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
It performs bioequivalency tests for several variables of a 2x2 study in a data file.
Kyun-Seop Bae firstname.lastname@example.org
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
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# 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 test
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