Sequential Generalized Likelihood Ratios and Adaptive Treatment Allocation for Optimal Sequential Selection

Chan, Hock Peng; Lai, T. L.

doi:10.1080/07474940600596695

Cited by 56 publications

(8 citation statements)

References 32 publications

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“…For normal outcomes, for instance, the adaptive treatment allocation by Chan and Lai (2006) may be applied to analogously transformed responses Y ij so as to accommodate situations for the selection with a control group. Due to the symmetric rules, blinding can be maintained even when a treatment arm is eliminated at an interim.…”

Section: Discussionmentioning

confidence: 99%

Simple Sequential Boundaries for Treatment Selection in Multi‐Armed Randomized Clinical Trials with a Control

Cheung

2007

Biometrics

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In situations when many regimens are possible candidates for a large phase III study, but too few resources are available to evaluate each relative to the standard, conducting a multi-armed randomized selection trial is a useful strategy to remove inferior treatments from further consideration. When the study has a relatively quick endpoint such as an imaging-based lesion volume change in acute stroke patients, frequent interim monitoring of the trial is ethically and practically appealing to clinicians. In this article, I propose a class of sequential selection boundaries for multi-armed clinical trials, in which the objective is to select a treatment with a clinically significant improvement upon the control group, or to declare futility if no such treatment exists. The proposed boundaries are easy to implement in a blinded fashion, and can be applied on a flexible monitoring schedule in terms of calendar time. Design calibration with respect to prespecified levels of confidence is simple, and can be accomplished when the response rate of the control group is known only up to an interval. One of the proposed methods is applied to redesign a selection trial with an imaging endpoint in acute stroke patients, and is compared to an optimal two-stage design via simulations: The proposed method imposes smaller sample size on average than the two-stage design; this advantage is substantial when there is in fact a superior treatment to the control group.

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Section: Discussionmentioning

confidence: 99%

Simple Sequential Boundaries for Treatment Selection in Multi‐Armed Randomized Clinical Trials with a Control

Cheung

2007

Biometrics

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“…Asymptotically efficient adaptive allocation schemes have been developed by Lai (1987) for the multi-armed bandit problem and by Chan and Lai (2006) for optimal selection of the population with the largest mean in an exponential family subject to a lower bound constraint on the probability of correct selection. Although the adaptive randomization scheme in the present article is in the same spirit, there are important differences.…”

Section: Discussionmentioning

confidence: 99%

“…Although the adaptive randomization scheme in the present article is in the same spirit, there are important differences. First, the optimization problem considered herein is related to testing the null hypothesis of no improvement in mean response of a new treatment, with k possible treatment strategies (arms), over a control treatment, whereas those in Lai (1987) and Chan and Lai (2006) are concerned with selecting the arm with the largest mean. Secondly, the setting considered herein is that of a randomized clinical trial in which patients are randomized to treatment arms, rather than being assigned to the treatments by some prescribed function of the past observations.…”

Section: Discussionmentioning

confidence: 99%

“…, k . Unlike the approach of Shun et al (2008), which does not consider the uncertainties in using the sample means to estimate the Δ i to choose the winning arm at the end of the first stage, we use insights from multi-armed bandit theory in Lai (1987) and optimal sequential selection of the best of k populations in Chan and Lai (2006) to come up with uncertainty adjustments via an adaptive randomization scheme. Moreover, unlike their fixed sample size procedure, we use efficient stopping rules that allow early stopping to either reject or accept H 0 .…”

Section: Introductionmentioning

confidence: 99%

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Efficient Adaptive Randomization and Stopping Rules in Multi-arm Clinical Trials for Testing a New Treatment

Lai

Liao

2012

Sequential Analysis

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Motivated by applications to confirmatory clinical trials for testing a new treatment against a placebo or active control when the new treatment has k possible treatment strategies (arms)—for example, k possible doses for a new drug—we develop an asymptotic theory for efficient outcome-adaptive randomization schemes and optimal stopping rules. Our approach consists of developing asymptotic lower bounds for the expected sample sizes from the k treatment arms and the control arm and using generalized sequential likelihood ratio procedures to achieve these bounds. Implementation details of our design and analysis and comparative simulation studies are also provided.

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“…In practice, however, one does not have any idea about the distance between the largest and second largest means. To address this difficulty, Chan and Lai [18] consider a stronger constraint that the probability of selecting a treatment whose mean effect is within δ of the largest is at least 1 − α . They also develop an efficient fully sequential procedure to attain this.…”

Section: Development and Validation Trials For Biomarker-guided Persomentioning

confidence: 99%

Group sequential designs for developing and testing biomarker-guided personalized therapies in comparative effectiveness research

Lai

Liao²,

Kim

2013

Contemporary Clinical Trials

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Biomarker-guided personalized therapies offer great promise to improve drug development and improve patient care, but also pose difficult challenges in designing clinical trials for the development and validation of these therapies. We first give a review of the existing approaches, briefly for clinical trials in new drug development and in more detail for comparative effectiveness trials involving approved treatments. We then introduce new group sequential designs to develop and test personalized treatment strategies involving approved treatments.

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Sequential Generalized Likelihood Ratios and Adaptive Treatment Allocation for Optimal Sequential Selection

Cited by 56 publications

References 32 publications

Simple Sequential Boundaries for Treatment Selection in Multi‐Armed Randomized Clinical Trials with a Control

Simple Sequential Boundaries for Treatment Selection in Multi‐Armed Randomized Clinical Trials with a Control

Efficient Adaptive Randomization and Stopping Rules in Multi-arm Clinical Trials for Testing a New Treatment

Group sequential designs for developing and testing biomarker-guided personalized therapies in comparative effectiveness research

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