Scoring submission readiness from a pharmaverse pipeline
In r4subpharma: 'pharmaverse' Adapters for R4SUB Submission Readiness Evidence

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
has_score <- requireNamespace("r4subscore", quietly = TRUE)

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.

library(r4subpharma)

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)

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")
ev_meta <- metacore_to_evidence(meta, ctx)

ev_meta[, c("indicator_id", "location", "result", "severity")]

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")

ev_adam[, c("indicator_id", "indicator_domain", "location", "result")]

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)
res

res$sci$SCI
res$sci$band

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.