grant No. 12571277). Hanzhong Liu was

supported by the National Natural Science Foundation of China (grant

no. 12531012) and Shenzhen Science and Technology Program (grant No.

Supplementary Materials

The online supplementary material contains details on the variance estimators, the generation of the semi-synthetic clinical trial data, and proofs.

Supplementary materials are available for download.