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Response adaptive randomization is appealing in confirmatory adaptive clinical trials from statistical, ethical, and pragmatic perspectives, in the sense that subjects are more likely to be randomized to better performing treatment groups based on accumulating data. The Doubly Adaptive Biased Coin Design (DBCD) is a popular solution due to its asymptotic normal property of final allocations, which further justifies its asymptotic type I error rate control. As an alternative, we propose a Response Adaptive Block Randomization (RABR) design with pre-specified randomization ratios for the control and high-performing groups to robustly achieve desired final sample size per group under different underlying responses, which is usually required in industry-sponsored clinical studies. We show that the usual test statistic has a controlled type I error rate. Our simulations further highlight the advantages of the proposed design over the DBCD in terms of consistently achieving final sample allocations and of power performance. We further apply this design to a Phase III study evaluating the efficacy of two dosing regimens of adjunctive everolimus in treating tuberous sclerosis complex but with no previous dose-finding studies in this indication.
Response-adaptive randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are used as a motivating application. In that context, patient allocation to treatments is determined by randomization pro
One central goal of design of observational studies is to embed non-experimental data into an approximate randomized controlled trial using statistical matching. Researchers then make the randomization assumption in their downstream, outcome analysis
Suppose an online platform wants to compare a treatment and control policy, e.g., two different matching algorithms in a ridesharing system, or two different inventory management algorithms in an online retail site. Standard randomized controlled tri
In this paper, we study the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile
Covariate-adaptive randomization schemes such as the minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theoretical developments on inference after