Scoring submission readiness from a pharmaverse pipeline

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r4subpharma bridges a pharmaverse pipeline and the R4SUB ecosystem. It reads the metadata and datasets you already build and emits standardized evidence that r4subscore can turn into a Submission Confidence Index (SCI). Nothing about your pipeline has to change: you add one block at the end.

The metadata contract

Both adapters operate on one small table with a row per dataset variable. You can hand it a data.frame directly, or a metacore object, which as_variable_metadata() unpacks for you.

meta <- data.frame(
  dataset  = "ADSL",
  variable = c("USUBJID", "AGE", "SEX", "TRTSDT"),
  label    = c("Unique Subject Identifier", "Age", "Sex", "Date of First Exposure"),
  type     = c("text", "integer", "text", "integer"),
  origin   = c("Predecessor", "Derived", "Predecessor", "Derived"),
  derivation = c(NA, "Age at informed consent", NA, "First dosing date from EX"),
  stringsAsFactors = FALSE
)

as_variable_metadata(meta)
#> # A tibble: 4 × 7
#>   dataset variable label                     type   origin derivation is_derived
#>   <chr>   <chr>    <chr>                     <chr>  <chr>  <chr>      <lgl>     
#> 1 ADSL    USUBJID  Unique Subject Identifier text   Prede… <NA>       FALSE     
#> 2 ADSL    AGE      Age                       integ… Deriv… Age at in… TRUE      
#> 3 ADSL    SEX      Sex                       text   Prede… <NA>       FALSE     
#> 4 ADSL    TRTSDT   Date of First Exposure    integ… Deriv… First dos… TRUE

With a real metacore object the call is identical — this is how you would wire it into an existing spec:

mc <- metacore::spec_to_metacore("adam_spec.xlsx")
meta <- as_variable_metadata(metacore::select_dataset(mc, "ADSL"))

Evidence from metadata

metacore_to_evidence() scores how completely each variable is documented, reusing the Q-DEFINE-002 (documented) and Q-DEFINE-003 (derivation present) indicators so this evidence lines up with anything parsed straight from Define-XML.

ctx <- r4subcore::r4sub_run_context("STUDY01", "PROD")
#> ℹ Run context created: "R4S-20260831223709-xmf6oiv3"
ev_meta <- metacore_to_evidence(meta, ctx)
#> ℹ metacore_to_evidence: 6 rows from 4 variables
#> ✔ Evidence table created: 6 rows

ev_meta[, c("indicator_id", "location", "result", "severity")]
#>   indicator_id     location result severity
#> 1 Q-DEFINE-002 ADSL:USUBJID   pass     info
#> 2 Q-DEFINE-002     ADSL:AGE   pass     info
#> 3 Q-DEFINE-002     ADSL:SEX   pass     info
#> 4 Q-DEFINE-002  ADSL:TRTSDT   pass     info
#> 5 Q-DEFINE-003     ADSL:AGE   pass     info
#> 6 Q-DEFINE-003  ADSL:TRTSDT   pass     info

Evidence from an ADaM dataset

adam_to_evidence() compares a built dataset against the same metadata. Here SEX is missing, STUDYID is undescribed, and no labels have been applied yet — each becomes an evidence row across the trace, quality, and usability pillars.

adsl <- data.frame(
  USUBJID = c("01-001", "01-002"),
  AGE     = c(54, 61),
  TRTSDT  = c(19100, 19112),
  STUDYID = c("STUDY01", "STUDY01"),
  stringsAsFactors = FALSE
)

ev_adam <- adam_to_evidence(adsl, meta, ctx, dataset_name = "ADSL")
#> ℹ adam_to_evidence: 11 rows for dataset "ADSL"
#> ✔ Evidence table created: 11 rows

ev_adam[, c("indicator_id", "indicator_domain", "location", "result")]
#>    indicator_id indicator_domain     location result
#> 1    T-ADAM-001            trace ADSL:USUBJID   pass
#> 2    T-ADAM-001            trace     ADSL:AGE   pass
#> 3    T-ADAM-001            trace     ADSL:SEX   fail
#> 4    T-ADAM-001            trace  ADSL:TRTSDT   pass
#> 5    T-ADAM-002            trace ADSL:STUDYID   warn
#> 6    Q-ADAM-001          quality ADSL:USUBJID   pass
#> 7    Q-ADAM-001          quality     ADSL:AGE   pass
#> 8    Q-ADAM-001          quality  ADSL:TRTSDT   pass
#> 9    Q-ADAM-002        usability ADSL:USUBJID   fail
#> 10   Q-ADAM-002        usability     ADSL:AGE   fail
#> 11   Q-ADAM-002        usability  ADSL:TRTSDT   fail

One call to a score

submission_readiness() runs both adapters over a set of datasets and, when r4subscore is installed, computes the SCI.

res <- submission_readiness(list(ADSL = adsl), meta, ctx)
#> ℹ metacore_to_evidence: 6 rows from 4 variables
#> ✔ Evidence table created: 6 rows
#> ℹ adam_to_evidence: 11 rows for dataset "ADSL"
#> ✔ Evidence table created: 11 rows
#> ✔ Bound 2 evidence tables: 17 total rows
#> ℹ Submission Confidence Index: 65.4 (conditional)
res
#> <submission_readiness>
#>   evidence rows: 17 
#>   SCI:           65.4 
#>   band:          conditional

res$sci$SCI
#> [1] 65.4
res$sci$band
#> [1] "conditional"

How the pieces map to the SCI

Source	Indicators	Pillar
`metacore_to_evidence()`	`Q-DEFINE-002`, `Q-DEFINE-003`	quality
`adam_to_evidence()`	`T-ADAM-001`, `T-ADAM-002`	trace
`adam_to_evidence()`	`Q-ADAM-001`	quality
`adam_to_evidence()`	`Q-ADAM-002`	usability

Because the adapters emit the standard R4SUB evidence schema, the resulting table also flows into r4subrisk for risk quantification and r4subprofile for authority-specific weighting, exactly like evidence from any other source.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.