Optimal design for population pk/pd models

Leonov, Sergei; Aliev, Alexander

doi:10.2478/v10127-012-0012-1

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…The explicit formula for FIM(β, ξi) using the block diagonal form is given in the Appendix. More information about the derivation of the FIM or other approximations is reported in [27,28,30,[32][33][34].…”

Section: Statistical Methods For Design In Nonlinear Mixed-effects Momentioning

confidence: 99%

Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies

Nyberg

Bazzoli

Ogungbenro

et al. 2014

Brit J Clinical Pharma

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Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.

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Section: Statistical Methods For Design In Nonlinear Mixed-effects Momentioning

confidence: 99%

Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies

Nyberg

Bazzoli

Ogungbenro

et al. 2014

Brit J Clinical Pharma

Self Cite

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“…An approximation of the expected M F has been proposed for NLMEM, using first order linearisation of the model around the random effect expectation (Mentré et al, 1997;Retout et al, 2002;Bazzoli et al, 2009). This approach has been implemented in several software programs (Bazzoli et al, 2010;Leonov and Aliev, 2012;Gueorguieva et al, 2007;Nyberg et al, 2012) such as PFIM (INSERM, University Paris Diderot), POPED (University of Uppsala), POPDES (University of Manchester), and POPT (University of Otago), frequently used to design new studies in academia as well as in pharmaceutical companies (Mentré et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature

Nguyễn

2014

Computational Statistics & Data Analysis

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. Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature. Computational Statistics and Data Analysis, Elsevier, 2014, 80, pp.57 -69 AbstractNonlinear mixed effect models (NLMEM) are used in model-based drug development to analyse longitudinal data. To design these studies, the use of the expected Fisher information matrix (M F ) is a good alternative to clinical trial simulation. Presently, M F in NLMEM is mostly evaluated with first-order linearisation. The adequacy of this approximation is, however, influenced by model nonlinearity. Alternatives for the evaluation of M F without linearisation are proposed, based on Gaussian quadratures. The M F , expressed as the expectation of the derivatives of the log-likelihood, can be obtained by stochastic integration. The likelihood for each simulated vector of observations is approximated by Gaussian quadrature centred at 0 (standard quadrature) or at the simulated random effects (adaptive quadrature). These approaches have been implemented in R. Their relevance was compared with clinical trial simulation and linearisation, using dose-response models, with various nonlinearity levels and different number of doses per patient. When the nonlinearity was mild, three approaches based on M F gave correct predictions of standard errors, when compared with the simulation. When the nonlinearity increased, linearisation correctly predicted standard errors of fixed effects, but over-predicted, with sparse designs, standard errors of some variability terms. Meanwhile, quadrature approaches gave correct predictions of standard errors overall, but standard Gaussian quadrature was very time-consuming when there were more than two random effects. To conclude, adaptive Gaussian quadrature is a relevant alternative for the evaluation of M F for models with stronger nonlinearity, while being more computationally efficient than standard quadrature.

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Optimal design for population pk/pd models

Cited by 2 publications

References 14 publications

Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies

Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies

Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature

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