auclast and aucinf not calculated

[jeslo328]

I am trying to conduct a straight forward pknca analysis but notice that the auc is not being calculated though other parameters are being calculated. Attached here is the data and the code - appreciate if you could help point what is going on.

dataP_conce <- data2 %>%
                    filter(CMT==2)
class(dataP_conce$DV)
dataP_conce$DV<-as.numeric(dataP_conce$DV)
as.numeric(dataP_conce$TIME)

dataP_dose <-  data2 %>%
                    filter(CMT==1)
library(PKNCA)

o_conc <- PKNCAconc(dataP_conce,DV~TIME|COHORT + ID)
o_dose <- PKNCAdose(dataP_dose,AMT~TIME|COHORT + ID)

data_obj <- PKNCAdata(o_conc, o_dose)

o_results <- pk.nca(data_obj)
summary(o_results)

start end COHORT N auclast cmax tmax half.life aucinf.obs
0 24 0.25 6 NC . . . .
0 Inf 0.25 6 . 1.22 [56.9] 166 [94.0, 262] 363 [63.1] NC
0 24 0.75 6 NC . . . .
0 Inf 0.75 6 . 4.68 [23.0] 142 [94.0, 190] 484 [55.0] NC

data2.txt

Thank you so much
PKNCA beginner

[billdenney]

Here is a reproducible example of your code:

library(PKNCA)
#> 
#> Attaching package: 'PKNCA'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(tidyverse)

data2 <-
  read.csv(
    "https://github.com/user-attachments/files/17365824/data2.txt",
    header = FALSE, sep = ";", na.strings = ".",
    col.names = c("ID", "AMT", "EVID", "CMT", "TIME", "DV", "X1", "MDV", "TIME2", "X2", "COHORT", "X3")
  )

dataP_conce <-
  data2 %>%
  filter(CMT==2)

dataP_dose <-
  data2 %>%
  filter(CMT==1)

o_conc <- PKNCAconc(dataP_conce,DV~TIME|COHORT + ID)
o_dose <- PKNCAdose(dataP_dose,AMT~TIME|COHORT + ID)

data_obj <- PKNCAdata(o_conc, o_dose)

o_results <- pk.nca(data_obj)
#> Warning: Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Warning: Requesting an AUC range starting (0) before the first measurement (24)
#> is not allowed
#> Warning: Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Warning: Requesting an AUC range starting (0) before the first measurement (24) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (24) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (24) is not allowed
#> Warning: Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Warning: Requesting an AUC range starting (0) before the first measurement (24)
#> is not allowed
#> Warning: Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (6) is not allowed
summary(o_results)
#>  start end COHORT N auclast        cmax            tmax  half.life aucinf.obs
#>      0  24   0.25 6      NC           .               .          .          .
#>      0 Inf   0.25 6       . 1.22 [56.9] 166 [94.0, 262] 363 [63.1]         NC
#>      0  24   0.75 6      NC           .               .          .          .
#>      0 Inf   0.75 6       . 4.68 [23.0] 142 [94.0, 190] 484 [55.0]         NC
#> 
#> Caption: auclast, cmax, aucinf.obs: geometric mean and geometric coefficient of variation; tmax: median and range; half.life: arithmetic mean and standard deviation; N: number of subjects

^{Created on 2024-10-14 with reprex v2.1.1}

[billdenney]

The warnings just after the pk.nca() line are the reason. In your source data, you do not have a concentration measurement at time zero.

I'm trying to improve the documentation so that people can find solutions to their issues more easily. Can you please take a look at the data imputation vignette and see if it answers your question? https://billdenney.github.io/pknca/articles/v08-data-imputation.html