mrgsolve-to-nca

library(mrgsolve)
#> 
#> Attaching package: 'mrgsolve'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(PKPDmisc)

mod <- modlib("pk2", end = 36, delta = 0.05)
#> Building pk2 ...
#> done.

dose <- ev(amt = 100) 

out <- mrgsim(mod, dose, output = "df")

summarise(
  out, 
  Cmax = max(CP),
  auc = auc_partial(time,CP),
  auc_inf = auc_inf(time,CP)
)
#>       Cmax      auc  auc_inf
#> 1 3.671298 68.80677 99.99051

dose$amt/mod$CL
#> [1] 100


mod <- modlib("popex", end = 24, delta = 1)
#> Building popex ... done.

dose <- ev(amt = 100, ID = 1:10)

out <- mrgsim(mod, dose, output  = "df")

summarise(
  group_by(out,ID),
  Cmax = max(DV),
  auc = auc_partial(time,DV),
  auc_inf = auc_inf(time,DV)
)
#> # A tibble: 10 x 4
#>       ID  Cmax   auc auc_inf
#>    <dbl> <dbl> <dbl>   <dbl>
#>  1     1  1.97  37.9    75.8
#>  2     2  5.37  81.8   115. 
#>  3     3  4.14  44.7    47.7
#>  4     4  2.67  47.4    65.9
#>  5     5  3.00  44.4    56.7
#>  6     6  1.85  36.2    83.6
#>  7     7  1.28  23.2   320. 
#>  8     8  2.41  45.8    87.1
#>  9     9  4.17  77.9   126. 
#> 10    10  3.78  64.2    95.7
^{Created on 2020-04-04 by the reprex package (v0.3.0)}
kylebaron/mrgsolve-to-nca.md
