Auto-generate nif from sdtm object

Usage

nif_auto(
  sdtm,
  ...,
  subject_filter = "!ACTARMCD %in% c('SCRNFAIL', 'NOTTRT')",
  observation_filter = "TRUE",
  baseline_filter = "LBBLFL == 'Y'",
  duplicates = "resolve",
  duplicate_function = mean,
  keep = NULL,
  silent = NULL
)

Arguments

sdtm

A sdtm object. Needs at least the 'DM' and 'VS' domains, and the domain the observations come from.

...

Formulae to define the relationships between PCTESTCD and EXTRT.

subject_filter

The filtering to apply to the DM domain.

observation_filter

The filtering to apply to the observation source data.

baseline_filter

A filter term to identify the baseline condition, as character.

duplicates

Selection how to deal with duplicate observations with respect to the USUBJID, ANALYTE and DTC fields:

'stop': Stop execution and produce error message
'ignore': Include duplicates in the data set
'identify': Return a list of duplicate entries
'resolve': Resolve duplicates, applying the duplicate_function to the duplicate entries.

duplicate_function

Function to resolve duplicate values, defaults to mean.

keep

Columns to keep, as character.

silent

Suppress messages, as logical. Defaults to nif_option setting if NULL.

Value

A nif object.

Details

If no mapping is provided, nif_auto() will try to find matching treatment names in EX and PCTESTCD in PC to create a basic nif object for pharmacokinetic observations. It will fail if no EXTRT and PCTESTCD with identical names are found.

If one or more mappings are provided as additional parameters, nif_auto() will create respective observations. Mappings must be provided in the form:

TESTCD ~ EXTRT

where TESTCD can be any xxTESTCD from any domain xx included in the sdtm object. See testcd() for an overview on all analytes in all domains. Formulae can also specify multiple observations to be associated with one treatment, e.g.,

RS2023 + RS2023487A ~ EXAMPLINIB.

Multiple mappings can be given, separated by commas. The analyte name in the nif object is automatically set to the respective TESTCD.

IMPORTANT: If multiple analytes are specified for a treatment, it is implicitly assumed that the first analyte corresponds to the parent analyte for the treatment, e.g., in the case above, 'RS2023' is assumed to be the analyte for the 'EXAMPLINIB' treatment parent compound.

Note that compartments (CMT) are automatically assigned. If more control over compartments, analyte names and parent assignment is desired, stepwise creation of a nif object using add_administration() and add_observation() is recommended.

Examples

nif_auto(examplinib_sad, RS2023 ~ EXAMPLINIB, silent = TRUE)
#> ----- NONMEM input file (NIF) object -----
#> 816 observations from 48 subjects across 1 study 
#> Analytes: RS2023 
#> 48 males (100%), 0 females (0%)
#> 
#> Columns:
#>   REF, ID, STUDYID, USUBJID, AGE, SEX, RACE, HEIGHT, WEIGHT, BMI, DTC, TIME,
#>   NTIME, TAFD, TAD, EVID, AMT, ANALYTE, CMT, PARENT, TRTDY, METABOLITE, DOSE,
#>   MDV, ACTARMCD, IMPUTATION, DV, BL_CREAT, BL_CRCL, BL_RENAL 
#> 
#> Data (selected columns):
#>   ID   NTIME   TIME   TAD   ANALYTE   EVID   CMT   AMT   DOSE   DV       
#>   1    0       0      0     RS2023    1      1     5     5      NA       
#>   1    0       0      0     RS2023    0      2     0     5      0        
#>   1    NA      0.5    0.5   RS2023    0      2     0     5      40.785   
#>   1    NA      1      1     RS2023    0      2     0     5      48.553   
#>   1    NA      1.5    1.5   RS2023    0      2     0     5      44.039   
#>   1    NA      2      2     RS2023    0      2     0     5      34.073   
#>   1    NA      3      3     RS2023    0      2     0     5      19.399   
#>   1    NA      4      4     RS2023    0      2     0     5      10.533   
#>   1    NA      6      6     RS2023    0      2     0     5      2.953    
#>   1    NA      8      8     RS2023    0      2     0     5      1.034    
#> 854 more rows
nif_auto(examplinib_sad, RS2023 + RS2023487A ~ EXAMPLINIB, silent = TRUE)
#> ----- NONMEM input file (NIF) object -----
#> 1632 observations from 48 subjects across 1 study 
#> Analytes: RS2023 and RS2023487A 
#> 48 males (100%), 0 females (0%)
#> 
#> Columns:
#>   REF, ID, STUDYID, USUBJID, AGE, SEX, RACE, HEIGHT, WEIGHT, BMI, DTC, TIME,
#>   NTIME, TAFD, TAD, EVID, AMT, ANALYTE, CMT, PARENT, TRTDY, METABOLITE, DOSE,
#>   MDV, ACTARMCD, IMPUTATION, DV, BL_CREAT, BL_CRCL, BL_RENAL 
#> 
#> Data (selected columns):
#>   ID   NTIME   TIME   TAD   ANALYTE      EVID   CMT   AMT   DOSE   DV       
#>   1    0       0      0     RS2023       1      1     5     5      NA       
#>   1    0       0      0     RS2023       0      2     0     5      0        
#>   1    0       0      0     RS2023487A   0      3     0     5      0        
#>   1    NA      0.5    0.5   RS2023       0      2     0     5      40.785   
#>   1    NA      0.5    0.5   RS2023487A   0      3     0     5      13.823   
#>   1    NA      1      1     RS2023       0      2     0     5      48.553   
#>   1    NA      1      1     RS2023487A   0      3     0     5      31.645   
#>   1    NA      1.5    1.5   RS2023       0      2     0     5      44.039   
#>   1    NA      1.5    1.5   RS2023487A   0      3     0     5      41.021   
#>   1    NA      2      2     RS2023       0      2     0     5      34.073   
#> 1670 more rows
nif_auto(examplinib_sad, RS2023 + WEIGHT ~ EXAMPLINIB, silent = TRUE)
#> ----- NONMEM input file (NIF) object -----
#> 864 observations from 48 subjects across 1 study 
#> Analytes: WEIGHT and RS2023 
#> 48 males (100%), 0 females (0%)
#> 
#> Columns:
#>   REF, ID, STUDYID, USUBJID, AGE, SEX, RACE, HEIGHT, WEIGHT, BMI, DTC, TIME,
#>   NTIME, TAFD, TAD, EVID, AMT, ANALYTE, CMT, PARENT, TRTDY, METABOLITE, DOSE,
#>   MDV, ACTARMCD, IMPUTATION, DV, BL_CREAT, BL_CRCL, BL_RENAL 
#> 
#> Data (selected columns):
#>   ID   NTIME   TIME   TAD   ANALYTE   EVID   CMT   AMT   DOSE   DV       
#>   1    0       0      0     WEIGHT    1      1     5     5      NA       
#>   1    NA      0      0     WEIGHT    0      2     0     5      77       
#>   1    0       0      0     RS2023    0      3     0     5      0        
#>   1    NA      0.5    0.5   RS2023    0      3     0     5      40.785   
#>   1    NA      1      1     RS2023    0      3     0     5      48.553   
#>   1    NA      1.5    1.5   RS2023    0      3     0     5      44.039   
#>   1    NA      2      2     RS2023    0      3     0     5      34.073   
#>   1    NA      3      3     RS2023    0      3     0     5      19.399   
#>   1    NA      4      4     RS2023    0      3     0     5      10.533   
#>   1    NA      6      6     RS2023    0      3     0     5      2.953    
#> 902 more rows