Optimal Crossover Designs for Logistic Regression Models in Pharmacodynamics

Waterhouse, T. H.; Eccleston, J. A.; Duffull, Stephen B.

doi:10.1080/10543400600851880

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…We will assess the sensitivity of k * for all other k values in the domain 2 22 . The sensitivity of a D-optimal design is defined as a measurement of the efficiency when the design obtained by optimizing at a nominal parameter value is applied to other parameter values (Waterhouse et al, 2006). The sensitivity of apply a design * at parameter set where * is the D-optimal design optimized at a nominal parameter set * is given by…”

Section: Foo and Duffullmentioning

confidence: 99%

Methods of Robust Design of Nonlinear Models with an Application to Pharmacokinetics

Foo

Duffull

2010

Journal of Biopharmaceutical Statistics

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Optimal design methods for nonlinear models are dependent on the true but unknown parameter values. Criteria for developing designs that are robust to the choice of parameter values such as ED optimality have been proposed. However, these criteria are computationally intensive and can perform poorly at extremes of the prior parameter distribution. Two different criteria are proposed. Both involve evaluation of the determinant of the Fisher information matrix over models formed at various combinations of the 2.5th and 97.5th percentiles of the parameter space. The performance of the proposed optimality criteria is compared to two existing robust optimal design criteria.

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Section: Foo and Duffullmentioning

confidence: 99%

Methods of Robust Design of Nonlinear Models with an Application to Pharmacokinetics

Foo

Duffull

2010

Journal of Biopharmaceutical Statistics

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“…Indeed, much of experimental design focuses on parameter estimation. Many implementations of design for biological models exist in the literature, in particular the area of pharmacology (see D'Argenio (1981), Duffull et al (2005), Waterhouse et al (2006)). A design is optimal, that is, the best choice of covariate settings, through assessment of an optimality criterion.…”

Section: Experimental Designmentioning

confidence: 99%

Optimal designs for studying bioimpedance

McGree¹,

Duffull²,

Eccleston³

et al. 2007

Physiol. Meas.

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D-optimal designs for nonlinear fixed and mixed effects models are explored, and the theory is applied to the measurement and analysis of bioelectrical impedance. Bioimpedance is known to vary more at extreme frequencies than others. D-optimal designs that account for this variation, and also possible mis-specification of initial parameter estimates, are considered in an attempt to find designs that will provide good parameter estimates in practice.

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“…While methods for analyzing GLM data arising from crossover trials are available in Senn (2002) and Jones and Kenward (2014), the question of designing such studies for GLMs in an optimal manner does not seem to have been much explored in the statistical literature. Waterhouse et al (2006) studied optimal 2×2 crossover trial for binary data in some special cases, like the carryover effect is proportional to the direct treatment effect and no period effects are considered. Adaptive crossover designs restricted to two period two treatment binary data useful in clinical trials have also been investigated by Bandyopadhyay et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Bayesian crossover designs for generalized linear models

Singh

Mukhopadhyay

2016

Computational Statistics & Data Analysis

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This article discusses D-optimal Bayesian crossover designs for generalized linear models.Crossover trials with t treatments and p periods, for t <= p, are considered. The designs proposed in this paper minimize the log determinant of the variance of the estimated treatment effects over all possible allocation of the n subjects to the treatment sequences. It is assumed that the p observations from each subject are mutually correlated while the observations from different subjects are uncorrelated. Since main interest is in estimating the treatment effects, the subject effect is assumed to be nuisance, and generalized estimating equations are used to estimate the marginal means. To address the issue of parameter dependence a Bayesian approach is employed. Prior distributions are assumed on the model parameters which are then incorporated into the D-optimal design criterion by integrating it over the prior distribution. Three case studies, one with binary outcomes in a 4 × 4 crossover trial, second one based on count data for a 2 × 2 trial and a third one with Gamma responses in a 3 × 2 crossover trial are used to illustrate the proposed method. The effect of the choice of prior distributions on the designs is also studied.

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Optimal Crossover Designs for Logistic Regression Models in Pharmacodynamics

Cited by 8 publications

References 11 publications

Methods of Robust Design of Nonlinear Models with an Application to Pharmacokinetics

Methods of Robust Design of Nonlinear Models with an Application to Pharmacokinetics

Optimal designs for studying bioimpedance

Bayesian crossover designs for generalized linear models

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