PkModelRange-class

PkModelRange

pkModelRange

pkmodelrange

S4 class PkModel representing a pharmacokinetic model and its parameters and uncertainty of parameter choices by ranges


Find optimal microsampling designs for non-compartmental pharacokinetic analysis using a  general simulation methodology:
Algorithm III of Barnett, Helen, Helena Geys, Tom Jacobs, and Thomas Jaki. (2017) "Optimal Designs for Non-Compartmental
Analysis of Pharmacokinetic Studies. (currently unpublished)"
This methodology consist of (1) specifying a pharmacokinetic model
including variability among animals; (2) generating possible sampling times; (3)
evaluating performance of each time point choice on simulated data; (4)
generating possible schemes given a time point choice and additional constraints
and finally (5) evaluating scheme performance on simulated data. The default
settings differ from the article of Barnett and others, in the default pharmacokinetic model used and
the parameterization of variability among animals. Details can be found in the package vignette. A 'shiny'
web application is included, which guides users from model parametrization to
optimal microsampling scheme.

Adriaan Blommaert

microsamplingDesign

Finding Optimal Microsampling Designs for Non-Compartmental
Pharmacokinetic Analysis

Daan Seynaeve

Helen Barnett

Helena Geys

Tom Jacobs

Fetene Tekle

Thomas Jaki

PkModelRange-class function

<dl class="dl-horizontal">
<dt><code>modelFunction</code></dt><dd>a function of parameters and hyperparameters</dd></dl><dt><code>parameters</code></dt><dd>a data.frame of parameters of mean parameters as input to the modelFunction
with columns:
 <ul>
<li>parameter: parameter name for</li>
<li>explanation: optional explantion</li>
<li>value: fixed parameter value for <code><a rd-options="" href="/link/PkModel-class?package=microsamplingDesign&version=1.0.8" data-mini-rdoc="microsamplingDesign::PkModel-class">PkModel-class</a></code>,
 for <code><a rd-options="" href="/link/PkModel-class?package=microsamplingDesign&version=1.0.8" data-mini-rdoc="microsamplingDesign::PkModel-class">PkModel-class</a></code> split up between <code>minValue</code> and <code>maxValue</code></li>
<li>coeffVariation: the coefficient of variation ( standard deviation / mean ) specifying between-subject variation,
 for <code><a rd-options="" href="/link/PkModelRange-class?package=microsamplingDesign&version=1.0.8" data-mini-rdoc="microsamplingDesign::PkModelRange-class">PkModelRange-class</a></code> split up into <code>minValue</code> and <code>maxValue</code></li>
</ul></dd><dt><code>correlationMatrix</code></dt><dd>correlation matrix of parameters at the log-scale</dd><dt><code>coeffVariationError</code></dt><dd>the coefficient of variation for residual normally distributed error, 
 for <code><a rd-options="" href="/link/PkModelRange-class?package=microsamplingDesign&version=1.0.8" data-mini-rdoc="microsamplingDesign::PkModelRange-class">PkModelRange-class</a></code> split up into <code>minCoeffVariationError</code> and <code>maxCoeffVariationError</code> 
@slot dosingInfo data frame containing:
 <ul>
<li>time numeric, times when a dose is administered</li>
<li>dose numeric, with an amount of dose</li>
</ul></dd>

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S4 class PkModel representing a pharmacokinetic model and its parameters and uncertainty of parameter choices by ranges — PkModelRange-class

S4 class PkModel representing a pharmacokinetic model and its parameters and uncertainty of parameter choices by ranges

PkModelRange-class: S4 class PkModel representing a pharmacokinetic model and its parameters and uncertainty of parameter choices by ranges

Description

Arguments

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