Comparison of response adaptive randomization features in multiarm clinical trials with control

Viele, Kert; Saville, Benjamin R.; McGlothlin, Anna; Broglio, Kristine

doi:10.1002/pst.2015

Cited by 20 publications

(39 citation statements)

References 28 publications

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“…If such a continuous monitoring scheme is difficult to employ in practice, for example, for logistic reasons, it can in principle be replaced by any more appropriate non-informative stopping rule. However, the results in Viele et al [ 35 ] suggest that, in AR designs, more frequent checks and updates are advantageous from the perspective of several different performance measures, and the same is likely to hold for BARTA as well.…”

Section: The Case Of Bernoulli Outcomesmentioning

confidence: 99%

“…Villar et al [ 30 ] contains a useful review of the theoretical background, connecting the theory of the optimal design of clinical trials with that of multi-armed bandit problems. Of particular interest to us are papers dealing with Bayesian versions of AR, where the randomization probabilities are updated directly by applying Bayes’ rule, e.g., Trippa et al [ 31 ], Wathen and Thall [ 32 ], Wathen and Thall [ 33 ], Viele et al [ 34 ], Viele et al [ 35 ] and Bassi et al [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…This is often done indirectly by applying pre-specified stopping boundaries, to determine whether a considered treatment should be dropped. Recent contributions to such designs include Wason and Jaki [ 37 ], Wason and Trippa [ 38 ], Jacob et al [ 39 ], Wathen and Thall [ 32 ], Yu et al [ 40 ], Ryan et al [ 33 ] and Ryan et al [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Arjas

Gasbarra

2022

BMC Med Res Methodol

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Background Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power. Results The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during phase II and III. This approach is based on comparing the performance of the different treatment arms in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm, and treatment selection, removing an arm from the trial permanently. The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package ’barts’. Conclusion The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

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Section: The Case Of Bernoulli Outcomesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Arjas

Gasbarra

2022

BMC Med Res Methodol

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“…For example, Thall et al 10 comment on some inferential problems of implementing BAR. Viele et al 11 compare multiple features of response adaptive randomization based on various metrics. Jiang et al 12 investigate the impact of time tend on treatment estimation when BAR is implemented.…”

Section: Introductionmentioning

confidence: 99%

“…Viele et al. 11 compare multiple features of response adaptive randomization based on various metrics. Jiang et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Lee

2021

Stat Methods Med Res

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Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.

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Decision rules for identifying combination therapies in open‐entry, randomized controlled platform trials

Meyer

Mesenbrink

Dunger-Baldauf

et al. 2022

Pharmaceutical Statistics

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Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies.Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open-entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.

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Comparison of response adaptive randomization features in multiarm clinical trials with control

Cited by 20 publications

References 28 publications

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Decision rules for identifying combination therapies in open‐entry, randomized controlled platform trials

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