admiral

name: admiral description: > Parent skill for the admiral ADaM derivation family. Covers shared conventions used across all admiral child skills: library setup, pipe style, date derivation rules, flag variable conventions, and QC patterns. Route to a child skill for dataset-specific derivation workflows. license: MIT metadata: author: Navitas Data Sciences version: "0.1" pharmaverse: "true"

admiral (parent skill)

This skill defines the shared conventions that apply across all admiral ADaM derivation skills in this family. When a child skill is loaded, this parent provides the foundation — child skills reference these conventions rather than repeating them.

Skill Routing

Choose the child skill that matches the target ADaM dataset type:

ADSL must be derived before any BDS or OCCDS dataset — population flags and treatment variables from ADSL are merged into all downstream datasets.

Shared Library Setup

Every admiral derivation script begins with this setup block. Add dataset-specific libraries (e.g. metatools, tfrmt) per child skill requirements.

library(admiral)
library(dplyr)
library(lubridate)

# For submission-ready output
library(metacore)
library(xportr)

# For benchmark reproducibility
library(pharmaversesdtm)
library(pharmaverseadam)

Pipe and Expression Style

Always use the native pipe |> — not the magrittr pipe %>%. admiral functions accept |> and it is the pharmaverse standard for new code.

Always use exprs() for admiral verb arguments that accept variable lists. The exprs() wrapper is required for by_vars, new_vars, order, and source_vars arguments in admiral functions. Bare variable names or character strings will not work.

# CORRECT
derive_vars_merged(
  by_vars  = exprs(STUDYID, USUBJID),
  new_vars = exprs(TRTSDT = EXSTDT),
  order    = exprs(EXSTDT)
)

# WRONG — does not work with admiral
derive_vars_merged(
  by_vars  = c("STUDYID", "USUBJID"),
  new_vars = c(TRTSDT = EXSTDT)
)

Date Derivation Rules

Never use as.Date(), as.POSIXct(), or convert_dtc_to_date() directly on --DTC variables. These silently return NA for partial dates (e.g. "2023-06", "2023") without any warning.

Always use the admiral date functions:

Imputation direction

Always use flag_imputation = "auto" — this auto-generates --DTF and --TMF imputation flag variables. Never suppress imputation flags.

# Correct date derivation pattern
source_dt <- source_domain |>
  derive_vars_dt(
    dtc             = XXDTC,
    new_vars_prefix = "XX",
    date_imputation = "first",
    flag_imputation = "auto"
  )

Study day convention

CDISC study day: Day 1 is the reference date; there is no Day 0. Always use derive_vars_dy(). Never compute manually with date2 - date1.

Flag Variable Convention

Flag variables must be "Y" or NA — never "N".

This applies to all flag types: population flags (--FL), baseline flags (ABLFL), analysis flags (ANL01FL), and any other indicator variable.

# CORRECT
mutate(SAFFL = if_else(has_dose, "Y", NA_character_))

# WRONG — violates CDISC ADaM convention
mutate(SAFFL = if_else(has_dose, "Y", "N"))

When using derive_var_merged_exist_flag(), always set both:

• true_value = "Y"
• false_value = NA_character_
• missing_value = NA_character_

DOMAIN Variable Removal

Remove DOMAIN from source datasets before passing to derive_vars_merged(). admiral errors if a variable exists in both the input dataset and the source.

# Pattern for ADSL and all BDS derivations
adsl <- adsl |>
  derive_vars_merged(
    dataset_add = select(ex, -DOMAIN),
    by_vars     = exprs(STUDYID, USUBJID),
    ...
  )

This applies to all SDTM source domains — EX, DS, VS, LB, AE, etc.

QC Patterns

Uniqueness assertions

Use stopifnot() to assert structural assumptions before proceeding. These catch data quality issues early and prevent misleading downstream results.

# Assert one record per subject in DM
stopifnot(nrow(dm) == n_distinct(dm$USUBJID))

# Assert uniqueness in a source dataset before merging
stopifnot(n_distinct(ds_eos$USUBJID) == nrow(ds_eos))

# Assert required structure at end of derivation
stopifnot(nrow(adsl) == n_distinct(adsl$USUBJID))

# REVIEW: annotations

Every protocol-specific decision point in generated code must have a # REVIEW: comment. This is a required output dimension evaluated in all benchmarks.

Required locations:

• Population flag definitions (SAFFL, ITTFL, PPROTFL, ANL01FL)
• Disposition record selection logic
• Age/BMI/lab categorisation cut-points
• Treatment arm coding and numeric assignments
• Baseline window definitions
• Any filter condition that may need protocol-specific adjustment

# REVIEW: SAFFL definition is protocol-specific. Confirm EXTRT values in EX
#   and verify placebo handling against the protocol and SAP before use.
adsl <- adsl |>
  derive_var_merged_exist_flag(
    dataset_add   = ex,
    new_var       = SAFFL,
    condition     = (EXDOSE > 0 | EXTRT == "PLACEBO") & !is.na(EXSTDTC),
    ...
  )

Shared References

The following reference files are shared across the admiral skill family: