marzo {jocre}

Bioequivalence study of ticlopidine hydrochloride

Description

Pharmacokinetic data from a study on the bioequivalence of a test and a reference formulation of ticlopidine hydrochloride in 24 healthy male volunteers, using a randomised crossover design (Marzo et al. 2002).

Usage

data("marzo")

Format

A data frame with 24 observations on the following 8 variables.

Volunteer

A numeric vector giving the volunteer ID.

Sequence

A factor with levels RT and TR specifying the sequence a volunteer was randomised to (R=reference, T=test).

Cmax_T

A numeric vector of the maximum concentration (Cmax) with the test product.

Cmax_R

A numeric vector of the maximum concentration (Cmax) with the reference product.

AUC_T

A numeric vector of the area under the concentration-time curve (AUC) from zero to the last observed time point with the test product.

AUC_R

A numeric vector of the area under the concentration-time curve (AUC) from zero to the last observed time point with the reference product.

AUCinf_T

A numeric vector of the area under the concentration-time curve AUC) from zero to infinity with the test product.

AUCinf_R

A numeric vector of the area under the concentration-time curve AUC) from zero to infinity with the reference product.

Details

The pharmacokinetic parameters (Cmax and AUC) were calculated using a non-compartmental approach. The data were taken from Tables I and II of Marzo et al. (2002).

Source

Antonio Marzo, Lorenzo Dal Bo, Antonio Rusca, Pierangelo Zini (2002) Bioequivalence of ticlopidine hydrochloride administered in single dose to healthy volunteers. Pharmacological Research, 46(5), 401–407.

References

Philip Pallmann & Thomas Jaki (2017) Simultaneous confidence regions and intervals for multivariate bioequivalence. Submitted to Statistics in Medicine.

Examples

data(marzo)

## An example analysis of Cmax assuming log-normality

# Difference of log(Cmax)
marzo$deltalogCmax <- log(marzo$Cmax_T) - log(marzo$Cmax_R)

# Estimated mean treatment effect with SE 
mean(marzo$deltalogCmax)
sd(marzo$deltalogCmax) / sqrt(nrow(marzo))

# Two one-sided test (TOST) p-values
t.test(x=marzo$deltalogCmax, alternative="less", mu=log(1.25))$p.value
t.test(x=marzo$deltalogCmax, alternative="greater", mu=log(0.80))$p.value

# 90% TOST confidence interval
t.test(x=marzo$deltalogCmax, conf.level=0.9)$conf.int[1:2]