Bioequivalence and Bioavailability Forum

Dear Yung-jin Lee,

❝ So how were the results obtained from running your R codes compared with that from SAS PROC GLM by CRO?

My parameters of interest were: Residuals Mean Squares (MSE), Point Estimate and 90% Confidence Interval. I was able to confirm that these parameters perfectly match with SAS reported values, as well as 'F Values' and 'Pr > F' for prd, drug and seq:subj, but not for seq.

I have regrouped my drug factor to avoid calculation of PE and 90%CI for R/T instead of T/R in some cases:
cat("Available treatments:…")
TotalData$drug["Levels"] ref.drug <- readline(prompt="Enter Reference Treatment Code:  ") #WARNING! No input check!
TotalData$drug <- relevel(TotalData$drug, ref = ref.drug)

By the way, bear's lm.mod() was not confused by having more than 2 periods and 2 sequences, but I've still removed observations for other treatments (T2,T3) from datasets to compare just pair of them. But data was balanced (same number of observations for all sequences).
lm.mod(log(TotalData$C24), TotalData)

Study1:
+----+------------+
|    | P1  P2  P3 |
+----+------------+
| S1 | R   T1  T2 |
| S2 | T1  T2  R  |
| S3 | T2  R   T1 |
+----+------------+
Study2:
+----+----------------+
|    | P1  P2  P3  P4 |
+----+----------------+
| S1 | R   T1  T2  T3 |
| S2 | T1  T2  T3  R  |
| S3 | T2  T3  R   T1 |
| S4 | T3  R   T1  T2 |
+----+----------------+


Best regards,
VStus