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Inference in Targeted Group-Sequential Covariate-Adjusted Randomized Clinical Trials



This article is devoted to the construction and asymptotic study of adaptive, group-sequential, covariate-adjusted randomized clinical trials analysed through the prism of the semiparametric methodology of targeted maximum likelihood estimation. We show how to build, as the data accrue group-sequentially, a sampling design that targets a user-supplied optimal covariate-adjusted design. We also show how to carry out sound statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the independent and identically distributed setting only so far), and how group-sequential testing applies on top of it. The procedure is robust (i.e. consistent even if the working model is mis-specified). A simulation study confirms the theoretical results and validates the conjecture that the procedure may also be efficient.