admiral-adsl: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Derives an ADaM Subject-Level Analysis Dataset (ADSL) using the {admiral} R package and pharmaverse ecosystem. Use when a user needs to create ADSL from SDTM domains, derive standard subject-level variables (treatment dates, disposition, demographics, population flags), or generate QC-ready R code following CDISC ADaM conventions. Requires SDTM input data and an ADaM spec.

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admiral-adsl

Shared conventions (library setup, pipe style, date rules, flag convention, # REVIEW: annotations, stopifnot() patterns) are defined in the parent ../SKILL.md. The workflow below is ADSL-specific.

Derives a CDISC-conformant ADSL dataset using {admiral}. Outputs executable, QC-ready R code with derivation logic traceable to the ADaM specification.

See admiral-functions reference for function selection guidance. See adsl-conventions reference for CDISC variable conventions.

Inputs

Before generating code, confirm the following are available or explicitly noted as absent:

Input	Required	Notes
DM	Yes	Subject spine; one record per USUBJID
EX	Yes	Exposure; needed for treatment dates and SAFFL
DS	Yes	Disposition; needed for EOSSTT, DCSREAS
DV	No	Protocol deviations; needed for PPROTFL
MH	No	Medical history flags if protocol requires
VS	No	HEIGHTBL, WEIGHTBL, BMIBL if in scope
ADaM ADSL spec	Yes	Variable list, derivation rules, grouping cut-points
Study context	Yes	Treatment arm names, population flag definitions

If required domains are absent, stop and request them. If optional domains are absent, omit the corresponding derivations and note this in code comments.

Workflow

Follow these steps in order. Generate code section by section, not as a single block.

Step 1 — Setup and domain loading

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

# Load SDTM domains
dm  <- pharmaversesdtm::dm
ex  <- pharmaversesdtm::ex
ds  <- pharmaversesdtm::ds
# mh <- pharmaversesdtm::mh  # uncomment if in scope

# Confirm one record per USUBJID in DM before proceeding
stopifnot(nrow(dm) == n_distinct(dm$USUBJID))

Step 2 — Subject spine

Start from DM. One record per USUBJID is mandatory at this step and must be preserved throughout. Select only variables needed downstream.

adsl <- dm |>
  select(
    STUDYID, USUBJID, SUBJID, SITEID,
    AGE, AGEU, SEX, RACE, ETHNIC, COUNTRY,
    ARM, ARMCD, ACTARM, ACTARMCD,
    DMDTC, RFSTDTC, RFENDTC,
    DTHFL, DTHDTC
  )

Step 3 — Treatment dates (TRTSDTM, TRTSTMF, TRTEDTM, TRTETMF, TRTSDT, TRTEDT)

Derive datetimes first, then extract date-only variables. Always include time_imputation arguments and always retain imputation flags (TRTSTMF, TRTETMF) in new_vars — setting flag_imputation = "auto" without capturing the flag variables provides no traceability benefit.

Remove DOMAIN from EX before merging to avoid variable conflicts.

ex_dtm <- ex |>
  select(-DOMAIN) |>
  derive_vars_dtm(
    dtc             = EXSTDTC,
    new_vars_prefix = "EXST",
    date_imputation = "first",
    time_imputation = "first",
    flag_imputation = "auto"
  ) |>
  derive_vars_dtm(
    dtc             = EXENDTC,
    new_vars_prefix = "EXEN",
    date_imputation = "last",
    time_imputation = "last",
    flag_imputation = "auto"
  )

# TRTSDTM / TRTSTMF: first dose datetime and imputation flag
# REVIEW: The placebo filter (EXTRT == "PLACEBO") must be confirmed against the
#   protocol. In some studies EXDOSE > 0 is sufficient; in others EXDOSE = 0
#   for placebo and EXTRT must be used. Adjust condition per protocol definition.
adsl <- adsl |>
  derive_vars_merged(
    dataset_add = ex_dtm,
    by_vars     = exprs(STUDYID, USUBJID),
    new_vars    = exprs(TRTSDTM = EXSTDTM, TRTSTMF = EXSTTMF),
    order       = exprs(EXSTDTM),
    mode        = "first",
    filter_add  = (EXDOSE > 0 | EXTRT == "PLACEBO") & !is.na(EXSTDTM)
  ) |>
  # TRTEDTM / TRTETMF: last dose datetime and imputation flag
  # REVIEW: If subjects have non-contiguous EX records, TRTEDTM reflects the
  #   last administration date only. Flag for QC if exposure gaps exist.
  derive_vars_merged(
    dataset_add = ex_dtm,
    by_vars     = exprs(STUDYID, USUBJID),
    new_vars    = exprs(TRTEDTM = EXENDTM, TRTETMF = EXENTMF),
    order       = exprs(EXENDTM),
    mode        = "last",
    filter_add  = (EXDOSE > 0 | EXTRT == "PLACEBO") & !is.na(EXENDTM)
  ) |>
  mutate(
    TRTSDT = as.Date(TRTSDTM),
    TRTEDT = as.Date(TRTEDTM)
  )

Step 4 — Planned and actual treatment (TRT01P, TRT01PN, TRT01A, TRT01AN)

Use derive_vars_merged_lookup() with a treatment lookup tibble — this is the idiomatic admiral approach for controlled terminology mapping and is preferred over case_when() or mutate() for treatment arm coding.

TRT01P/TRT01PN: from DM.ARMCD (planned). TRT01A/TRT01AN: from DM.ACTARMCD (actual). These are distinct — derive independently.

# REVIEW: Confirm ARMCD values, treatment labels, and numeric codes against
#   the randomisation schedule and ADaM spec before use.
arm_lookup <- tibble::tribble(
  ~ARMCD,    ~TRT01P,                   ~TRT01PN,
  "Pbo",     "Placebo",                 1L,
  "Xan_Lo",  "Xanomeline Low Dose",     2L,
  "Xan_Hi",  "Xanomeline High Dose",    3L
  # Screen failure subjects (Scrnfail) are not in the lookup — they receive NA
)

adsl <- adsl |>
  derive_vars_merged_lookup(
    dataset_add = arm_lookup,
    by_vars     = exprs(ARMCD),
    new_vars    = exprs(TRT01P, TRT01PN)
  ) |>
  derive_vars_merged_lookup(
    dataset_add = arm_lookup |>
      rename(ACTARMCD = ARMCD, TRT01A = TRT01P, TRT01AN = TRT01PN),
    by_vars     = exprs(ACTARMCD),
    new_vars    = exprs(TRT01A, TRT01AN)
  )

Step 5 — Randomisation and reference dates

Use derive_vars_dt() for all date conversions from DM — never use as.Date() directly on --DTC variables as this bypasses partial date imputation handling.

adsl <- adsl |>
  # RANDDT: date of randomisation from DM.DMDTC
  # REVIEW: Confirm DMDTC is the randomisation date in this study. In some
  #   studies randomisation date comes from a separate SDTM domain (e.g. RS).
  derive_vars_dt(
    dtc             = DMDTC,
    new_vars_prefix = "RAND",
    date_imputation = "first",
    flag_imputation = "auto"
  ) |>
  derive_vars_dt(
    dtc             = RFSTDTC,
    new_vars_prefix = "RFST",
    date_imputation = "first",
    flag_imputation = "auto"
  ) |>
  derive_vars_dt(
    dtc             = RFENDTC,
    new_vars_prefix = "RFEND",
    date_imputation = "last",
    flag_imputation = "auto"
  )

Step 6 — Death variables

adsl <- adsl |>
  derive_vars_dt(
    dtc             = DTHDTC,
    new_vars_prefix = "DTH",
    date_imputation = "first",
    flag_imputation = "auto"
  ) |>
  mutate(
    # Ensure CDISC flag convention: "Y" or NA — never "N"
    DTHFL = if_else(DTHFL == "Y", "Y", NA_character_)
  )

Step 7 — Study day variables

Use derive_vars_dy() — do not compute manually with date subtraction.

adsl <- adsl |>
  derive_vars_dy(
    reference_date = TRTSDT,
    source_vars    = exprs(RANDDT)
  )

Step 8 — Treatment duration

adsl <- adsl |>
  derive_var_trtdurd()
  # Requires TRTSDT and TRTEDT to be present. NA for untreated subjects.

Step 9 — Disposition (EOSSTT, DCSREAS, EOSDT)

Filter DS to DSCAT == "DISPOSITION EVENT". Verify uniqueness before merging. Categorise EOSSTT within the source dataset before the merge — never pass DSDECOD through directly to EOSSTT, as DSDECOD contains reason values ("ADVERSE EVENT", "SCREEN FAILURE") not status values.

Derive DCSREAS in a separate derive_vars_merged() call filtered to discontinued subjects only — this avoids a post-merge mutate() cleanup step.

ds_eos <- ds |>
  select(-DOMAIN) |>
  filter(DSCAT == "DISPOSITION EVENT") |>
  derive_vars_dt(
    dtc             = DSDTC,
    new_vars_prefix = "DS",
    date_imputation = "last",
    flag_imputation = "auto"
  )

# Confirm one DISPOSITION EVENT record per subject
stopifnot(n_distinct(ds_eos$USUBJID) == nrow(ds_eos))

# EOSSTT: end of study status — "COMPLETED" or "DISCONTINUED" only
# REVIEW: Verify the COMPLETED/DISCONTINUED mapping covers all DSDECOD values
#   in this study's DS domain. Some protocols require a third category for
#   "STUDY TERMINATED BY SPONSOR". Confirm with the statistician.
adsl <- adsl |>
  derive_vars_merged(
    dataset_add = ds_eos |>
      mutate(
        EOSSTT = if_else(DSDECOD == "COMPLETED", "COMPLETED", "DISCONTINUED")
      ),
    by_vars  = exprs(STUDYID, USUBJID),
    new_vars = exprs(EOSSTT, EOSDT = DSDT)
  ) |>
  # DCSREAS: decoded discontinuation reason — NA for completers per CDISC convention
  # REVIEW: DCSREAS is sourced from DS.DSDECOD (decoded value). DS.DSTERM
  #   (verbatim text) belongs in DCSREASP. Do not swap these.
  derive_vars_merged(
    dataset_add = ds_eos |>
      filter(DSDECOD != "COMPLETED"),
    by_vars  = exprs(STUDYID, USUBJID),
    new_vars = exprs(DCSREAS = DSDECOD, DCSREASP = DSTERM)
  )

Step 10 — Baseline demographics

Derive AGE groupings per the ADaM spec. The example cut-points below are placeholders only — always replace with the study-specific values from the ADaM spec. Do not use these defaults without explicit confirmation.

# REVIEW: Age cut-points must come from the ADaM spec — they are study-specific.
#   The values below are placeholders. Replace before use.
adsl <- adsl |>
  mutate(
    AGEGR1 = case_when(
      AGE < 65              ~ "<65",     # PLACEHOLDER — confirm from spec
      AGE >= 65 & AGE <= 80 ~ "65-80",  # PLACEHOLDER — confirm from spec
      AGE > 80              ~ ">80"      # PLACEHOLDER — confirm from spec
    ),
    AGEGR1N = case_when(
      AGEGR1 == "<65"   ~ 1L,
      AGEGR1 == "65-80" ~ 2L,
      AGEGR1 == ">80"   ~ 3L
    )
  )

If VS is in scope, derive HEIGHTBL, WEIGHTBL, BMIBL using derive_vars_merged() from the baseline VS records (VSBLFL == "Y").

Step 11 — Population flags (SAFFL, ITTFL, PPROTFL)

Critical: population flag definitions are protocol-specific. The derivations below implement standard logic but must be reviewed against the protocol and SAP before use. Flag is "Y" or NA only — never "N".

# SAFFL: received at least one dose
# REVIEW: SAFFL definition is protocol-specific. The condition below includes
#   placebo subjects (EXTRT == "PLACEBO") who have EXDOSE = 0 in some studies.
#   Verify EXTRT values in EX exhaustively and confirm with the statistician.
adsl <- adsl |>
  derive_var_merged_exist_flag(
    dataset_add   = ex,
    by_vars       = exprs(STUDYID, USUBJID),
    new_var       = SAFFL,
    condition     = (EXDOSE > 0 | EXTRT == "PLACEBO") & !is.na(EXSTDTC),
    true_value    = "Y",
    false_value   = NA_character_,
    missing_value = NA_character_
  ) |>
  # ITTFL: randomised subjects — ARMCD != "Scrnfail" AND ARM != "Screen Failure"
  # REVIEW: Confirm ITTFL exclusion criteria with the statistician. The ARMCD
  #   condition is more reliable than ARM text matching — use both as a safeguard.
  mutate(
    ITTFL = if_else(
      ARMCD != "Scrnfail" & ARM != "Screen Failure",
      "Y",
      NA_character_
    )
  )
# ITTFL pipe chain ends here; PPROTFL is derived separately below.

# PPROTFL: per-protocol — ITT subjects with no major protocol deviations.
# Uses derive_vars_merged() + filter_add (not derive_var_merged_exist_flag) because
# the exclusion criter

View source ↗

File metadata

name: admiral-adsl
description: >
  Derives an ADaM Subject-Level Analysis Dataset (ADSL) using the {admiral}
  R package and pharmaverse ecosystem. Use when a user needs to create ADSL
  from SDTM domains, derive standard subject-level variables (treatment dates,
  disposition, demographics, population flags), or generate QC-ready R code
  following CDISC ADaM conventions. Requires SDTM input data and an ADaM spec.
license: MIT
metadata:
  author: Navitas Data Sciences
  version: "0.2"
  pharmaverse: "true"
  parent: admiral
compatibility: >
  Requires R with admiral, dplyr, lubridate, and pharmaversesdtm installed.
  Designed for use in a GxP-compliant environment with access to SDTM datasets
  and an ADaM ADSL specification.

View original text

---
name: admiral-adsl
description: >
  Derives an ADaM Subject-Level Analysis Dataset (ADSL) using the {admiral}
  R package and pharmaverse ecosystem. Use when a user needs to create ADSL
  from SDTM domains, derive standard subject-level variables (treatment dates,
  disposition, demographics, population flags), or generate QC-ready R code
  following CDISC ADaM conventions. Requires SDTM input data and an ADaM spec.
license: MIT
metadata:
  author: Navitas Data Sciences
  version: "0.2"
  pharmaverse: "true"
  parent: admiral
compatibility: >
  Requires R with admiral, dplyr, lubridate, and pharmaversesdtm installed.
  Designed for use in a GxP-compliant environment with access to SDTM datasets
  and an ADaM ADSL specification.
---

# admiral-adsl

> Shared conventions (library setup, pipe style, date rules, flag convention,
> `# REVIEW:` annotations, `stopifnot()` patterns) are defined in the parent
> [`../SKILL.md`](../SKILL.md). The workflow below is ADSL-specific.

Derives a CDISC-conformant ADSL dataset using {admiral}. Outputs executable,
QC-ready R code with derivation logic traceable to the ADaM specification.

See [admiral-functions reference](references/admiral-functions.md) for function
selection guidance. See [adsl-conventions reference](references/adsl-conventions.md)
for CDISC variable conventions.

---

## Inputs

Before generating code, confirm the following are available or explicitly noted
as absent:

| Input | Required | Notes |
|---|---|---|
| DM | Yes | Subject spine; one record per USUBJID |
| EX | Yes | Exposure; needed for treatment dates and SAFFL |
| DS | Yes | Disposition; needed for EOSSTT, DCSREAS |
| DV | No | Protocol deviations; needed for PPROTFL |
| MH | No | Medical history flags if protocol requires |
| VS | No | HEIGHTBL, WEIGHTBL, BMIBL if in scope |
| ADaM ADSL spec | Yes | Variable list, derivation rules, grouping cut-points |
| Study context | Yes | Treatment arm names, population flag definitions |

If required domains are absent, stop and request them. If optional domains are
absent, omit the corresponding derivations and note this in code comments.

---

## Workflow

Follow these steps in order. Generate code section by section, not as a single
block.

### Step 1 — Setup and domain loading

```r
library(admiral)
library(dplyr)
library(lubridate)
library(pharmaversesdtm)

# Load SDTM domains
dm  <- pharmaversesdtm::dm
ex  <- pharmaversesdtm::ex
ds  <- pharmaversesdtm::ds
# mh <- pharmaversesdtm::mh  # uncomment if in scope

# Confirm one record per USUBJID in DM before proceeding
stopifnot(nrow(dm) == n_distinct(dm$USUBJID))
```

### Step 2 — Subject spine

Start from DM. One record per USUBJID is mandatory at this step and must be
preserved throughout. Select only variables needed downstream.

```r
adsl <- dm |>
  select(
    STUDYID, USUBJID, SUBJID, SITEID,
    AGE, AGEU, SEX, RACE, ETHNIC, COUNTRY,
    ARM, ARMCD, ACTARM, ACTARMCD,
    DMDTC, RFSTDTC, RFENDTC,
    DTHFL, DTHDTC
  )
```

### Step 3 — Treatment dates (TRTSDTM, TRTSTMF, TRTEDTM, TRTETMF, TRTSDT, TRTEDT)

Derive datetimes first, then extract date-only variables. Always include
`time_imputation` arguments and always retain imputation flags (TRTSTMF,
TRTETMF) in `new_vars` — setting `flag_imputation = "auto"` without capturing
the flag variables provides no traceability benefit.

Remove DOMAIN from EX before merging to avoid variable conflicts.

```r
ex_dtm <- ex |>
  select(-DOMAIN) |>
  derive_vars_dtm(
    dtc             = EXSTDTC,
    new_vars_prefix = "EXST",
    date_imputation = "first",
    time_imputation = "first",
    flag_imputation = "auto"
  ) |>
  derive_vars_dtm(
    dtc             = EXENDTC,
    new_vars_prefix = "EXEN",
    date_imputation = "last",
    time_imputation = "last",
    flag_imputation = "auto"
  )

# TRTSDTM / TRTSTMF: first dose datetime and imputation flag
# REVIEW: The placebo filter (EXTRT == "PLACEBO") must be confirmed against the
#   protocol. In some studies EXDOSE > 0 is sufficient; in others EXDOSE = 0
#   for placebo and EXTRT must be used. Adjust condition per protocol definition.
adsl <- adsl |>
  derive_vars_merged(
    dataset_add = ex_dtm,
    by_vars     = exprs(STUDYID, USUBJID),
    new_vars    = exprs(TRTSDTM = EXSTDTM, TRTSTMF = EXSTTMF),
    order       = exprs(EXSTDTM),
    mode        = "first",
    filter_add  = (EXDOSE > 0 | EXTRT == "PLACEBO") & !is.na(EXSTDTM)
  ) |>
  # TRTEDTM / TRTETMF: last dose datetime and imputation flag
  # REVIEW: If subjects have non-contiguous EX records, TRTEDTM reflects the
  #   last administration date only. Flag for QC if exposure gaps exist.
  derive_vars_merged(
    dataset_add = ex_dtm,
    by_vars     = exprs(STUDYID, USUBJID),
    new_vars    = exprs(TRTEDTM = EXENDTM, TRTETMF = EXENTMF),
    order       = exprs(EXENDTM),
    mode        = "last",
    filter_add  = (EXDOSE > 0 | EXTRT == "PLACEBO") & !is.na(EXENDTM)
  ) |>
  mutate(
    TRTSDT = as.Date(TRTSDTM),
    TRTEDT = as.Date(TRTEDTM)
  )
```

### Step 4 — Planned and actual treatment (TRT01P, TRT01PN, TRT01A, TRT01AN)

Use `derive_vars_merged_lookup()` with a treatment lookup tibble — this is the
idiomatic admiral approach for controlled terminology mapping and is preferred
over `case_when()` or `mutate()` for treatment arm coding.

TRT01P/TRT01PN: from DM.ARMCD (planned). TRT01A/TRT01AN: from DM.ACTARMCD
(actual). These are distinct — derive independently.

```r
# REVIEW: Confirm ARMCD values, treatment labels, and numeric codes against
#   the randomisation schedule and ADaM spec before use.
arm_lookup <- tibble::tribble(
  ~ARMCD,    ~TRT01P,                   ~TRT01PN,
  "Pbo",     "Placebo",                 1L,
  "Xan_Lo",  "Xanomeline Low Dose",     2L,
  "Xan_Hi",  "Xanomeline High Dose",    3L
  # Screen failure subjects (Scrnfail) are not in the lookup — they receive NA
)

adsl <- adsl |>
  derive_vars_merged_lookup(
    dataset_add = arm_lookup,
    by_vars     = exprs(ARMCD),
    new_vars    = exprs(TRT01P, TRT01PN)
  ) |>
  derive_vars_merged_lookup(
    dataset_add = arm_lookup |>
      rename(ACTARMCD = ARMCD, TRT01A = TRT01P, TRT01AN = TRT01PN),
    by_vars     = exprs(ACTARMCD),
    new_vars    = exprs(TRT01A, TRT01AN)
  )
```

### Step 5 — Randomisation and reference dates

Use `derive_vars_dt()` for all date conversions from DM — never use
`as.Date()` directly on `--DTC` variables as this bypasses partial date
imputation handling.

```r
adsl <- adsl |>
  # RANDDT: date of randomisation from DM.DMDTC
  # REVIEW: Confirm DMDTC is the randomisation date in this study. In some
  #   studies randomisation date comes from a separate SDTM domain (e.g. RS).
  derive_vars_dt(
    dtc             = DMDTC,
    new_vars_prefix = "RAND",
    date_imputation = "first",
    flag_imputation = "auto"
  ) |>
  derive_vars_dt(
    dtc             = RFSTDTC,
    new_vars_prefix = "RFST",
    date_imputation = "first",
    flag_imputation = "auto"
  ) |>
  derive_vars_dt(
    dtc             = RFENDTC,
    new_vars_prefix = "RFEND",
    date_imputation = "last",
    flag_imputation = "auto"
  )
```

### Step 6 — Death variables

```r
adsl <- adsl |>
  derive_vars_dt(
    dtc             = DTHDTC,
    new_vars_prefix = "DTH",
    date_imputation = "first",
    flag_imputation = "auto"
  ) |>
  mutate(
    # Ensure CDISC flag convention: "Y" or NA — never "N"
    DTHFL = if_else(DTHFL == "Y", "Y", NA_character_)
  )
```

### Step 7 — Study day variables

Use `derive_vars_dy()` — do not compute manually with date subtraction.

```r
adsl <- adsl |>
  derive_vars_dy(
    reference_date = TRTSDT,
    source_vars    = exprs(RANDDT)
  )
```

### Step 8 — Treatment duration

```r
adsl <- adsl |>
  derive_var_trtdurd()
  # Requires TRTSDT and TRTEDT to be present. NA for untreated subjects.
```

### Step 9 — Disposition (EOSSTT, DCSREAS, EOSDT)

Filter DS to `DSCAT == "DISPOSITION EVENT"`. Verify uniqueness before merging.
Categorise EOSSTT **within the source dataset** before the merge — never pass
`DSDECOD` through directly to EOSSTT, as DSDECOD contains reason values
(`"ADVERSE EVENT"`, `"SCREEN FAILURE"`) not status values.

Derive DCSREAS in a separate `derive_vars_merged()` call filtered to
discontinued subjects only — this avoids a post-merge `mutate()` cleanup step.

```r
ds_eos <- ds |>
  select(-DOMAIN) |>
  filter(DSCAT == "DISPOSITION EVENT") |>
  derive_vars_dt(
    dtc             = DSDTC,
    new_vars_prefix = "DS",
    date_imputation = "last",
    flag_imputation = "auto"
  )

# Confirm one DISPOSITION EVENT record per subject
stopifnot(n_distinct(ds_eos$USUBJID) == nrow(ds_eos))

# EOSSTT: end of study status — "COMPLETED" or "DISCONTINUED" only
# REVIEW: Verify the COMPLETED/DISCONTINUED mapping covers all DSDECOD values
#   in this study's DS domain. Some protocols require a third category for
#   "STUDY TERMINATED BY SPONSOR". Confirm with the statistician.
adsl <- adsl |>
  derive_vars_merged(
    dataset_add = ds_eos |>
      mutate(
        EOSSTT = if_else(DSDECOD == "COMPLETED", "COMPLETED", "DISCONTINUED")
      ),
    by_vars  = exprs(STUDYID, USUBJID),
    new_vars = exprs(EOSSTT, EOSDT = DSDT)
  ) |>
  # DCSREAS: decoded discontinuation reason — NA for completers per CDISC convention
  # REVIEW: DCSREAS is sourced from DS.DSDECOD (decoded value). DS.DSTERM
  #   (verbatim text) belongs in DCSREASP. Do not swap these.
  derive_vars_merged(
    dataset_add = ds_eos |>
      filter(DSDECOD != "COMPLETED"),
    by_vars  = exprs(STUDYID, USUBJID),
    new_vars = exprs(DCSREAS = DSDECOD, DCSREASP = DSTERM)
  )
```

### Step 10 — Baseline demographics

Derive AGE groupings per the ADaM spec. **The example cut-points below are
placeholders only** — always replace with the study-specific values from the
ADaM spec. Do not use these defaults without explicit confirmation.

```r
# REVIEW: Age cut-points must come from the ADaM spec — they are study-specific.
#   The values below are placeholders. Replace before use.
adsl <- adsl |>
  mutate(
    AGEGR1 = case_when(
      AGE < 65              ~ "<65",     # PLACEHOLDER — confirm from spec
      AGE >= 65 & AGE <= 80 ~ "65-80",  # PLACEHOLDER — confirm from spec
      AGE > 80              ~ ">80"      # PLACEHOLDER — confirm from spec
    ),
    AGEGR1N = case_when(
      AGEGR1 == "<65"   ~ 1L,
      AGEGR1 == "65-80" ~ 2L,
      AGEGR1 == ">80"   ~ 3L
    )
  )
```

If VS is in scope, derive HEIGHTBL, WEIGHTBL, BMIBL using
`derive_vars_merged()` from the baseline VS records (VSBLFL == "Y").

### Step 11 — Population flags (SAFFL, ITTFL, PPROTFL)

**Critical:** population flag definitions are protocol-specific. The derivations
below implement standard logic but must be reviewed against the protocol and SAP
before use. Flag is `"Y"` or `NA` only — never `"N"`.

```r
# SAFFL: received at least one dose
# REVIEW: SAFFL definition is protocol-specific. The condition below includes
#   placebo subjects (EXTRT == "PLACEBO") who have EXDOSE = 0 in some studies.
#   Verify EXTRT values in EX exhaustively and confirm with the statistician.
adsl <- adsl |>
  derive_var_merged_exist_flag(
    dataset_add   = ex,
    by_vars       = exprs(STUDYID, USUBJID),
    new_var       = SAFFL,
    condition     = (EXDOSE > 0 | EXTRT == "PLACEBO") & !is.na(EXSTDTC),
    true_value    = "Y",
    false_value   = NA_character_,
    missing_value = NA_character_
  ) |>
  # ITTFL: randomised subjects — ARMCD != "Scrnfail" AND ARM != "Screen Failure"
  # REVIEW: Confirm ITTFL exclusion criteria with the statistician. The ARMCD
  #   condition is more reliable than ARM text matching — use both as a safeguard.
  mutate(
    ITTFL = if_else(
      ARMCD != "Scrnfail" & ARM != "Screen Failure",
      "Y",
      NA_character_
    )
  )
# ITTFL pipe chain ends here; PPROTFL is derived separately below.

# PPROTFL: per-protocol — ITT subjects with no major protocol deviations.
# Uses derive_vars_merged() + filter_add (not derive_var_merged_exist_flag) because
# the exclusion criter

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
The SKILL.md excerpt in the prompt is truncated, but the full file appears complete and well-structured.
The skill references a parent SKILL.md for shared conventions; ensure that parent skill is available in the same repository.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Stars/forks activity: 104 stars, 23 forks; issue activity unavailable in current metadata

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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: RConsortium/pharma-skills
License: MIT
Version: 1.0.0
Last GitHub push: Sep 6, 2026
Registry updated: Sep 7, 2026
Instruction path: admiral/admiral-adsl/SKILL.md @ 1fa96eabc072

Version reported in registry metadata; check source releases before relying on it.

Quality

67/100

Promising

Trust

67/100

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    "notice": "Publication, static checks, AI review, and creator verification are independent facts. None guarantees runtime safety."
  },
  "skill": {
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    "name": "admiral-adsl",
    "description": "Derives an ADaM Subject-Level Analysis Dataset (ADSL) using the {admiral} R package and pharmaverse ecosystem. Use when a user needs to create ADSL from SDTM domains, derive standard subject-level variables (treatment dates, disposition, demographics, population flags), or generate QC-ready R code following CDISC ADaM conventions. Requires SDTM input data and an ADaM spec.",
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      "notice": "A skill instruction path and install command are recorded. This is not proof of compatibility, runtime success or safety; review the source and permissions first."
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      "repoActivity": "104 stars, 23 forks",
      "lastPushed": "10d since push",
      "license": "MIT",
      "repository": "https://github.com/RConsortium/pharma-skills/tree/main/admiral/admiral-adsl",
      "install": "npx skills add RConsortium/pharma-skills --skill admiral-adsl",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "no high-risk permission surface in public metadata",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
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      "success_rate": null,
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      "installAttempts": 0,
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      "The skill references a parent SKILL.md for shared conventions; ensure that parent skill is available in the same repository.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Stars/forks activity: 104 stars, 23 forks; issue activity unavailable in current metadata"
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    "human_review_required": true,
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    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "10d since push",
    "risk": "Risky"
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    "The SKILL.md excerpt in the prompt is truncated, but the full file appears complete and well-structured.",
    "No OpenAgentSkill engagement data yet",
    "Audit risk risky exceeds max_risk=medium",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
    "The skill references a parent SKILL.md for shared conventions; ensure that parent skill is available in the same repository.",
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      "Audit: 80/100 Risky",
      "Safety: 68/100 Avoid automatic install",
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    "expected_agent_output": {
      "selected_skill": "rconsortium-admiral-adsl (admiral-adsl)",
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}

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Creator: RConsortium
Source: RConsortium/pharma-skills
Indexed by: OpenAgentSkill community index

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