Randomization Challenges in Adaptive Design Studies

Kuznetsova, Olga M.

doi:10.1007/978-1-4939-1100-4_9

Cited by 2 publications

(10 citation statements)

References 56 publications

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“…Condition (1). The probability of choosing any specific preferred treatment is at least ρ times higher than the probability of choosing any specific non-preferred treatment:…”

Section: Improved Implementation Of the Unequal Allocation Biased Coimentioning

confidence: 99%

“…Variations in the unconditional allocation ratio present a serious problem. First, while perfect blinding eliminates the opportunity for selection bias in a study with an ARP randomization, variations in the unconditional allocation ratio provide potential for selection and evaluation bias even in double‐blind studies . Indeed, if the investigator knows that the probability of the active allocation is much higher than its target allocation ratio at the 2nd, 5th, 8th, and so on allocations, they can assign subjects with better prognosis at these places in the allocation sequence.…”

Section: Introductionmentioning

confidence: 99%

“…Clinical trials often employ unequal allocation to the treatment arms. With the spread of adaptive design studies, unequal allocation is seen in dose‐ranging studies, multistage studies, studies with treatment selection design, studies with sample size re‐estimation, and studies with response‐adaptive randomization . The common reasons for unequal allocation use in studies of fixed (non‐adaptive) design are power considerations , improved attractiveness, and more extensive experience with the experimental arm in studies with higher allocation to the experimental arm .…”

Section: Introductionmentioning

confidence: 99%

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Approaches to expanding the two‐arm biased coin randomization to unequal allocation while preserving the unconditional allocation ratio

Kuznetsova

Johnson

2017

Statistics in Medicine

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The paper discusses three methods for expanding the biased coin randomization (BCR) to unequal allocation while preserving the unconditional allocation ratio at every step. The first method originally proposed in the contexts of BCR and minimization is based on mapping from an equal allocation multi-arm BCR. Despite the improvement proposed in this paper to ensure tighter adherence to the targeted unequal allocation, this method still distributes the probability mass at least as wide as the permuted block randomization (PBR). This works for smaller block sizes, but for larger block sizes, a tighter control of the imbalance in the treatment assignments is desired. The second method, which has two versions, allows to tighten the distribution of the imbalance compared with that achieved with the PBR. However, the distribution of the imbalance remains considerably wider than that of the brick tunnel randomization - the unequal allocation procedure with the tightest possible imbalance distribution among all allocation ratio preserving procedures with the same allocation ratio. Finally, the third method, the BCR with a preset proportion of maximal forcing, mimics the properties of the equal allocation BCR. With maximum forcing, it approaches the brick tunnel randomization, similar to how 1:1 BCR approaches 1:1 PBR with the permuted block size of 2 (the equal allocation procedure with the lowest possible imbalance) when the bias approaches 1. With minimum forcing, the BCR with a preset proportion of maximal forcing approaches complete randomization (similar to 1:1 BCR). Copyright © 2017 John Wiley & Sons, Ltd.

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“…Condition (1). The probability of choosing any specific preferred treatment is at least ρ times higher than the probability of choosing any specific non-preferred treatment:…”

Section: Improved Implementation Of the Unequal Allocation Biased Coimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Approaches to expanding the two‐arm biased coin randomization to unequal allocation while preserving the unconditional allocation ratio

Kuznetsova

Johnson

2017

Statistics in Medicine

Self Cite

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“…The common reasons include ethical or power considerations , improved acceptability of the trial with higher allocation to the experimental group, increased exposure to the experimental treatment, increased power for secondary analyses, reduced cost of the treatment when fewer patients are placed on the more expensive treatment, the need to have more patients on specific treatment in the second phase of the trial , and maximized power for pairwise comparisons in dose–response studies . Unequal allocation is often used in adaptive design studies with two or more treatment arms: dose‐ranging studies, multistage studies, studies with treatment selection design, studies with sample size re‐estimation, and studies with response‐adaptive randomization .…”

Section: Introductionmentioning

confidence: 99%

“…We will also explain, based on the concept of the center of probability mass, some properties of the BTR resident probabilities observed in earlier work [7].A narrow allocation space of the BTR guarantees that the observed allocation ratio is close to the target one throughout the enrollment; it also reduces the accidental bias associated with the time trend. This is helpful in adaptive design dose-ranging studies [5,24,25], studies with a time trend in response or baseline characteristics, and multicenter studies [7,25]. However, in a single-center open-label trial, where the investigator knows the sequence of previous treatment assignments, it makes the allocation procedure more predictable and thus prone to selection bias.…”

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confidence: 99%

Brick tunnel randomization and the momentum of the probability mass

Kuznetsova

2015

Statistics in Medicine

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The allocation space of an unequal-allocation permuted block randomization can be quite wide. The development of unequal-allocation procedures with a narrower allocation space, however, is complicated by the need to preserve the unconditional allocation ratio at every step (the allocation ratio preserving (ARP) property). When the allocation paths are depicted on the K-dimensional unitary grid, where allocation to the l-th treatment is represented by a step along the l-th axis, l = 1 to K, the ARP property can be expressed in terms of the center of the probability mass after i allocations. Specifically, for an ARP allocation procedure that randomizes subjects to K treatment groups in w1 :⋯:wK ratio, w1 +⋯+wK =1, the coordinates of the center of the mass are (w1 i,…,wK i). In this paper, the momentum with respect to the center of the probability mass (expected imbalance in treatment assignments) is used to compare ARP procedures in how closely they approximate the target allocation ratio. It is shown that the two-arm and three-arm brick tunnel randomizations (BTR) are the ARP allocation procedures with the tightest allocation space among all allocation procedures with the same allocation ratio; the two-arm BTR is the minimum-momentum two-arm ARP allocation procedure. Resident probabilities of two-arm and three-arm BTR are analytically derived from the coordinates of the center of the probability mass; the existence of the respective transition probabilities is proven. Probability of deterministic assignments with BTR is found generally acceptable. Copyright © 2015 John Wiley & Sons, Ltd.

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Randomization Challenges in Adaptive Design Studies

Cited by 2 publications

References 56 publications

Approaches to expanding the two‐arm biased coin randomization to unequal allocation while preserving the unconditional allocation ratio

Approaches to expanding the two‐arm biased coin randomization to unequal allocation while preserving the unconditional allocation ratio

Brick tunnel randomization and the momentum of the probability mass

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