theo.saemix: Pharmacokinetics of theophylline

In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Pharmacokinetics of theophylline

Description

The theo.saemix data frame has 132 rows and 6 columns of data from an experiment on the pharmacokinetics of theophylline. A column with gender was added to the original data for demo purposes, and contains simulated data.

Usage

theo.saemix

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 by time at which the observation was made.

Dose

dose of theophylline administered orally to the subject (mg/kg).

Time

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

Concentration

theophylline concentration in the sample (mg/L).

Weight

weight of the subject (kg).

Sex

gender (simulated, 0=male, 1=female

Details

Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model.

These data are also available in the library datasets under the name Theoph in a slightly modified format and including the data at time 0. Here, we use the file in the format provided in the NONMEM installation path (see the User Guide for that software for details).

Source

AJ Boeckmann, LB Sheiner, SL Beal (1994), NONMEM Users Guide: Part V, NONMEM Project Group, University of California, San Francisco.

References

M Davidian, DM Giltinan (1995) Nonlinear Models for Repeated Measurement Data, Chapman & Hall (section 5.5, p. 145 and section 6.6, p. 176)

JC Pinheiro, DM Bates (2000) Mixed-effects Models in S and S-PLUS, Springer (Appendix A.29)

Examples

data(theo.saemix)

#Plotting the theophylline data
plot(Concentration~Time,data=theo.saemix,xlab="Time after dose (hr)",
ylab="Theophylline concentration (mg/L)")

saemix.data<-saemixData(name.data=theo.saemix,header=TRUE,sep=" ",na=NA, 
  name.group=c("Id"),name.predictors=c("Dose","Time"),
  name.response=c("Concentration"),name.covariates=c("Weight","Sex"),
  units=list(x="hr",y="mg/L",covariates=c("kg","-")), name.X="Time")
  model1cpt<-function(psi,id,xidep) { 
    dose<-xidep[,1]
    tim<-xidep[,2]  
    ka<-psi[id,1]
    V<-psi[id,2]
    CL<-psi[id,3]
    k<-CL/V
    ypred<-dose*ka/(V*(ka-k))*(exp(-k*tim)-exp(-ka*tim))
    return(ypred)
    }
# Default model, no covariate
saemix.model<-saemixModel(model=model1cpt,
       description="One-compartment model with first-order absorption",
       psi0=matrix(c(1.,20,0.5,0.1,0,-0.01),ncol=3,byrow=TRUE, 
       dimnames=list(NULL, c("ka","V","CL"))),transform.par=c(1,1,1))
 # Note: remove the options save=FALSE and save.graphs=FALSE 
 # to save the results and graphs
 saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE, displayProgress=FALSE)
 
 # Not run (strict time constraints for CRAN)
 saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)
 
 # Model with covariates
 saemix.model<-saemixModel(model=model1cpt,
      description="One-compartment model with first-order absorption",
      psi0=matrix(c(1.,20,0.5,0.1,0,-0.01),ncol=3,byrow=TRUE, 
      dimnames=list(NULL, c("ka","V","CL"))),transform.par=c(1,1,1), 
      covariate.model=matrix(c(0,0,1,0,0,0),ncol=3,byrow=TRUE),fixed.estim=c(1,1,1),
      covariance.model=matrix(c(1,0,0,0,1,1,0,1,1),ncol=3,byrow=TRUE),
      omega.init=matrix(c(1,0,0,0,1,0,0,0,1),ncol=3,byrow=TRUE),error.model="combined")
      
 saemix.options<-list(seed=39546,save=FALSE,save.graphs=FALSE,displayProgress=FALSE)
 
 # Not run (strict time constraints for CRAN)
 saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)
 
 

saemix documentation built on July 9, 2023, 7:43 p.m.