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Abstract

Covariate-adjusted response-adaptive (CARA) design becomes an important statistical tool for evaluating and comparing the performance of treatments when targeted medicine and adaptive therapy become important medical innovations. Due to the nature of the adaptive therapies of interest and how subjects accrue to a sampling procedure, it is of interest how to control the sample size sequentially such that the estimates of treatment effects have satisfactory precision in addition to its asymptotic properties. In this paper, we apply a multiple-stage sequential sampling method to CARA design in such a way that the control of the sample size is more feasible. The theoretical properties of the proposed method, including the estimates of regression parameters and the allocation probabilities under this randomly stopped sampling procedure, are discussed. The numerical results based on synthesized data and a real example are presented.

Keywords

covaraite adjustment response-adaptive design multiple-stage stopping rule confidence set

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Multiple-stage sampling procedure for covariate-adjusted response-adaptive designs

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