Trace a subject's complete lineage journey

Given a USUBJID (or a .__lid__ value), returns the complete history of that subject across all tagged datasets and operations in the session: which datasets they appear in, which operations they passed through or were excluded by, and which population flags apply to them.

Usage

lg_trace(usubjid, verbose = TRUE)

Arguments

usubjid

Character. The subject identifier to trace. Must match a value of USUBJID in at least one tagged dataset.

verbose

Logical. If TRUE (default), prints a formatted trace to the console.

Value

A list (invisibly) with components:

usubjid

The traced subject ID.

datasets

Character vector of dataset IDs the subject appears in.

operations

Data frame of operations applied to datasets containing this subject.

exclusions

Data frame of exclusion records for this subject, or a zero-row data frame if none.

populations

Named list of population flag values for this subject across all registered populations.

Details

This is the key regulatory tracing capability : a reviewer can ask "show me everything that happened to subject 01-042" and get a complete, programmatically generated answer.

See also

lg_exclusions(), lg_disposition()

Examples

lg_start()
#> lineager: session started
adsl <- lg_tag(
  data.frame(USUBJID = c("01", "02", "03"),
             RANDFL  = c("Y", "N", "Y")),
  dataset_id = "ADSL"
)
#> lineager: tagged 'ADSL' — 3 rows, 2 cols
lg_filter(adsl, RANDFL == "Y",
          reason = "Not randomised", population = "RANDFL")
#> lineager: [ADSL] filter 'Not randomised' — 3 in, 2 out, 1 excluded
#> <lg_df> 'ADSL'  [2 × 3]
#>   USUBJID RANDFL
#> 1      01      Y
#> 2      03      Y

lg_trace("02")
#> 
#> ── lineager trace: USUBJID '02' ──
#> 
#>   Appears in: ADSL
#> 
#>   Operations:
#>     [FILTER] ADSL: Not randomised (3→2)
#> 
#>   Exclusions (1):
#>     ✗ [ADSL] Not randomised [pop: RANDFL]
#>