Optimal Microsampling design non-compartmental analysis

In microsamplingDesign: Finding Optimal Microsampling Designs for Non-Compartmental Pharmacokinetic Analysis

#library(Pkdesign)
#paramList                      <-  list()
#model                          <-  getExamplePkModel() 
#paramList$pkParameters         <-  getParameters( model )
#paramList$kineticsPlot         <-  plotMMKinetics( model , doseRange = c( 0 , 10 ) , concentrationRange = c(0,5) )
#settings                       <-  list( NsimTimePoints = 100 ,  nSimSchemes = 1000 , nSubjectsForTimePoints = 3 , weightAUC = 50 , weightCmax = 50  )
#paramList$settings             <-  settings 
#paramList$optimalTimePoints    <-  "{ 1, 2 , 3.5 , 8}" 
#paramList$optimalScheme        <-  getExampleSetOfSchemes()[ ,, 1]

Pharmacokinetic model

Input parameters

#paramList   <-  params$paramList
knitr::kable( modelParametersTable()  )

Absorption and clearance kinetics

plot( MMKineticsPlot() )

Dosing info

knitr::kable( dosingInfoFormat() )

Sample Curves

plot( samplePlot() )

Settings

Constraints on time points

tableToPrint          <-  timeConstraintsFormat()
knitr::kable( tableToPrint )

Constraints on schemes

schemeConstraintTable   <-  schemeConstraintsFormat()
knitr::kable( schemeConstraintTable )

Other settings

settings           <-  list(
  nSamplesForTimePoints          =  input$nSamplesForTimePoints ,
  nSubjectsPerSchemeTimePoints   =  input$nSubjectsPerSchemeTimePoints ,
  nSamplesForSchemes             =  input$nSamplesForSchemes ,
  weightAUC                      =  input$wAUC ,
  weightCMax                     =  input$wCmax 
  )
settingsTable      <-  data.frame(  Setting = names( settings ) , Value = unlist( settings ) )
rownames( settingsTable ) <-  NULL
knitr::kable( settingsTable )

Optimal design

Time points

knitr::kable( selectedTimePointOption() )

Scheme

knitr::kable( selectedScheme() )

microsamplingDesign documentation built on Oct. 13, 2021, 5:10 p.m.