Optimal adaptive generalized Pólya urn design for multi-arm clinical trials

Yuan, Ao; Chai, Gen Xiang

doi:10.1016/j.jmva.2006.12.004

Cited by 10 publications

(5 citation statements)

References 39 publications

(79 reference statements)

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“…The use of continuous contents, such as the mass weighted rule, in an urn model is not new [24]. It allows the MWUD to target any desired unequal allocation in the real number domain without approximation.…”

Section: Discussionmentioning

confidence: 99%

Mass weighted urn design — A new randomization algorithm for unequal allocations

Zhao

2015

Contemporary Clinical Trials

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Unequal allocations have been used in clinical trials motivated by ethical, efficiency, or feasibility concerns. Commonly used permuted block randomization faces a tradeoff between effective imbalance control with a small block size and accurate allocation target with a large block size. Few other unequal allocation randomization designs have been proposed in literature with applications in real trials hardly ever been reported, partly due to their complexity in implementation compared to the permuted block randomization. Proposed in this paper is the mass weighted urn design, in which the number of balls in the urn equals to the number of treatments, and remains unchanged during the study. The chance a ball being randomly selected is proportional to the mass of the ball. After each treatment assignment, a part of the mass of the selected ball is re-distributed to all balls based on the target allocation ratio. This design allows any desired optimal unequal allocations be accurately targeted without approximation, and provides a consistent imbalance control throughout the allocation sequence. The statistical properties of this new design is evaluated with the Euclidean distance between the observed treatment distribution and the desired treatment distribution as the treatment imbalance measure; and the Euclidean distance between the conditional allocation probability and the target allocation probability as the allocation predictability measure. Computer simulation results are presented comparing the mass weighted urn design with other randomization designs currently available for unequal allocations.

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Section: Discussionmentioning

confidence: 99%

Mass weighted urn design — A new randomization algorithm for unequal allocations

Zhao

2015

Contemporary Clinical Trials

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“…But there is a cost, and a rather steep one at that. Each forced allocation is a deterministic allocation, and these are predictable, and have the potential to lead to selection bias by eliminating the possibility of allocation concealment [10] . It is not possible to simultaneously eliminate both chronological bias and selection bias [9] .…”

Section: Rocket Powered Big Sticksmentioning

confidence: 99%

“…Given this popularity and near ubiquity of permuted block randomization, one might expect, on this basis alone, that the permuted blocks design is also, in some sense, optimal, since it is fair to ask why it would be used so often if this was not the case. Unfortunately, this is actually not the case, and for this reason many competitors have been proposed [2] , [3] , [8] , [9] , [10] . One newer and better class, which is gaining in popularity, is the so-called class of MTI procedures, which use a big stick to force the allocation sequence back towards balance when it reaches the MTI (maximally tolerated imbalance).…”

Section: Introductionmentioning

confidence: 99%

Characterizing permuted block randomization as a big stick procedure

Berger

Odia

2016

Contemporary Clinical Trials Communications

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There are numerous approaches to randomizing patients to treatment groups in clinical trials. The most popular is permuted block randomization, and a newer and better class, which is gaining in popularity, is the so-called class of MTI procedures, which use a big stick to force the allocation sequence back towards balance when it reaches the MTI (maximally tolerated imbalance). Three prominent members of this class are the aptly named big stick procedure, Chen's procedure, and the maximal procedure. As we shall establish in this article, blocked randomization, though not typically cast as an MTI procedure, does in fact use the big stick as well. We shall argue that its weaknesses, which are well known, arise precisely from its improper use, bordering on outright abuse, of this big stick. Just as rocket powered golf clubs add power to a golf swing, so too does the big stick used by blocked randomization hit with too much power. In addition, the big stick is invoked when it need not be, thereby resulting in the excessive prediction for which permuted blocks are legendary. We bridge the gap between the MTI procedures and block randomization by identifying a new randomization procedure intermediate between the two, namely based on an excessively powerful big stick, but one that is used only when needed. We shall then argue that the MTI procedures are all superior to this intermediate procedure by virtue of using a restrained big stick, and that this intermediate procedure is superior to block randomization by virtue of restraint in when the big stick is invoked. The transitivity property then completes our argument.

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“…Other designs for irrational target allocations can be constructed by adopting the methodology of optimal response-adaptive randomization (30). Some promising designs for this purpose are the doubly adaptive biased coin design (31), the generalized drop-theloser urn (32), and the optimal adaptive generalized Pólya urn (33), to name a few. However, all these designs rely on asymptotic results which may not hold in small to moderate sample sizes that are common in practice.…”

Section: Introductionmentioning

confidence: 99%

Implementing Optimal Designs for Dose–Response Studies Through Adaptive Randomization for a Small Population Group

Ryeznik

Sverdlov²,

Hooker

2018

AAPS J

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In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.

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Optimal adaptive generalized Pólya urn design for multi-arm clinical trials

Cited by 10 publications

References 39 publications

Mass weighted urn design — A new randomization algorithm for unequal allocations

Mass weighted urn design — A new randomization algorithm for unequal allocations

Characterizing permuted block randomization as a big stick procedure

Implementing Optimal Designs for Dose–Response Studies Through Adaptive Randomization for a Small Population Group

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