Generalized Nonlinear Models for Pharmacokinetic Data

Lindsey, J. K.; Byrom, Bill; Wang, Jihui; Jarvis, Philip; Jones, B.

doi:10.1111/j.0006-341x.2000.00081.x

Cited by 60 publications

(29 citation statements)

References 10 publications

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“…Lindsey et al [17,18] have recently pointed out that the log-normal assumption is not necessarily appropriate and other skewed distributions such as Weibull and gamma distributions may be more appropriate. The proposed approach is easily applicable to the cases where other skewed distributions are assumed.…”

Section: Discussionmentioning

confidence: 99%

A simulation‐based confidence band for drug concentrations in blood: an application to a clinical phase I trial

Yafune

Ishiguro

2003

Statistics in Medicine

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Since drug concentrations in blood are usually related to the effectiveness and toxicity, a confidence band for the drug concentrations gives useful information for the treatment. This paper proposes a simulation-based approach for constructing confidence bands for drug concentrations in blood. The confidence band covers the whole profile of the drug concentrations with a specified probability. The proposed approach presupposes that actual concentrations of a subject fluctuate randomly around a curve specified by an appropriate pharmacokinetic model and the subject-specific pharmacokinetic parameters. The confidence band is constructed by taking into account both the random fluctuations of the actual concentrations and the statistical uncertainty of the parameter estimates. The proposed approach is applied to a simulation study and an actual phase I trial.

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Section: Discussionmentioning

confidence: 99%

A simulation‐based confidence band for drug concentrations in blood: an application to a clinical phase I trial

Yafune

Ishiguro

2003

Statistics in Medicine

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“…An alternative, with constant coefficient of variation, is log y(t ij ) = log µ(t ij ) + (t ij ) with (t ij ) ∼ iid N(0, σ 2 ). Lindsey et al (2000) have suggested combining a compartmental model mean with a gamma error model.…”

Section: Compartmental Modelsmentioning

confidence: 99%

Gamma Generalized Linear Models for Pharmacokinetic Data

Salway

Wakefield

2008

Biometrics

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Summary This article considers the modeling of single‐dose pharmacokinetic data. Traditionally, so‐called compartmental models have been used to analyze such data. Unfortunately, the mean function of such models are sums of exponentials for which inference and computation may not be straightforward. We present an alternative to these models based on generalized linear models, for which desirable statistical properties exist, with a logarithmic link and gamma distribution. The latter has a constant coefficient of variation, which is often appropriate for pharmacokinetic data. Inference is convenient from either a likelihood or a Bayesian perspective. We consider models for both single and multiple individuals, the latter via generalized linear mixed models. For single individuals, Bayesian computation may be carried out with recourse to simulation. We describe a rejection algorithm that, unlike Markov chain Monte Carlo, produces independent samples from the posterior and allows straightforward calculation of Bayes factors for model comparison. We also illustrate how prior distributions may be specified in terms of model‐free pharmacokinetic parameters of interest. The methods are applied to data from 12 individuals following administration of the antiasthmatic agent theophylline.

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“…While generalized nonlinear models were not practical due to the lack of available computer software in the past, several computational options now exist to handle the requirements of generalized nonlinear models [see for example PROC NLMIXED in the SAS software package (SAS, 1999), or the GNLM module for the R statistical package (Lindsey, et al 2000]. Typically, these software solutions can estimate parameter values, as well as specified functions of the parameters.…”

Section: Generalized Nonlinear Models Analysismentioning

confidence: 99%

Comparing Estimation Procedures for Dose-Response Functions

Price¹,

Shafii²,

Newman³

et al. 2003

Conference on Applied Statistics in Agriculture

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The dose-response design is often used in agricultural research when it is necessary to measure a biological response at various levels of an experimental factor. This type of problem is common in chemical and pesticide research, however, it can also occur in other disciplines such as plant, animal, soil, and environmental sciences. While the analysis of dose-response data usually involves fitting a regression curve, the primary objective often centers on the estimation of doserelated percentiles such as the LDso or LC so . These measures are useful for comparing the relative efficacy of various treatments, however, the estimation of the specified percentiles is not always straightforward. Traditional methodology has relied on inverted solutions or asymptotic theory for statistical inference. More recently, computer intensive methods have been used to model dose-response relationships and can be more appropriate than traditional methods in some situations. This paper examines both the traditional and modem approaches to estimating doseresponse functions as they apply to binomial data. The techniques will be demonstrated using mortality data collected on black vine weevil eggs exposed to an organic pesticide treatment.

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Generalized Nonlinear Models for Pharmacokinetic Data

Cited by 60 publications

References 10 publications

A simulation‐based confidence band for drug concentrations in blood: an application to a clinical phase I trial

A simulation‐based confidence band for drug concentrations in blood: an application to a clinical phase I trial

Gamma Generalized Linear Models for Pharmacokinetic Data

Comparing Estimation Procedures for Dose-Response Functions

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