Simulated data for the computation of normalised prediction distribution errors

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Description

The simtheopp dataset contains 100 simulations using the design of dataset theopp. These simulations are used to compute npde. The control file used to perform the simulations can be found in the subdirectory 'doc' within the library npde.

Usage

1

Format

This data frame contains the following columns:

ID

an ordered factor with levels 1, ..., 12 identifying the subject on whom the observation was made. The ordering is first by simulation then by increasing time.

xsim

time since drug administration when the sample was drawn (hr).

ysim

simulated theophylline concentration (mg/L).

Details

See theopp for a description of the original dataset.

The simulated data was obtained using the software NONMEM. A one-compartment model was fit to the data. An exponential interindividual variability was assumed for the three parameters (absorption rate constant ka, volume of distribution V and clearance CL) and a combined additive and proportional residual error model was usd. The estimated parameters were then used to simulate 100 datasets with the same structure as the original dataset. Thus, for each observation in the original dataset, the simulated dataset contains 100 simulations under the model used for the estimation.

This dataset is provided so that users can figure out what type of data is needed for the computation of prediction distribution errors. More information can be found in the User Guide distributed along with this package, which contains a run-through of the theophylline example.

Source

Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994), NONMEM Users Guide: Part V, NONMEM Project Group, University of California, San Francisco.

See Also

theopp

Examples

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data(simtheopp)

# Plotting the simulated data for subject 1 in the first simulation
plot(ysim[2:12]~xsim[2:12],data=simtheopp,xlab="Time after dose (hr)",
ylab="Theophylline concentration (mg/L)",type="l",
main="Example of simulated data for subject 1")

# Plotting a 90% prediction interval for the observations in theopp
# using the simulated data in simtheopp
# note : differences in doses between subjects are not taken into account
data(theopp)
xpl<-c(0,0.25,0.5,1,2,3.5,5,7,9,12,24)
xpl1<-list(c(0,0.1),c(0.2,0.4),c(0.5,0.65),c(0.9,1.2),c(1.9,2.2),c(3.4,4),
c(4.9,5.2),c(6.9,7.2),c(8.8,9.4),c(11.5,12.2),c(23.7,24.7))

ypl<-cbind(xpl=xpl,binf=xpl,median=xpl,bsup=xpl)
for(i in 1:(length(xpl))) {
  vec<-simtheopp$ysim[simtheopp$xsim>=xpl1[[i]][1] &simtheopp$xsim<=xpl1[[i]][2]] 
  ypl[i,2:4]<-quantile(vec,c(0.05,0.5,0.95))
}
plot(Conc~Time,data=theopp,xlab="Time after dose (hr)",
ylab="Theophylline concentration (mg/L)")
lines(ypl[,1],ypl[,3],lwd=2)
lines(ypl[,1],ypl[,2],lty=2)
lines(ypl[,1],ypl[,4],lty=2)