auc {PK}R Documentation

Estimation of the Area Under the Concentration Time Curve in Complete and Incomplete Data Designs

Description

Non-compartmental estimation of area under the concentration time curve (AUC) and area under the first moment curve (AUMC).

Usage

auc(conc, time, exact=NA, n.interpolate=2, n.tail=3, 
     design=c('ssd','batch','complete'))

Arguments

conc Levels of concentrations. For batch designs a list is required, while a vector is expected otherwise. Multiple measurements per time point are expected to be adjacent to each other.
time Time points of concentration assessment. For batch designs a list of batches containing time points for each batch is required. Otherwise a vector is required. One time point for each concentration measured needs to be specified.
exact Time point for linear interpolation/extrapolation (default=NA). Only implemented for a complete data design.
n.interpolate Number of last data points used for linear interpolation/extrapolation (default=2). Only implemented for a complete data design.
n.tail Number of last data points used for tail area correction (default=3).
design A character string indicating the type of design used. Possible values are ssd (the default) for a serial sampling design, batch for a batch design and complete for a complete data design.

Details

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, batch and complete data 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. In a complete data design all measurements are taken for all subjects at all time points. The AUC (from 0 to the last time point) is calculated using the linear trapezoidal rule on the arithmetic means at the different time points.

The total area under the concentration curve (AUC 0-infinity) and total area under the first moment curve (AUMC 0-infinity) is computed using a the tail area correction calculated similar as suggested by Perrier and Gibaldi (Appendix D, 1982).

The linearly interpolated area under the concentration time curve (AUC 0-exact) and linearly interpolated area under the moment curve (AUMC 0-exact) is also estimated for complete data designs. The time point (exact) for linear interpolation/extrapolation must be after the second to last time point of concentration assessments. n.interpolate specifies the number of timepoints to be used for the linear interpolation.

Equal sample size per time point is required for batch designs.

Value

An object of the class PK including AUC and AUMC estimates.

Note

This is a wrapper function for auc.complete, auc.batch and auc.ssd. See the documentation of these functions for more details.

Author(s)

Thomas Jaki

References

Cawello W. (2003). Parameters for Compartment-free Pharmacokinetics. Standardisation of Study Design, Data Analysis and Reporting. Shaker Verlag, Aachen.

Gibaldi M. and Perrier D. (1982). Pharmacokinetics. 2nd Edition. Marcel Dekker, New York and Basel.

Holder D. J., Hsuan F., Dixit R. and Soper K. (1999). A method for estimating and testing area under the curve in serial sacrifice, batch, and complete data designs. Journal of Biopharmaceutical Statistics, 9(3):451-464.

Jaki T. and Wolfsegger M. J. (In press). A theoretical framework for estimation of AUCs in complete and incomplete sampling designs. Statistics in Biopharmaceutical Research.

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.

See Also

auc.ci, auc.test.

Examples

## example for a complete data design from Cawello W. (2003, page 70 and 74)
time <- c(0, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5, 6)
conc <- c(0, 5.67, 20.6, 28.7, 22.5, 17.4, 17.7, 13.4, 11.0, 8.23, 5.14, 2.84) 

# using a vector for both concentration and time
auc(conc=conc, time=time, exact=7, n.tail=4, design='complete')

## a batch design from Jaki and Wolfsegger (in press), originally in Holder et al. (1999).
conc <- list(batch1=c(0,0,0,1.75,2.2,1.58,4.63,2.99,1.52), 
             batch2=c(3.03,1.98,2.22,3.34,1.3,1.22),
             batch3=c(3.54,2.84,2.55,0.3,0.0421,0.231))
time <- list(batch1=c(0,0,0,1,1,1,6,6,6), batch2=c(2,2,2,10,10,10), 
             batch3=c(4,4,4,24,24,24))
auc(conc, time, n.tail=3, design='batch')

## example for a serial sampling design 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(conc=m.030, time=time, n.tail=3, design='ssd')
auc(conc=f.030, time=time, n.tail=3, design='ssd')


[Package PK version 1.00 Index]