Design of cross-over trials for pharmacokinetic studies

Jones, B.; Wang, J.; Jarvis, Philip; Byrom, Bill

doi:10.1016/s0378-3758(98)00221-3

Cited by 16 publications

(8 citation statements)

References 14 publications

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“…The block A is a p ×p-symmetric matrix for the ÿxed e ects and is similar to the expression found by Jones and Wang [14] who developed the Fisher matrix for the ÿxed e ects only. The second block B = 1 2 F is a (p + 1) × (p + 1)-symmetric matrix for the variances and can be simpliÿed with:…”

Section: The Fisher Information Matrixmentioning

confidence: 59%

Fisher information matrix for non‐linear mixed‐effects models: evaluation and application for optimal design of enoxaparin population pharmacokinetics

Retout

Mentré

Bruno³

2002

Statistics in Medicine

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We address the problem of the choice and the evaluation of designs in population pharmacokinetic studies that use non-linear mixed-effects models. Criteria, based on the Fisher information matrix, have been developed to optimize designs and adapted to such models. We optimize designs under different constraints and evaluate them for a population pharmacokinetics study, within a new phase III trial of enoxaparin, a low molecular weight heparin. To do this, we approximate the expression of the Fisher information matrix for non-linear mixed-effects models including the residual error variance as a parameter to be estimated. We use the Fedorov-Wynn algorithm to minimize the inverse of the determinant of this matrix as required by the D-optimality criterion. Two optimal designs, as well as a design defined by pharmacologists, are evaluated by the simulation of 30 replicated data sets with NONMEM; all designs involve 220 patients with four measurements per patient. We also evaluate the relevance of the standard errors of estimation given from the Fisher information matrix by comparison with those given by NONMEM. The three designs provide more precise population parameter estimates; the optimal design gives the best precision and offers a simple clinical implementation. The expected standard errors given by the information matrix are close to those obtained by NONMEM on the simulation. Moreover, the proposed criterion of D-optimality appears to be a good measure to compare designs for population studies.

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Section: The Fisher Information Matrixmentioning

confidence: 59%

Fisher information matrix for non‐linear mixed‐effects models: evaluation and application for optimal design of enoxaparin population pharmacokinetics

Retout

Mentré

Bruno³

2002

Statistics in Medicine

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“…The problem is considerably more involved than our introductory pharmacokinetic example. Readers are referred to Jones et al , 29 Jones and Wang 30 and Jones and Donev 31 for examples of optimal design methods for cross‐over designs. To date, software applications for the evaluation of the information matrix are available only to the level of population pharmacokinetic studies 27 …”

Section: Data‐independent Methodsmentioning

confidence: 99%

“…There are many criteria that can be chosen, such as any from the so‐called alphabetic optimality series, 21 but the most common is D‐optimality (see Box and Lucas 33 for an early example; Fedorov 34 for explanatory details and the works of D’Argenio 23 and Mentré et al 25 for pharmacokinetic examples) and, less commonly, C‐optimality (see Landaw 35 for a description) and A‐optimality 29 . D‐Optimality is described in a little more detail.…”

Section: Data‐independent Methodsmentioning

confidence: 99%

Design Of Clinical Pharmacology Trials

Duffull

2001

Clin Exp Pharma Physio

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1. There are a variety of methods that could be used to increase the efficiency of the design of experiments. However, it is only recently that such methods have been considered in the design of clinical pharmacology trials. 2. Two such methods, termed data-dependent (e.g. simulation) and data-independent (e.g. analytical evaluation of the information in a particular design), are becoming increasingly used as efficient methods for designing clinical trials. These two design methods have tended to be viewed as competitive, although a complementary role in design is proposed here. 3. The impetus for the use of these two methods has been the need for a more fully integrated approach to the drug development process that specifically allows for sequential development (i.e. where the results of early phase studies influence later-phase studies). 4. The present article briefly presents the background and theory that underpins both the data-dependent and -independent methods with the use of illustrative examples from the literature. In addition, the potential advantages and disadvantages of each method are discussed.

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“…In this section we illustrate the application of the methodology determining Bayesian optimal designs for the EMAX model and a compartment model, which are frequently used in pharmacology. Locally optimal designs for this model have been determined by numerous authors [see Atkinson et al (1993), Jones et al (1999), Dette et al (2008) and Dette et al (2010)] and we present some Bayesian optimal designs with respect to non-informative priors. For both models we assume that the response at experimental condition x ∈ X is normally distributed with mean µ(x, θ) and variance σ 2 (θ) = θ 3 > 0.…”

Section: Bayesian Optimal Designs For Nonlinear Regressionmentioning

confidence: 99%

Optimal designs for nonlinear regression models with respect to non-informative priors

Burghaus

Dette

2014

Journal of Statistical Planning and Inference

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In nonlinear regression models the Fisher information depends on the parameters of the model. Consequently, optimal designs maximizing some functional of the information matrix cannot be implemented directly but require some preliminary knowledge about the unknown parameters. Bayesian optimality criteria provide an attractive solution to this problem. These criteria depend sensitively on a reasonable specification of a prior distribution for the model parameters which might not be available in all applications. In this paper we investigate Bayesian optimality criteria with non-informative prior distributions. In particular, we study the Jeffreys and the Berger-Bernardo prior for which the corresponding optimality criteria are not necessarily concave. Several examples are investigated where optimal designs with respect to the new criteria are calculated and compared to Bayesian optimal designs based on a uniform and a functional uniform prior.

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Design of cross-over trials for pharmacokinetic studies

Cited by 16 publications

References 14 publications

Fisher information matrix for non‐linear mixed‐effects models: evaluation and application for optimal design of enoxaparin population pharmacokinetics

Fisher information matrix for non‐linear mixed‐effects models: evaluation and application for optimal design of enoxaparin population pharmacokinetics

Design Of Clinical Pharmacology Trials

Optimal designs for nonlinear regression models with respect to non-informative priors

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