Bayesian optimal designs for estimating a set of symmetrical quantiles

Zhu, Wei; Wong, Weng Kee

doi:10.1002/1097-0258(20010115)20:1<123::aid-sim643>3.0.co;2-5

Cited by 21 publications

(28 citation statements)

References 17 publications

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“…We were assured, however, of future considerations, at least as the 'gold standard', for the upcoming dose ranging trials. In addition to estimating the model parameters of the underlying dose response curve (Kalish, 1990;Meier et al, 1993;Zeng and Zhu, 1997;Zhu and Wong, 2000), there is also a great need to estimate other percentiles besides the median effective dose (α) (Flournoy, 1993;Rosenberger and Grill, 1997;Mats et al, 1997;Zhu and Wong, 2001). For example, the low percentiles are of interest in toxicity studies such as in virtually safe dose extrapolation problems, and the high percentiles are of interest in efficacy studies.…”

Section: Merck Dose Ranging Trial Revisitedmentioning

confidence: 99%

Optimal Designs for Dose–Response Models With Restricted Design Spaces

Biedermann

Dette

Zhu

2006

Journal of the American Statistical Association

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In dose response studies, the dose range is often restricted due to concerns over drug toxicity and/or efficacy. We present restricted and unrestricted interval locally optimal designs with respect to a very general class of optimality criteria for estimating the underlying dose response curve. The underlying curve belongs to a diversified set of link functions suitable for the dose response studies and having a common canonical form. These include the fundamental binary response models -the logit and the probit as well as the skewed versions of these models. The results are illustrated through the re-design of a dose ranging trial conducted at the Merck Research Laboratories (Zeng and Zhu, 1997). This work is a generalization of the results of Dai and Zhu (2002) in terms of the design interval, the underlying dose response curve and the optimality criterion.

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Section: Merck Dose Ranging Trial Revisitedmentioning

confidence: 99%

Optimal Designs for Dose–Response Models With Restricted Design Spaces

Biedermann

Dette

Zhu

2006

Journal of the American Statistical Association

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“…Illustrative applications of multiple objective designs for biomedical studies are available in Huang and Wong (1998), Zhu el at. (2000) and, Zhu and Wong (2000a, b, 2001). …”

Section: Web Sites For Finding Optimal Designsmentioning

confidence: 96%

Web-based tools for finding optimal designs in biomedical studies

Wong

2013

Computer Methods and Programs in Biomedicine

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Experimental costs are rising and applications of optimal design ideas are increasingly applied in many disciplines. However, the theory for constructing optimal designs can be esoteric and its implementation can be difficult. To help practitioners have easier access to optimal designs and better appreciate design issues, we present a web site at http://optimal-design.biostat.ucla.edu/optimal/ capable of generating different types of tailor-made optimal designs for popular models in the biological sciences. This site also evaluates various efficiencies of a user-specified design and so enables practitioners to appreciate robustness properties of the design before implementation.

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“…Our constrained optimization problems are thus more difficult because they have more variables to optimize over a continuous multi-dimensional space. Earlier, Zhu and Wong [27] also found multiple-objective optimal designs but their setup was limiting; their interest was confined only to estimating percentiles in a two parameter logistic model and did not discuss different types of criteria, which we have here.…”

Section: Multiple-objective Optimal Designsmentioning

confidence: 99%

Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels

Hyun

Wong

2015

The International Journal of Biostatistics

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We construct an optimal design to simultaneously estimate three common interesting features in a dose-finding trial with possibly different emphasis on each feature. These features are (1) the shape of the dose-response curve, (2) the median effective dose and (3) the minimum effective dose level. A main difficulty of this task is that an optimal design for a single objective may not perform well for other objectives. There are optimal designs for dual objectives in the literature but we were unable to find optimal designs for 3 or more objectives to date with a concrete application. A reason for this is that the approach for finding a dual-objective optimal design does not work well for a 3 or more multiple-objective design problem. We propose a method for finding multiple-objective optimal designs that estimate the three features with user-specified higher efficiencies for the more important objectives. We use the flexible 4-parameter logistic model to illustrate the methodology but our approach is applicable to find multiple-objective optimal designs for other types of objectives and models. We also investigate robustness properties of multiple-objective optimal designs to mis-specification in the nominal parameter values and to a variation in the optimality criterion. We also provide computer code for generating tailor made multiple-objective optimal designs.

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Bayesian optimal designs for estimating a set of symmetrical quantiles

Cited by 21 publications

References 17 publications

Optimal Designs for Dose–Response Models With Restricted Design Spaces

Optimal Designs for Dose–Response Models With Restricted Design Spaces

Web-based tools for finding optimal designs in biomedical studies

Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels

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