Covariate-adjusted response-adaptive designs based on semiparametric approaches

We consider theoretical and practical issues for innovatively using a large number of covariates in clinical trials to achieve various design objectives without model misspecification. Specifically, we propose a new family of semiparametric covariate-adjusted response-adaptive randomization (CARA) designs and we use the target maximum likelihood estimation (TMLE) to analyze the correlated data from CARA designs. Our approach can flexibly achieve multiple objectives and correctly incorporate the effect of a large number of covariates on the responses without model misspecification. We also obtain the consistency and asymptotic normality of the target parameters, allocation probabilities, and allocation proportions. Numerical studies demonstrate that our approach has advantages over existing approaches, even when the data-generating distribution is complicated.

Automated summary

This study investigates the use of a large number of covariates in clinical trials to achieve various design objectives without model misspecification. A new family of semiparametric covariate-adjusted response-adaptive randomization (CARA) designs is proposed, and the target maximum likelihood estimation (TMLE) is used to analyze the correlated data from CARA designs. The approach can achieve multiple objectives and correctly incorporate the effect of a large number of covariates on the responses without model misspecification. Consistency and asymptotic normality of the target parameters, allocation probabilities, and allocation proportions are also obtained. Numerical studies demonstrate the advantages of the approach over existing approaches, even with complicated data-generating distributions.