Bioequivalence and Bioavailability Forum

Hi Sury,

❝ WinNonlin Version 6.4.

AFAIK PHX6.4 is out of support – update.

Are you trying to confuse us – mixed up R and WinNonlin in your table?
I got identical results in the 64bit versions of Phoenix 6.3.0.395 (Mar 2012), 6.4.0.768 (Jul 2014), 7.0.0.2535 (Jun 2016), 8.0.0.3176 (Sep 2017), and 8.1.0.3530 (May 2018).
8.2 (Jun 2019) not installed yet.

Subject Period    lin      linlog
   1      1     9352.581  9317.106
   1      2     7965.182  7931.885
   2      1     8153.202  8126.054
   2      2    11230.876 11214.851
   3      1    11156.603 11087.569
   3      2    10008.454  9949.515
   4      1     6673.169  6637.356
   4      2     8652.151  8610.507

❝ The concern is it may impact the 90% CI in border range?

Unlikely, since T and R will be affected in the same way and it will mean out.

❝ is this difference after decimal point accepted?

If (if!) the result is not correct (by comparing with a manual calculation by the formula), that’s a problem. Software not valid(ated).
However, I got exactly the same results – both linear and lin-up/log-down – in bear and PKNCA (clumsy code at the end) like in Phoenix. Hence, I think that the problem lies in your installation or setup.

❝ Thanks for the manual calculation provided. It helped me a lot

RTFM. Given on page 56 (2|40) of the “Phoenix WinNonlin 6.4 User’s Guide” (Noncompartmental Analysis | AUC calculation and interpolation formulas).

Concerning your OP:

❝ ❝ ❝ conc_obj <- PKNCAconc(X003, Concentration~Time|Subject)

❝ ❝ ❝ i am getting the error "Rows that are not unique per group and time (column names: Time) found within concentration data"

Similar in Phoenix. If you sort only by Subject (i.e., not additionally by Period) you would get:

ERROR 14062: Duplicate time values on data file.

In PKNCA you need to group the data appropriately as well, i.e.,

conc_obj <- PKNCAconc(X003, Concentration~Time|Period+Subject)

---

R-code:

library(RCurl)
library(PKNCA)
url     <- getURL("https://bebac.at/downloads/X003.csv")
data    <- read.csv(text = url)
attach(data) # Convenience
dose    <- 1 # Give the actual one here (identical for all subjects)
subject <- unique(Subject)
period  <- unique(Period)
dose    <- data.frame(Subject = rep(subject, each = length(period)),
                      Period = period, Time = 0, Dose = dose)
data$include_auclast <- TRUE
intervals <- data.frame(start = rep(0, length(subject)*length(period)),
                        end = Inf, auclast = TRUE)
conc_obj  <- PKNCAconc(data, Concentration ~ Time|Period + Subject)
dose_obj  <- PKNCAdose(dose, Dose ~ Time|Period + Subject)
data_obj  <- PKNCAdata(conc_obj, dose_obj, intervals = intervals)
detach(data)
PKNCA.options(auc.method = "lin up/log down")
results_obj <- pk.nca(data_obj)
res.linlog  <- unique(results_obj$result[c(4, 3, 6)])
names(res.linlog)[3] <- "linlog"
PKNCA.options(auc.method = "linear")
results_obj <- pk.nca(data_obj)
res.lin     <- unique(results_obj$result[c(4, 3, 6)])
names(res.lin)[3] <- "lin"
auc         <- merge(res.lin, res.linlog)
print(auc, row.names = FALSE)

Gives:

Subject Period       lin    linlog
      1      1  9352.581  9317.106
      1      2  7965.182  7931.885
      2      1  8153.202  8126.054
      2      2 11230.876 11214.851
      3      1 11156.603 11087.569
      3      2 10008.454  9949.515
      4      1  6673.169  6637.356
      4      2  8652.151  8610.507 ✔