Covariate Selection for Optimizing Balance with an Innovative Adaptive Randomization Approach

TL;DR

This paper introduces a new adaptive randomization method that dynamically selects and balances influential covariates in clinical trials, improving treatment effect estimation efficiency especially with many covariates.

Contribution

It proposes a novel covariate selection approach that integrates patient responses, ensuring better balance and theoretical consistency in adaptive randomization.

Findings

Faster convergence of covariate imbalance measure.

Higher efficiency in treatment effect estimation.

Validated through extensive simulations and empirical studies.

Abstract

Balancing influential covariates is crucial for valid treatment comparisons in clinical studies. While covariate-adaptive randomization is commonly used to achieve balance, its performance can be inadequate when the number of baseline covariates is large. It is therefore essential to identify the influential factors associated with the outcome and ensure balance among these critical covariates. In this article, we propose a novel adaptive randomization approach that integrates the patients' responses and covariates information to select sequentially significant covariates and maintain their balance. We establish theoretically the consistency of our covariate selection method and demonstrate that the improved covariate balancing, as evidenced by a faster convergence rate of the imbalance measure, leads to higher efficiency in estimating treatment effects. Furthermore, we provide extensive numerical and empirical studies to illustrate the benefits of our proposed method across various settings.