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Article details
Title
A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies
Published in
Pharmaceutical Statistics, March 2012
DOI 10.1002/pst.1509
Pubmed ID
Authors
Abstract

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 3 43%
Student > Ph. D. Student 2 29%
Researcher 1 14%
Unknown 1 14%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 4 57%
Chemical Engineering 1 14%
Biochemistry, Genetics and Molecular Biology 1 14%
Unknown 1 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 March 2012.
All research outputs
#22,036,945
of 24,585,562 outputs
Outputs from Pharmaceutical Statistics
#488
of 566 outputs
Outputs of similar age
#145,360
of 160,118 outputs
Outputs of similar age from Pharmaceutical Statistics
#2
of 3 outputs
Altmetric has tracked 24,585,562 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 566 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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