Bioequivalence Study Data Analysis
Analyze bioequivalence study data with industrial strength. Sample size could be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4 design, Balaam design, Two-sequence dual design, and William design.
Basic assumption is that the variable is distributed as a log-normal distribution. This is SAS PROC GLM style. If you want PROC MIXED style, use nlme::lme.
It performs bioequivalency tests for several variables of a 2x2 study in a data file.
Kyun-Seop Bae k@acr.kr
Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. 3rd ed. (2009, ISBN:978-1-58488-668-6)
Hauschke D, Steinijans V, Pigeot I. Bioequivalence Studies in Drug Development. (2007, ISBN:978-0-470-09475-4)
Diletti E, Hauschke D, Steinijans VW. Sample size determination for bioequivalence assessment by means of confidence intervals. Int J Clinical Pharmacol Ther Tox. 1991;29(1):1-8
# write.csv(NCAResult4BE, "temp.csv", quote=FALSE, row.names=FALSE)
# be2x2("temp.csv", c("AUClast", "Cmax", "Tmax"))
## 'nlme' or SAS PROC MIXED is preferred for the confidence interval
## SAS PROC MIXED equivalent
# require(nlme)
# r2 = lme(log(Cmax) ~ GRP + PRD + TRT, random=~1|SUBJ, data=BEdata)
# summary(r2)
# VarCorr(r2)
# ci = intervals(r2, 0.90) ; ci
# exp(ci$fixed["TRTT",])
## SAS PROC GLM equivalent
# require(sasLM) # includes 'BEdata' which is a real dataset
# BEdata = af(BEdata, c("SEQ", "SUBJ", "PRD", "TRT")) # Columns as factor
# formula1 = log(CMAX) ~ SEQ/SUBJ + PRD + TRT # Model
# GLM(formula1, BEdata) # ANOVA tables of Type I, II, III SS
# T3MS(formula1, BEdata) # EMS table
# T3test(formula1, BEdata, Error="SEQ:SUBJ") # Hypothesis testPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.