auc.ssd {PK} | R Documentation |
Non-compartmental estimation of area under the concentration time curve (AUC) and area under the first moment curve (AUMC) in serial sampling designs.
auc.ssd(conc, time, exact=NA, n.interpolate=2, n.tail=3)
conc |
A vector of concentration levels. |
time |
A vector of time points of concentration assessment. One time point for each concentration measured needs to be specified. |
exact |
Time point for linear interpolation/extrapolation (default=NA ). Currently not implemented. |
n.interpolate |
Number of last data points used for linear interpolation/extrapolation (default=2 ). Currently not implemented. |
n.tail |
Number of last data points used for tail area estimation (default=3 ). |
Estimation of the area under the concentration time curve (AUC 0-tlast) and area under the first moment curve (AUMC 0-tlast) for serial sampling designs. In a serial sampling design only one measurement is available per subject at a specific time point, while in a batch design multiple time points are measured for each subject.
Estimation of the linearly interpolated area under the concentration time curve (AUC 0-exact) and linearly interpolated area under the moment curve (AUMC 0-exact). The time point for linear interpolation/extrapolation must be after the last but one time point of concentration assessments.
Estimation of the total area under the concentration curve (AUC 0-infinity) and total area under the first moment curve (AUMC 0-infinity) by using a tail area correction similar as suggested by Perrier and Gibaldi (Appendix D, 1982).
An object of the class PK including AUC and AUMC estimates.
Thomas Jaki
Gibaldi M. and Perrier D. (1982). Pharmacokinetics. 2nd Edition. Marcel Dekker, New York and Basel.
Nedelman J. R., Gibiansky E. and Lau D. T. W. (1995). Applying Bailer's method for AUC confidence intervals to sparse sampling. Pharmaceutical Research, 12(1):124-128.
## example from Nedelman et al. (1995) time <- c(1, 1, 2, 2, 4, 4, 8, 8, 24, 24) m.030 <- c(391, 396, 649, 1990, 3290, 3820, 844, 1650, 75.7, 288) f.030 <- c(353, 384, 625, 1410, 1020, 1500, 933, 1030, 0, 80.5) auc.ssd(conc=m.030, time=time, n.tail=3) auc.ssd(conc=f.030, time=time, n.tail=3)