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Back to the forum  Query: 2017-11-24 14:02 CET (UTC+1h)
 

Homecooked solution? [R for BE/BA]

posted by ElMaestro - Denmark, 2017-08-29 23:18  - Posting: # 17753
Views: 795

Hi all,

since no standard function exists which helps me along, I wrote the stuff below.
The structure of the input file is like in the appendices for Schütz, Labes, Fuglsang's paper in the AAPS Journal.
I am sure the code police will tell me this isn't efficient :-D because the same could be achieved with fewer code lines or some apply family calls. I am sure that is correct. Does the code achieve its goal properly, though? That is a much more interesting question. Not at all throughly tested and certainly not validated.
The purpose is to allow some CI info where we don't ask for "T/R" but perhaps for "R/T" or for "B/D" or "Foo/Bar", whatever.




Extract.Hot.Info=function(TreatmentRatio, Z)
{
  i=regexpr("/", TreatmentRatio)
  T1=substring(TreatmentRatio, 1, i-1)
  T2=substring(TreatmentRatio, i+1, nchar(TreatmentRatio)) ##heck!
  for (i in 1:nrow(Z))
   {
    if (as.character(Z[i,1]) == T1) E1=as.numeric(Z[i,2])
    if (as.character(Z[i,1]) == T2) E2=as.numeric(Z[i,2])
   }
  return(E1-E2)
}

Get.CI.BE=function(FName,  alpha, TreatmentRatio, Stage.Term=F)
{
  A=read.table(FName, header=T) ##Note Var is untransformed
  if (Stage.Term)
   M=lm(log(Var)~factor(Trt)+factor(Seq)+factor(Per)+factor(Subj)+factor(Stg), data=A)
  else
   M=lm(log(Var)~factor(Trt)+factor(Seq)+factor(Per)+factor(Subj), data=A)
 
  df=M$df.residual
  Sc=summary(M)$coefficients
  Trt.Lvls=levels(A$Trt)
  Eff.Lvls=rep(0, length(Trt.Lvls)) ##pseudo effect levels. Usefyl for for extr. of differences
 
  for (i in 1:nrow(Sc))
  for (j in 1:length(Trt.Lvls))
   {
    x=paste("factor(Trt)", as.character(Trt.Lvls[j]), sep="")
    if (rownames(Sc)[i]==x) { Eff.Lvls[j]=Sc[i,1]; SEdiffs=Sc[i,2]}
   }
  Z=data.frame(Trt.Lvls, Eff.Lvls)
  Diff.LogScale=Extract.Hot.Info(TreatmentRatio,Z)
  logL=Diff.LogScale-SEdiffs*qt(p=1-alpha, df=df)
  logU=Diff.LogScale+SEdiffs*qt(p=1-alpha, df=df)
  L=list(PE=exp(Diff.LogScale), Lower=exp(logL), Upper=exp(logU))
  return(L)
}
## call it like Get.CI.BE("Cojones.txt",  0.05, "T/R", F)


I could be wrong, but…


Best regards,
ElMaestro

No, I still don't believe much in the usefulness of IVIVCs for OIPs when it comes to picking candidate formulations for the next trial. This is not the same as saying I don't believe in IVIVCs.

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