This is a package to create NONMEM input file (NIF) objects from SDTM-formatted clinical study data.

Installation

You can install the development version of nif like this:

devtools::install_github("rstrotmann/nif", build_vignettes=TRUE)

Example

Generate a NIF data set

This is a very basic example using sample SDTM data from a fictional single ascending dose study to create a NIF data set using make_nif():

library(nif)
library(tidyverse)

sdtm <- examplinib_sad

nif <- new_nif() %>% 
  add_administration(sdtm, "EXAMPLINIB", analyte = "RS2023") %>% 
  add_observation(sdtm, "pc", "RS2023", analyte = "RS2023")

head(nif)
#>   REF ID    STUDYID           USUBJID AGE SEX  RACE HEIGHT WEIGHT     BMI
#> 1   1  1 2023000001 20230000011010001  43   0 WHITE  187.4     77 21.9256
#> 2   2  1 2023000001 20230000011010001  43   0 WHITE  187.4     77 21.9256
#> 3   3  1 2023000001 20230000011010001  43   0 WHITE  187.4     77 21.9256
#> 4   4  1 2023000001 20230000011010001  43   0 WHITE  187.4     77 21.9256
#> 5   5  1 2023000001 20230000011010001  43   0 WHITE  187.4     77 21.9256
#> 6   6  1 2023000001 20230000011010001  43   0 WHITE  187.4     77 21.9256
#>                   DTC TIME NTIME TAFD TAD EVID AMT ANALYTE CMT PARENT TRTDY
#> 1 2000-12-31 10:18:00  0.0     0  0.0 0.0    1   5  RS2023   1 RS2023     1
#> 2 2000-12-31 10:18:00  0.0     0  0.0 0.0    0   0  RS2023   2 RS2023     1
#> 3 2000-12-31 10:48:00  0.5    NA  0.5 0.5    0   0  RS2023   2 RS2023     1
#> 4 2000-12-31 11:18:00  1.0    NA  1.0 1.0    0   0  RS2023   2 RS2023     1
#> 5 2000-12-31 11:48:00  1.5    NA  1.5 1.5    0   0  RS2023   2 RS2023     1
#> 6 2000-12-31 12:18:00  2.0    NA  2.0 2.0    0   0  RS2023   2 RS2023     1
#>   METABOLITE DOSE MDV ACTARMCD IMPUTATION      DV
#> 1      FALSE    5   1       C1                 NA
#> 2      FALSE    5   0       C1             0.0000
#> 3      FALSE    5   0       C1            40.7852
#> 4      FALSE    5   0       C1            48.5530
#> 5      FALSE    5   0       C1            44.0391
#> 6      FALSE    5   0       C1            34.0729

In many cases, you may want to add further covariates, e.g., baseline creatinine from the LB domain:

nif <- nif %>%  
  mutate(COHORT = ACTARMCD) %>% 
  add_baseline(sdtm, "lb", "CREAT") %>% 
  add_bl_crcl()

Data exploration

The nif package provides a range of functions to explore and summarize NIF files:

summary(nif)
#> ----- NONMEM input file (NIF) object summary -----
#> Data from 48 subjects across one study:
#>   STUDYID      N    
#>   2023000001   48   
#> 
#> Sex distribution:
#>   SEX      N    percent   
#>   male     48   100       
#>   female   0    0         
#> 
#> Renal impairment class:
#>   CLASS      N    percent   
#>   normal     42   87.5      
#>   mild       6    12.5      
#>   moderate   0    0         
#>   severe     0    0         
#> 
#> Treatments:
#>   RS2023
#> 
#> Analytes:
#>   RS2023
#> 
#> Subjects per dose level:
#>   COHORT   RS2023   N    
#>   C1       5        3    
#>   C10      500      12   
#>   C2       10       3    
#>   C3       20       3    
#>   C4       50       3    
#>   C5       100      6    
#>   C6       200      3    
#>   C7       500      6    
#>   C8       800      6    
#>   C9       1000     3    
#> 
#> 816 observations:
#>   CMT   ANALYTE   N     
#>   2     RS2023    816   
#> 
#> Subjects with dose reductions
#>   RS2023   
#>   2        
#> 
#> Treatment duration overview:
#>   PARENT   min   max   mean   median   
#>   RS2023   1     1     1      1

invisible(capture.output(
  summary(nif) %>%
    plot()
))

Further information

For further guidance see the help for individual functions and the project website on github pages.