scholarly journals A new inferential approach for response-adaptive clinical trials: the variance-stabilized bootstrap

Test ◽  
2021 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Marco Novelli ◽  
Maroussa Zagoraiou
Keyword(s):  
Clinical Trials ◽  
Adaptive Designs ◽  
Simulation Studies ◽  
Adaptive Randomization ◽  
Allocation Procedure ◽  
Adaptive Clinical Trials ◽  
Response Adaptive Designs ◽  
Treatment Comparisons

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.

scholarly journals A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials

2021 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Marco Novelli ◽  
Maroussa Zagoraiou
Keyword(s):  
Clinical Trials ◽  
Primary Outcome ◽  
Wald Test ◽  
Simple Solution ◽  
Nuisance Parameters ◽  
Simulation Studies ◽  
Fundamental Properties ◽  
Adaptive Clinical Trials ◽  
Treatment Comparisons ◽  
Practical Recommendations

AbstractThe present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.

The use of response-adaptive designs in clinical trials

1993 ◽  
Vol 14 (6) ◽  
pp. 471-484 ◽  
Author(s):  
William F. Rosenberger ◽  
John M. Lachin
Keyword(s):  
Clinical Trials ◽  
Adaptive Designs ◽  
Response Adaptive Designs

A Bayesian response-adaptive trial in tuberculosis: The endTB trial

2016 ◽  
Vol 14 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Matteo Cellamare ◽  
Steffen Ventz ◽  
Elisabeth Baudin ◽  
Carole D. Mitnick ◽  
Lorenzo Trippa
Keyword(s):  
Statistical Power ◽  
Treatment Success ◽  
Multidrug Resistant ◽  
Adaptive Designs ◽  
Primary Study ◽  
Adaptive Randomization ◽  
Resistant Tuberculosis ◽  
Timely Fashion ◽  
Response Adaptive Designs ◽  
New Treatments

Purpose: To evaluate the use of Bayesian adaptive randomization for clinical trials of new treatments for multidrug-resistant tuberculosis. Methods: We built a response-adaptive randomization procedure, adapting on two preliminary outcomes for tuberculosis patients in a trial with five experimental regimens and a control arm. The primary study outcome is treatment success after 73 weeks from randomization; preliminary responses are culture conversion at 8 weeks and treatment success at 39 weeks. We compared the adaptive randomization design with balanced randomization using hypothetical scenarios. Results: When we compare the statistical power under adaptive randomization and non-adaptive designs, under several hypothetical scenarios we observe that adaptive randomization requires fewer patients than non-adaptive designs. Moreover, adaptive randomization consistently allocates more participants to effective arm(s). We also show that these advantages are limited to scenarios consistent with the assumptions used to develop the adaptive randomization algorithm. Conclusion: Given the objective of evaluating several new therapeutic regimens in a timely fashion, Bayesian response-adaptive designs are attractive for tuberculosis trials. This approach tends to increase allocation to the effective regimens.

scholarly journals The Bayesian Design of Adaptive Clinical Trials

2021 ◽  
Vol 18 (2) ◽  
pp. 530
Author(s):  
Alessandra Giovagnoli
Keyword(s):  
Clinical Trials ◽  
Adaptive Design ◽  
Recent Literature ◽  
Adaptive Designs ◽  
Bayesian Design ◽  
High Expectations ◽  
Reading Material ◽  
Adaptive Trials ◽  
Adaptive Clinical Trials ◽  
Interesting Reading

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.

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