Response-adaptive designs for clinical trials: Simultaneous learning from multiple patients

2016 ◽  
Vol 248 (2) ◽  
pp. 619-633 ◽  
Author(s):  
Vishal Ahuja ◽  
John R. Birge
1993 ◽  
Vol 14 (6) ◽  
pp. 471-484 ◽  
Author(s):  
William F. Rosenberger ◽  
John M. Lachin

Test ◽  
2021 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Marco Novelli ◽  
Maroussa Zagoraiou

AbstractThis paper discusses disadvantages and limitations of the available inferential approaches in sequential clinical trials for treatment comparisons managed via response-adaptive randomization. Then, we propose an inferential methodology for response-adaptive designs which, by exploiting a variance stabilizing transformation into a bootstrap framework, is able to overcome the above-mentioned drawbacks, regardless of the chosen allocation procedure as well as the desired target. We derive the theoretical properties of the suggested proposal, showing its superiority with respect to likelihood, randomization and design-based inferential approaches. Several illustrative examples and simulation studies are provided in order to confirm the relevance of our results.


2016 ◽  
Vol 27 (3) ◽  
pp. 891-904 ◽  
Author(s):  
Fumiyasu Komaki ◽  
Atanu Biswas

Response-adaptive designs are used in phase III clinical trials to allocate a larger number of patients to the better treatment arm. Optimal designs are explored in the recent years in the context of response-adaptive designs, in the frequentist view point only. In the present paper, we propose some response-adaptive designs for two treatments based on Bayesian prediction for phase III clinical trials. Some properties are studied and numerically compared with some existing competitors. A real data set is used to illustrate the applicability of the proposed methodology where we redesign the experiment using parameters derived from the data set.


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