Pharmacokinetics of Theophylline
Theoph data frame has 132 rows and 5 columns of data from
an experiment on the pharmacokinetics of theophylline.
An object of class
c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame")
containing the following columns:
an ordered factor with levels
12identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.
weight of the subject (kg).
dose of theophylline administered orally to the subject (mg/kg).
time since drug administration when the sample was drawn (hr).
theophylline concentration in the sample (mg/L).
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,
for which a self-starting model function,
SSfol, is available.
This dataset was originally part of package
nlme, and that has
methods (including for
Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994), NONMEM Users Guide: Part V, NONMEM Project Group, University of California, San Francisco.
Davidian, M. and Giltinan, D. M. (1995) Nonlinear Models for Repeated Measurement Data, Chapman & Hall (section 5.5, p.\sspace145 and section 6.6, p.\sspace176)
Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer (Appendix A.29)
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require(stats); require(graphics) coplot(conc ~ Time | Subject, data = Theoph, show.given = FALSE) Theoph.4 <- subset(Theoph, Subject == 4) fm1 <- nls(conc ~ SSfol(Dose, Time, lKe, lKa, lCl), data = Theoph.4) summary(fm1) plot(conc ~ Time, data = Theoph.4, xlab = "Time since drug administration (hr)", ylab = "Theophylline concentration (mg/L)", main = "Observed concentrations and fitted model", sub = "Theophylline data - Subject 4 only", las = 1, col = 4) xvals <- seq(0, par("usr"), length.out = 55) lines(xvals, predict(fm1, newdata = list(Time = xvals)), col = 4)
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