Journal ArticleDOI
D- and c-optimal designs for exponential regression models used in viral dynamics and other applications
Cong Han,Kathryn Chaloner +1 more
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TLDR
In this paper, locally D-and c-optimal designs for exponential decay models with one to three parameters were derived analytically, and the invariance of optimal designs to reparametrization was discussed and examples of Bayesian optimal designs were also provided.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 2003-08-01. It has received 69 citations till now. The article focuses on the topics: Optimal design & Exponential decay.read more
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Model-based design of experiments for parameter precision: State of the art
TL;DR: An overview and critical analysis of the state of the art in this sector are proposed and the main contributions to model-based experiment design procedures in terms of novel criteria, mathematical formulations and numerical implementations are highlighted.
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On the de la Garza phenomenon
TL;DR: In this article, the authors show that the de la Garza phenomenon also holds for nonlinear models, such as the Emax model, exponential model, three-and four-parameter log-linear model, Emax-PK1 model, as well as many classical polynomial regression models.
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Design in nonlinear mixed effects models: Optimization using the Fedorov–Wynn algorithm and power of the Wald test for binary covariates
Sylvie Retout,Sylvie Retout,Emmanuelle Comets,Emmanuelle Comets,Adeline Samson,Adeline Samson +5 more
TL;DR: The methodology for designs evaluation and optimization in nonlinear mixed effects models is extended and an illustration of the decrease of human immunodeficiency virus viral load after antiretroviral treatment initiation described by a bi‐exponential model is illustrated.
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Optimal designs for the emax, log-linear and exponential models
TL;DR: In this article, locally D- and EDp-optimal designs for the exponential, log-linear and three-parameter emax models are derived at the same set of points, while the corresponding weights are different.
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D-optimal designs for logistic models with three and four parameters
Gang Li,Dibyen Majumdar +1 more
TL;DR: In this article, Locally D-optimal designs for logistic models with three and four parameters are investigated and it is shown that these designs are minimally supported, and the efficiency is computed for equally spaced and uniform designs.
References
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HIV-1 Dynamics in Vivo: Virion Clearance Rate, Infected Cell Life-Span, and Viral Generation Time
TL;DR: A new mathematical model was used to analyze a detailed set of human immunodeficiency virus-type 1 (HIV-1) viral load data collected from five infected individuals after the administration of a potent inhibitor of HIV-1 protease, providing not only a kinetic picture ofAIDS pathogenesis, but also theoretical principles to guide the development of treatment strategies.
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Hepatitis C Viral Dynamics in Vivo and the Antiviral Efficacy of Interferon-α Therapy
Avidan U. Neumann,Nancy P. Lam,Harel Dahari,David R. Gretch,Thelma E. Wiley,Thomas J. Layden,Alan S. Perelson +6 more
TL;DR: Findings show that infection with hepatitis C virus is highly dynamic and that early monitoring of viral load can help guide therapy, with blocking efficacies of 81, 95, and 96% for daily doses of 5, 10, and 15 million international units, respectively.
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Bayesian Experimental Design: A Review
TL;DR: This paper reviews the literature on Bayesian experimental design, both for linear and nonlinear models, and presents a uniied view of the topic by putting experimental design in a decision theoretic framework.
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Mathematical Analysis of HIV-1 Dynamics in Vivo
TL;DR: It is shown how dynamical modeling and parameter estimation techniques have uncovered important features of HIV pathogenesis and impacted the way in which AIDS patients are treated with potent antiretroviral drugs.
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On a Measure of the Information Provided by an Experiment
TL;DR: In this paper, a measure of the information provided by an experiment is introduced, derived from the work of Shannon and involves the knowledge prior to performing the experiment, expressed through a prior probability distribution over the parameter space.