Compound optimal designs for percentile estimation in dose–response models with restricted design intervals

Biedermann, Stefanie; Dette, Holger; Zhu, Wei

doi:10.1016/j.jspi.2007.04.003

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…We used an innovative optimization code in our work and are encouraged by the performance of the optimization method. Although the current program assumes an underlying logit dose response model, the code can be readily extended to accommodate other dose response models (Smith and Ridout 1998;Biedermann et al 2006Biedermann et al , 2007.…”

Section: Discussionmentioning

confidence: 99%

Controlled Optimal Design Program for the Logit Dose Response Model

Hu¹,

Zhu²,

Su³

et al. 2010

J. Stat. Soft.

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The assessment of dose-response is an integral component of the drug development process. Parallel dose-response studies are conducted, customarily, in preclinical and phase 1, 2 clinical trials for this purpose. Practical constraints on dose range, dose levels and dose proportions are intrinsic issues in the design of dose response studies because of drug toxicity, efficacy, FDA regulations, protocol requirements, clinical trial logistics, and marketing issues. We provide a free on-line software package called Controlled Optimal Design 2.0 for generating controlled optimal designs that can incorporate prior information and multiple objectives, and meet multiple practical constraints at the same time. Researchers can either run the web-based design program or download its stand-alone version to construct the desired multiple-objective controlled Bayesian optimal designs. Because researchers often adopt ad-hoc design schemes such as the equal allocation rules without knowing how efficient such designs would be for the design problem, the program also evaluates the efficiency of user-supplied designs.

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Section: Discussionmentioning

confidence: 99%

Controlled Optimal Design Program for the Logit Dose Response Model

Hu¹,

Zhu²,

Su³

et al. 2010

J. Stat. Soft.

Self Cite

View full text Add to dashboard Cite

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“…Several recent papers provided optimal designs for different models with various dose-finding objectives. [84][85][86][87][88][89][90] While many of these optimal designs are based on nonlinear models and depend on unknown model parameters, they do provide insight for practical use. One can also construct sequential or multistage adaptive designs utilizing likelihoodbased or Bayesian methods for parameter estimation to generate dose assignments near the ''true'' optimal design.…”

Section: A Combination Of Multiple Comparisons With Modeling Techniqumentioning

confidence: 99%

Novel Statistical Designs for Phase I/II and Phase II Clinical Trials With Dose-Finding Objectives

Sverdlov¹,

Wong

2014

Ther Innov Regul Sci

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In modern drug development, there has been an increasing interest in adaptive clinical trials-research designs that allow judicious modification of certain aspects of an ongoing clinical trial based on prespecified criteria according to accumulating data to achieve predetermined experimental objectives. A particularly important application of adaptive designs is in phase I and II stages of drug development. Many novel adaptive designs have been proposed in the context of phase I oncology trials of cytotoxic agents where acceptable toxicity frequently translates into therapeutic response. However, an assessment of efficacy measurements based on biomarkers in early development is also very important. The current paper gives an overview of adaptive designs for early development studies that utilize efficacy measurements in design adaptation rules. These include seamless phase I/II designs, where efficacy and safety considerations are both incorporated in dose-finding objectives, and phase II dose-response studies, which typically aim at establishing a dose-response relationship with respect to some efficacy outcome and at identifying the most promising doses to be tested in subsequent confirmatory trials. The authors discuss statistical, logistical, and regulatory aspects of these designs and provide perspectives on their applications in modern clinical trials.

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“…It is sometimes argued for 'standardizing' the criteria and (see e.g. Biedermann et al, 2007or also Mcgree et al, 2008 by employing…”

Section: General Approaches For Multipurpose Designsmentioning

confidence: 99%

Compound optimal spatial designs

Müller

Stehlík

2009

Environmetrics

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A single purpose design may be quite inefficient for handling a real-life problem. Therefore, we often need to incorporate more than one design criterion and a common approach is simply to construct a weighted average, which may depend upon different information matrices. Designs based upon this method have been termed compound designs. The need to satisfy more than one design criterion is particularly relevant in the context of random fields. It is evident that for precise universal kriging it is important not only to efficiently estimate the spatial trend parameters, but also the parameters of the variogram or covariance function. Both tasks could for instance be comprised by applying corresponding design criteria and constructing a compound design from there. Modern techniques for such first and second order characteristics will be suggested and reviewed in the presentation. A new hybrid stochastic exchange type optimization algorithm is proposed and an illustrating example of the design of a water-quality monitoring network is provided. COMPOUND OPTIMAL SPATIAL DESIGNS 355 Case 1. We are interested only in the trend parameters β and consider θ as known or a nuisance. Case 2. We are interested only in the covariance parameters θ. Case 3. We are interested in both sets of parameters. periment. In the following, we will define optimality of a design always strictly in the tradition of Kiefer (see e.g. Kiefer, 1959), where the inputs are selected such, that a prespecified design criterion (e.g. the determinant of the trend parameters variance-covariance matrix, so-called D-optimality) is optimized. The classic Fisher information M(β) = E[(∂ ln f (x, β))/∂β) 2 ], which is the basis for We also use now the notation [C(ξ)] ii = c(x i , x i ; θ) to emphasize the dependence of C on the design, and we assume knowledge of θ.Again all the entries can be condensed to a design criterion . If we now want to determine a Doptimal design for the whole parameter set, we can obviously-due to the orthogonality of the first and second order parameters-simply use the product of the respective determinants as an optimum design

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Compound optimal designs for percentile estimation in dose–response models with restricted design intervals

Cited by 9 publications

References 14 publications

Controlled Optimal Design Program for the Logit Dose Response Model

Controlled Optimal Design Program for the Logit Dose Response Model

Novel Statistical Designs for Phase I/II and Phase II Clinical Trials With Dose-Finding Objectives

Compound optimal spatial designs

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