Optimal screening designs with flexible cost and constraint structures

Stout, Quentin F.; Hardwick, Janis

doi:10.1016/j.jspi.2004.06.020

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…Their boundaries are optimal with respect to a utility function that incorporates sampling cost and potential pay-off if the pivotal trial shows significant benefit, similar to utility function at the end of Section 2.3. Stout and Hardwick (2005) present a general decision-theoretic framework for finding optimal designs for screening trials, incorporating costs and constraints.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimal Design for Phase II Screening Trials

2007

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SUMMARYMost phase II screening designs available in the literature consider one treatment at a time. Each study is considered in isolation. We propose a more systematic decision-making approach to the phase II screening process. The sequential design allows for more efficiency and greater learning about treatments. The approach incorporates a Bayesian hierarchical model that allows combining information across several related studies in a formal way and improves estimation in small data sets by borrowing strength from other treatments. The design incorporates a utility function that includes sampling costs and possible future payoff. Computer simulations show that this method has high probability of discarding treatments with low success rates and moving treatments with high success rates to phase III trial.

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Section: Introductionmentioning

confidence: 99%

Bayesian Optimal Design for Phase II Screening Trials

2007

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“…A general loss function including the three partial costs of Chen et al (2004b) as well as the salvage value is introduced to determine the corresponding optimal sampling plan. Stout and Hardwick (2005) present a unified approach to the problem of response adaptive screening, when multiple costs and constraints need to be incorporated. In particular, they describe a cost-and constraint-based approach, which is suitable in AS where sample products must be tested before an entire lot is accepted.…”

Section: Related Literaturementioning

confidence: 99%

A New Model for the Representation of the ISO 2859 Standard

Nenes

Nikolaidis

2014

Communications in Statistics - Theory and Methods

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In order to carry out acceptance sampling, companies often use sampling schemes recommended by easy-to-use quality standards. These standards, however, do not take quality costs directly into account. This research proposes a new model for the representation of the use of ISO 2859ISO -1 (1999 standard. Using this new model, it is feasible to evaluate economically the use of the standard. A real case example is presented and the quality cost of the standard's implementation is evaluated.

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“…Under model (1) and the encompassing prior (6), the marginal density of data y under hypothesis H j is given by We can see that c j is independent of s 2 , which greatly simplifies the evaluation of the marginal density of y. Then it follows that…”

Section: Conflict Of Interestmentioning

confidence: 99%

“…Yao and Venkatraman [4], and Wang and Leung [5] extended that design to two-stage and fully sequential designs. Stout and Hardwick [6] developed a cost- and constraint-based decision-theoretic approach to the design of screening trials. Rossell et al [7] proposed a screening design based on Bayesian decision theoretics that uses optimal linear boundaries.…”

Section: Introductionmentioning

confidence: 99%

Bayesian adaptive phase II screening design for combination trials

2013

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Background Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Methods Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Results Simulation studies show that the proposed design substantially outperforms the conventional multiarm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while allocating substantially more patients to efficacious treatments. Limitations The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. Conclusions The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while providing higher power to identify the best treatment at the end of the trial.

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Optimal screening designs with flexible cost and constraint structures

Cited by 8 publications

References 8 publications

Bayesian Optimal Design for Phase II Screening Trials

Bayesian Optimal Design for Phase II Screening Trials

A New Model for the Representation of the ISO 2859 Standard

Bayesian adaptive phase II screening design for combination trials

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