Bioequivalence and Bioavailability Forum • Group effects EMA

Group effects EMA [Two-Stage / GS Designs]

posted by mittyri – Russia, 2016-10-07 17:06 (2751 d 03:21 ago) – Posting: # 16706
Views: 35,740

Hi VStus & ElMaestro!

This is really funny, we had 2 last days discussions with Helmut regarding Group effect during the workshop

Yes, in case of just adding a Group you do not see any changes (Fixed: sequence, period, treatment, group, subject(sequence))
But in case of Period %in% Group the situation is not the same!!

a little code for discussion:

# the original code is from this perfect paper:

# http://link.springer.com/article/10.1208/s12248-014-9661-0

Analyse222BE <- function (data, alpha=0.05, Group = FALSE) {

  

  data$Subj <- factor(data$Subj) # Subject

  data$Per  <- factor(data$Per)  # Period

  data$Seq  <- factor(data$Seq)  # Sequence

  data$Trt  <- factor(data$Trt)  # Treatment

  # be explicite

  ow <- options()

  options(contrasts=c("contr.treatment","contr.poly"))

  

  if(!Group) {

    muddle <- lm(log(Var)~Trt+Per+Seq+Subj, data=data)

  }  else {

    muddle <- lm(log(Var)~Trt+Per+Seq+Subj+

                          Group+Per:Group, data=data)

  }

  # in the standard contrasts option "contr.treatment" 

  # the coefficient TrtT is the difference T-R since TrtR is set to zero

  lnPE     <- coef(muddle)["TrtT"]


  lnCI     <- confint(muddle,c("TrtT"), level=1-2*alpha)

  typeI    <- anova(muddle)

  names(typeI)[5]   <- "Pr(>F)"



  # no need for the next

  mse      <- summary(muddle)$sigma^2

  # another possibility:

  #mse <- typeI["Residuals","Mean Sq]


  #df       <- df.residual(muddle)

  

  # back transformation to the original domain

  CV <- 100*sqrt(exp(mse)-1)

  PE <- exp(lnPE)

  CI <- exp(lnCI)

  # output

  

  cat(sep,"\n")

  options(digits=8)

  cat("Type I sum of squares: ")

  print(typeI)



  cat("\nBack-transformed PE and ",100*(1-2*alpha))

  cat("% confidence interval\n")

  cat("CV (%) ..................................:", 

      formatC(CV, format="f", digits=2),"\n")

  cat("Point estimate (GMR).(%).................:", 

      formatC(100*PE, format="f", digits=2),"\n")

  cat("Lower confidence limit.(%)...............:", 

      formatC(100*CI[1], format="f", digits=2) ,"\n")

  cat("Upper confidence limit.(%)...............:", 

      formatC(100*CI[2],format="f", digits=2) ,"\n")

  cat(sep,"\n\n")

  

  #reset options

  options(ow)

}



data <- read.delim("https://static-content.springer.com/esm/art%3A10.1208%2Fs12248-014-9661-0/MediaObjects/12248_2014_9661_MOESM1_ESM.txt")

Group1 <- subset(data, Subj <= 9)

Group1$Group <- 1

Group2 <- subset(data, Subj > 9)

Group2$Group <- 2

data<-rbind(Group1, Group2)



Analyse222BE(data, alpha=0.05, Group = FALSE)

Analyse222BE(data, alpha=0.05, Group = TRUE)

> Analyse222BE(data, alpha=0.05, Group = FALSE)

_______________________________________________________________________________ 

_______________________________________________________________________________ 

Type I sum of squares: Analysis of Variance Table



Response: log(Var)

          Df  Sum Sq   Mean Sq  F value     Pr(>F)    

Trt        1 0.02285 0.0228494  3.57254   0.076998 .  

Per        1 0.04535 0.0453497  7.09049   0.017019 *  

Seq        1 0.21836 0.2183553 34.14018 2.4964e-05 ***

Subj      16 4.24539 0.2653366 41.48578 5.2285e-10 ***

Residuals 16 0.10233 0.0063958                        

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1



Back-transformed PE and  90% confidence interval

CV (%) ..................................: 8.01 

Point estimate (GMR).(%).................: 95.09 

Lower confidence limit.(%)...............: 90.76 

Upper confidence limit.(%)...............: 99.62 

_______________________________________________________________________________ 



> Analyse222BE(data, alpha=0.05, Group = TRUE)

_______________________________________________________________________________ 

_______________________________________________________________________________ 

Type I sum of squares: Analysis of Variance Table



Response: log(Var)

          Df  Sum Sq   Mean Sq  F value     Pr(>F)    

Trt        1 0.02285 0.0228494  3.42473   0.084021 .  

Per        1 0.04535 0.0453497  6.79711   0.019816 *  

Seq        1 0.21836 0.2183553 32.72757 4.0497e-05 ***

Subj      16 4.24539 0.2653366 39.76923 2.1387e-09 ***

Per:Group  1 0.00225 0.0022549  0.33797   0.569635    

Residuals 15 0.10008 0.0066719                        

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1



Back-transformed PE and  90% confidence interval

CV (%) ..................................: 8.18 

Point estimate (GMR).(%).................: 94.92 

Lower confidence limit.(%)...............: 90.47 

Upper confidence limit.(%)...............: 99.59 

_______________________________________________________________________________

Even PE differs!
What the heck?? Where am I wrong? I need to take some Schützomycin!
PS: I see now, the model with Period:Group is wrong

—
Kind regards,
Mittyri

Complete thread:

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