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(nif)
nif <- new_nif() %>%
add_administration(examplinib_sad, "EXAMPLINIB", analyte = "RS2023") %>%
add_observation(examplinib_sad, "pc", "RS2023", cmt = 2) %>%
add_baseline(examplinib_sad, "lb", "CREAT") %>%
add_administration(examplinib_fe, "EXAMPLINIB", analyte = "RS2023") %>%
add_observation(examplinib_fe, "pc", "RS2023", cmt = 2) %>%
add_baseline(examplinib_fe, "lb", "CREAT") %>%
add_administration(examplinib_poc, "EXAMPLINIB", analyte = "RS2023") %>%
add_observation(examplinib_poc, "pc", "RS2023", cmt = 2) %>%
add_baseline(examplinib_poc, "lb", "CREAT") %>%
add_bl_crcl() %>%
add_bl_renal()
#> Warning in add_observation(., examplinib_fe, "pc", "RS2023", cmt = 2):
#> Compartment 2 is already assigned!
#> Warning in add_observation(., examplinib_poc, "pc", "RS2023", cmt = 2):
#> Compartment 2 is already assigned!
nif %>%
summary()
#> ----- NONMEM input file (NIF) object summary -----
#> Data from 148 subjects across 3 studies:
#> STUDYID N
#> 2023000001 48
#> 2023000022 80
#> 2023000400 20
#>
#> Sex distribution:
#> SEX N percent
#> male 108 73
#> female 40 27
#>
#> Renal impairment class:
#> CLASS N percent
#> normal 89 60.1
#> mild 44 29.7
#> moderate 15 10.1
#> severe 0 0
#>
#> Treatments:
#> RS2023
#>
#> Analytes:
#> RS2023
#>
#> Subjects per dose level:
#> RS2023 N
#> 5 3
#> 10 3
#> 20 3
#> 50 3
#> 100 6
#> 200 3
#> 500 118
#> 800 6
#> 1000 3
#>
#> 2168 observations:
#> CMT ANALYTE N
#> 2 RS2023 2168
#>
#> Subjects with dose reductions
#> RS2023
#> 2
#>
#> Treatment duration overview:
#> PARENT min max mean median
#> RS2023 1 99 43 58.5