Bioequivalence and Bioavailability Forum

Dear Mittyri,

❝ I mean the R code for CVwr estimation and outliers detection in Reference dataset according to ... EMA GL:

since I have to do my daily work with SAS I don't have an R solution for doing this out of the box.

But I think it should not that complicated to do the estimation of CVwR and this could be programmed even by a novice in R.
Part I: Estimation of CVwR

• Make a subset of your data.frame containing only the PK metrics under R(eference)
  dtaR <- subset(data, tmt=="R")
dtaR$period <- as.factor(dtaR$period)
dtaR$subject<- as.factor(dtaR$subject)
dtaR$sequence<- as.factor(dtaR$sequence)
  
• Fit an model containing period sequence and subject within sequence
  muddleR <- lm(log(PKmetric) ~ period + sequence + subject%in%sequence, data=dtaR)
  or according to our Captn's preferred implementation simply
  muddleR <- lm(log(PKmetric) ~ period + sequence + subject, data=dtaR)
  
• Extract the error mean square from that model and you calculate the CV from it
  msewR <- sum(muddleR$residuals^2)/muddleR$df.residual
CVwR  <- sqrt(exp(msewR)-1)*100 # in %
  

Part II - ODA
It is another story and is a grey zone. An outlier test is not expected. So simply create a boxplot from the residuals, cross your fingers and hope that not suspected "outliers" are visible:

boxplot(muddleR$residuals)

This is of course only a rough description just in this moment came to my mind. Intended to give you a starting point. Needs to be tested and validated before going in production. Needs also to make a nice printout of the results. It's up to you.