ptest.ssd {PK}R Documentation

Comparing AUCs in Serial Sampling Designs

Description

Comparing two AUCs assessed in a serial sampling design with a permutation test.

Usage

ptest.ssd(conc, time, group, alternative=c("two.sided", "less", "greater"), 
          nsample=1000, data)        

Arguments

conc Levels of concentrations.
time Time points of concentration assessment.
group Grouping variable with two levels.
alternative Character string specifying the alternative hypothesis (default=two.sided).
nsample Number of resampling iterations (default=1000).
data Optional data frame containing variables named as conc, time and group.

Details

Comparing two AUCs assessed in a serial sampling design with a permutation test using a Monte Carlo approximation for the permutation distribution. The difference between two AUCs is used as the test statistic and not the z-statistic as suggested in Bailer and Ruberg (1995). In a serial sampling design only one measurement is available per analysis subject at a specific time point.

Value

A data frame consisting of:

statistic estimate for the difference.
p.value p-value.

Note

Records including missing values are omitted.

Author(s)

Martin J. Wolfsegger

References

Bailer A. J. (1988). Testing for the equality of area under the curves when using destructive measurement techniques. Journal of Pharmacokinetics and Biopharmaceutics, 16(3):303-309.

Bailer J. A. and Ruberg S. J. (1995). Randomization tests for assessing the equality of area under curves for studies using destructive sampling. Journal of Applied Toxicology, 16(5):391-395.

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, eqv.ssd.

Examples

## example from Nedelman et al. (1995)
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)
m.100 <- c(1910, 2550, 4230, 5110, 7490, 13500, 4380, 5380, 260, 326)
f.100 <- c(2790, 3280, 4980, 7550, 5500, 6650, 2250, 3220, 213, 636)
time <- c(1,1,2,2,4,4,8,8,24,24)

data <- data.frame(conc=c(m.030, f.030, m.100, f.100), 
                   time=rep(time, 4), 
                   sex=c(rep("m", 10), rep("f", 10), rep("m", 10), rep("f", 10)),
                   dose=c(rep(30, 20), rep(100, 20)))

data$concadj <- data$conc / data$dose

set.seed(523423)
ptest.ssd(conc=data$concadj, time=data$time, group=data$dose)

## example from Bailer (1988)
time <- c(rep(0, 4), rep(1.5, 4), rep(3, 4), rep(5, 4), rep(8, 4))
grp1 <- c(0.0658, 0.0320, 0.0338, 0.0438, 0.0059, 0.0030, 0.0084,
          0.0080, 0.0000, 0.0017, 0.0028, 0.0055, 0.0000, 0.0037,
          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000)

grp2 <- c(0.2287, 0.3824, 0.2402, 0.2373, 0.1252, 0.0446, 0.0638,
          0.0511, 0.0182, 0.0000, 0.0117, 0.0126, 0.0000, 0.0440,
          0.0039, 0.0040, 0.0000, 0.0000, 0.0000, 0.0000)

grp3 <- c(0.4285, 0.5180, 0.3690, 0.5428, 0.0983, 0.0928, 0.1128,
          0.1157, 0.0234, 0.0311, 0.0344, 0.0349, 0.0032, 0.0052,
          0.0049, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000)

data <- data.frame(conc=c(grp1, grp2, grp3), time=rep(time, 3),
                   group=c(rep(1, length(grp1)), rep(2, length(grp2)), 
                   rep(3, length(grp3))))

## function call with data frame with subsequent multiple comparisons
set.seed(62432) 
pvalue <- rep(NA, 3)
pvalue[1] <- ptest.ssd(data=subset(data, group==1 | group==2), nsample=100)$p.value
pvalue[2] <- ptest.ssd(data=subset(data, group==1 | group==3), nsample=100)$p.value
pvalue[3] <- ptest.ssd(data=subset(data, group==2 | group==3), nsample=100)$p.value

pvalue
p.adjust(pvalue, method="holm")

[Package PK version 1.00 Index]