Comparison of Robust Criteria for D-Optimal Designs

Foo, Lee Kien; McGree, James; Eccleston, J. A.; Duffull, Stephen B.

doi:10.1080/10543406.2011.580484

Cited by 11 publications

(9 citation statements)

References 9 publications

(11 reference statements)

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“…Alternatives such as Monte Carlo integration [61], Laplace approximation [62], or adaptive Gaussian quadrature [63] could be used with our models. Wide ranges of individual RSE were also observed from CTS for the same parameters, which suggested the development of robust criteria based on M BF that take into account extreme individuals, inspired from those already developed for M PF [64]. Previously, it has been shown that the optimal population design gives satisfactory efficiency for MAP estimation of individual parameters [20], that is why here we optimized first the best one group population design and then evaluated it for individual estimation.…”

Section: Discussionmentioning

confidence: 84%

Design optimisation for pharmacokinetic modeling of a cocktail of phenotyping drugs

Nguyễn

Bénech

Delaforge

et al. 2015

Pharmaceutical Statistics

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Our paper proposes a methodological strategy to select optimal sampling designs for phenotyping studies including a cocktail of drugs. A cocktail approach is of high interest to determine the simultaneous activity of enzymes responsible for drug metabolism and pharmacokinetics, therefore useful in anticipating drug-drug interactions and in personalized medicine. Phenotyping indexes, which are area under the concentration-time curves, can be derived from a few samples using nonlinear mixed effect models and maximum a posteriori estimation. Because of clinical constraints in phenotyping studies, the number of samples that can be collected in individuals is limited and the sampling times must be as flexible as possible. Therefore to optimize joint design for several drugs (i.e., to determine a compromise between informative times that best characterize each drug's kinetics), we proposed to use a compound optimality criterion based on the expected population Fisher information matrix in nonlinear mixed effect models. This criterion allows weighting different models, which might be useful to take into account the importance accorded to each target in a phenotyping test. We also computed windows around the optimal times based on recursive random sampling and Monte-Carlo simulation while maintaining a reasonable level of efficiency for parameter estimation. We illustrated this strategy for two drugs often included in phenotyping cocktails, midazolam (probe for CYP3A) and digoxin (P-glycoprotein), based on the data of a previous study, and were able to find a sparse and flexible design. The obtained design was evaluated by clinical trial simulations and shown to be efficient for the estimation of population and individual parameters.

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Section: Discussionmentioning

confidence: 84%

Design optimisation for pharmacokinetic modeling of a cocktail of phenotyping drugs

Nguyễn

Bénech

Delaforge

et al. 2015

Pharmaceutical Statistics

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“…13 More efficient algorithms could also be explored, such as stochastic approximation, annealing methods, randomized exchange algorithm, 45 multiplicative method 46 or particle swarm optimization. 47,48 It is also important to account for uncertainty in parameters in addition to uncertainty in models, 49,50 by evaluating the expectation of the FIM over the distribution of population parameters instead of assuming known values of these parameters. We also plan to integrate these robust design methods (over the parameter distribution and over the models) in a next version of PFIM, which is the software program for designing longitudinal studies developed by our team (www.pfim.biostat.fr).…”

Section: Discussionmentioning

confidence: 99%

Robust designs accounting for model uncertainty in longitudinal studies with binary outcomes

Seurat

Nguyễn

2019

Stat Methods Med Res

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To optimize designs for longitudinal studies analyzed by mixed-effect models with binary outcomes, the Fisher information matrix can be used. Optimal design approaches, however, require a priori knowledge of the model. We aim to propose, for the first time, a robust design approach accounting for model uncertainty in longitudinal trials with two treatment groups, assuming mixed-effect logistic models. To optimize designs given one model, we compute several optimality criteria based on Fisher information matrix evaluated by the new approach based on Monte-Carlo/Hamiltonian Monte-Carlo. We propose to use the DDS-optimality criterion, as it ensures a compromise between the precision of estimation of the parameters, and hence the Wald test power, and the overall precision of parameter estimation. To account for model uncertainty, we assume candidate models with their respective weights. We compute robust design across these models using compound DDS-optimality. Using the Fisher information matrix, we propose to predict the average power over these models. Evaluating this approach by clinical trial simulations, we show that the robust design is efficient across all models, allowing one to achieve good power of test. The proposed design strategy is a new and relevant approach to design longitudinal studies with binary outcomes, accounting for model uncertainty.

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“…However, it requires a priori knowledge of the model and its parameters, which can usually be obtained from previous experiments and which leads to locally optimal designs. Alternatives to locally optimal designs are robust designs, relying on a priori distribution of parameters [24,25], or adaptive designs, which use accumulating information in order to decide how to modify predefined aspects of the study during its implementation instead of leaving them fixed until the end [26,27]. An adaptive design approach in NLMEMs that optimizes the design of each cohort while taking into account previous FIM obtained from previous cohorts has been proposed [28,29].…”

Section: Introductionmentioning

confidence: 99%

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

Dumont

Lestini

Nagard

et al. 2018

Computer Methods and Programs in Biomedicine

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Comparison of Robust Criteria for D-Optimal Designs

Cited by 11 publications

References 9 publications

Design optimisation for pharmacokinetic modeling of a cocktail of phenotyping drugs

Design optimisation for pharmacokinetic modeling of a cocktail of phenotyping drugs

Robust designs accounting for model uncertainty in longitudinal studies with binary outcomes

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

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