Covariate-Adaptive Randomization in Clinical Trials without Inflated Variances

TL;DR

This paper introduces a new covariate adaptive randomization method for clinical trials that balances specified covariates without inflating the variance of unspecified covariates, ensuring valid treatment effect tests.

Contribution

A novel CAR procedure that balances covariates efficiently while preventing variance inflation of unbalanced covariates, addressing the shift problem.

Findings

Convergence rate of covariate imbalance is o(n^{1/2})

Asymptotic variance of unspecified covariates does not exceed simple randomization

The shift problem is eliminated under the new procedure

Abstract

Covariate adaptive randomization (CAR) procedures are extensively used to reduce the likelihood of covariate imbalances occurring in clinical trials. In literatures, a lot of CAR procedures have been proposed so that the specified covariates are balanced well between treatments. However, the variance of the imbalance of the unspecified covariates may be inflated comparing to the one under the simple randomization. The inflation of the variance causes the usual test of treatment effects being not valid and adjusting the test being not an easy work. In this paper, we propose a new kind covariate adaptive randomization procedures to balance covariates between two treatments with a ratio $\rho:(1-\rho)$. Under this kind of CAR procedures, the convergence rate of the imbalance of the specified covariates is $o(n^{1/2})$, and at the same time the asymptotic variance of the imbalance of any unspecified (observed or unobserved) covariates does not exceed the one under the simple randomization. The ``shift problem'' found by Liu, Hu, and Ma (2025) will not appear under the new CAR procedures.