Transfer Learning for Covariate-Adaptive Randomized Trials with High-Dimensional Generalized Linear Models

Covariate-adaptive randomization (CAR) is widely adopted in clinical 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 improve-Statistica Sinica: Newly accepted Paper ment 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.