Optimal Adaptive Designs for Binary Response Trials

Rosenberger, William F.; Stallard, Nigel; Ivanova, Anastasia; Harper, Cherice N.; Ricks, Michelle L.

doi:10.1111/j.0006-341x.2001.00909.x

Cited by 229 publications

(226 citation statements)

References 11 publications

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“…But we emphasize that there is nothing special about targeting the Neyman allocation, the whole methodology applying equally well to any choice of targeted treatment mechanism. To be more specific, we could also have chosen to target that treatment mechanism which minimizes the expected number of treatment failures subject to power constraint (the optimal allocation proposed by Rosenberger, Stallard, Ivanova, Harper, and Ricks (2001)), or any other treatment mechanism of interest (see for instance (Hu and Rosenberger, 2006, Chapter 2) or (Tymofyeyev, Rosenberger, and Hu, 2007)). Quoting James Hung of the FDA (about design adaptation in general-see (Hung, 2006)), our adaptive design meets clearly stated objectives, it is certainly a "more careful planning, not sloppy planning".…”

Section: The Notion Of Adaptive Group Sequential Designsmentioning

confidence: 99%

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Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Theoretical Study

Chambaz

Laan²

2011

The International Journal of Biostatistics

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This article is devoted to the asymptotic study of adaptive group sequential designs in the case of randomized clinical trials (RCTs) with binary treatment, binary outcome and no covariate. By adaptive design, we mean in this setting a RCT design that allows the investigator to dynamically modify its course through data-driven adjustment of the randomization probability based on data accrued so far, without negatively impacting on the statistical integrity of the trial. By adaptive group sequential design, we refer to the fact that group sequential testing methods can be equally well applied on top of adaptive designs. We obtain that, theoretically, the adaptive design converges almost surely to the targeted unknown randomization scheme. In the estimation framework, we obtain that our maximum likelihood estimator of the parameter of interest is a strongly consistent estimator, and it satisfies a central limit theorem. We can estimate its asymptotic variance, which is the same as that it would feature had we known in advance the targeted randomization scheme and independently sampled from it. Consequently, inference can be carried out as if we had resorted to independent and identically distributed (iid) sampling. In the testing framework, we obtain that the multidimensional t-statistic that we would use under iid sampling still converges to the same canonical distribution under adaptive sampling. Consequently, the same group sequential testing can be carried out as if we had resorted to iid sampling.Furthermore, a comprehensive simulation study that we undertake in a companion article validates the theory.A three-sentence take-home message is "Adaptive designs do learn the targeted optimal design and inference, and testing can be carried out under adaptive sampling as they would under the targeted optimal randomization probability iid sampling. In particular, adaptive designs achieve the same efficiency as the fixed oracle design. This is confirmed by a simulation study, at least for moderate or large sample sizes, across a large collection of targeted randomization probabilities.'"

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Section: The Notion Of Adaptive Group Sequential Designsmentioning

confidence: 99%

“…Once again, we emphasize that there is nothing special about targeting this specific treatment mechanism. One could have also chosen to target that treatment mechanism which minimizes the expected number of failures subject to power constraint (Rosenberger et al, 2001).…”

Section: A Relative Efficiency Criterionmentioning

confidence: 99%

Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Theoretical Study

Chambaz

Laan²

2011

The International Journal of Biostatistics

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“…We compare our proposed design with some exiting designs such as the RPW, DL, the design targeting the optimal allocation proportion (denoted as RSIHR, Rosenberger et al (2001)) and the design proposed by Bandyopadhyay and Bhattacharya (2006) (denoted as BB).…”

Section: A Comparison Of Designsmentioning

confidence: 99%

Response Adaptive Designs with a Variance‐penalized Criterion

Wang

2009

Biometrical J

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We consider a response adaptive design of clinical trials with a variance-penalized criterion. It is shown that this criterion evaluates the performance of a response adaptive design based on both the number of patients assigned to the better treatment and the power of the statistical test. A new proportion of treatment allocation is proposed and the doubly biased coin procedure is used to target the proposed proportion. Under reasonable assumptions, the proposed design is demonstrated to generate an asymptotic variance of allocation proportions, which is smaller than that of the drop-the-loser design. Simulation comparisons of the proposed design with some existing designs are presented.

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“…One form of optimality, for binary responses, consists of minimizing an aspect of behaviour, such as the total expected number of failures, for a given variance of the estimated treatment difference. Such designs include those of Rosenberger et al (2001) and Biswas and Mandal (2007) for binary responses. Rosenberger (2006, 2007) and find optimum designs for continuous responses.…”

Section: Introductionmentioning

confidence: 99%

Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses

Atkinson

Biswas

2017

Computational Statistics & Data Analysis

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Adaptive randomization of the sequential construction of optimum experimental designs is used to derive biased-coin designs for longitudinal clinical trials with continuous responses. The designs, coming from a very general rule, target pre-specified allocation proportions for the ranked treatment effects. Many of the properties of the designs are similar to those of wellunderstood designs for univariate responses. A numerical study illustrates this similarity in a comparison of four designs for longitudinal trials. Designs for multivariate responses can likewise be found, requiring only the appropriate information matrix. Some new results in the theory of optimum experimental design for multivariate responses are presented.

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Optimal Adaptive Designs for Binary Response Trials

Cited by 229 publications

References 11 publications

Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Theoretical Study

Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Theoretical Study

Response Adaptive Designs with a Variance‐penalized Criterion

Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses

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