admiral-bds

Name: admiral-bds
Author: RConsortium

88stars20forks

How to install admiral-bds?

rconsortium/pharma-skills/admiral-bds

$npx -y skills add RConsortium/pharma-skills --skill admiral-bds

Installs into the current project.

Summary

Derives ADaM Basic Data Structure (BDS) datasets using the {admiral} R package. Initial scope covers ADVS (vital signs) and ADLB (laboratory values). Use when a user needs to create a BDS findings dataset from SDTM domains, derive parameter assignments, baseline values, change fr

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SKILL.md

1	# admiral-bds
2
3	> Shared conventions (library setup, pipe style, date rules, flag convention,
4	> `# REVIEW:` annotations, `stopifnot()` patterns) are defined in the parent
5	> [`../SKILL.md`](../SKILL.md). The workflow below is BDS-specific.
6
7	Derives CDISC-conformant BDS findings datasets using {admiral}. Outputs
8	executable, QC-ready R code for ADVS and ADLB with full derivation traceability.
9
10	See [bds-conventions reference](references/bds-conventions.md) for BDS variable
11	conventions and record structure. See
12	[`../admiral-adsl/references/admiral-functions.md`](../admiral-adsl/references/admiral-functions.md)
13	for function selection guidance shared across the admiral family.
14
15	---
16
17	## Inputs
18
19	Before generating code, confirm the following are available or explicitly noted
20	as absent:
21
22	\| Input \| Required \| Notes \|
23	\|---\|---\|---\|
24	\| VS or LB \| Yes \| Source SDTM domain for ADVS or ADLB respectively \|
25	\| ADSL \| Yes \| Provides TRTSDT, TRTEDT, treatment variables, and population flags \|
26	\| ADaM BDS spec \| Yes \| Parameter list, derivation rules, visit windows, baseline definition \|
27	\| Study context \| Yes \| Baseline window, analysis flag definitions, visit map \|
28
29	If ADSL is absent, stop and request it. ADSL variables are required before
30	baseline flagging and analysis flags can be derived.
31
32	---
33
34	## Workflow
35
36	Follow these steps in order. Generate code section by section, not as a single block.
37
38	### Step 1 — Setup and domain loading
39
40	```r
41	library(admiral)
42	library(dplyr)
43	library(lubridate)
44	library(pharmaversesdtm)
45
46	# Load source domain — replace with vs/lb per dataset being derived
47	vs <- pharmaversesdtm::vs
48	adsl <- <loaded ADSL dataset>
49
50	# Remove DOMAIN to avoid conflicts in derive_vars_merged() calls
51	vs <- select(vs, -DOMAIN)
52	```
53
54	### Step 2 — Merge ADSL backbone variables
55
56	Bring required ADSL variables into the source dataset before any derivations.
57	At minimum: TRTSDT, TRTEDT, population flags used as analysis set criteria.
58
59	```r
60	# REVIEW: Confirm which population flags and ADSL variables are required by
61	# the ADaM spec for this dataset. Add or remove from new_vars accordingly.
62	advs <- vs \|>
63	derive_vars_merged(
64	dataset_add = adsl,
65	by_vars = exprs(STUDYID, USUBJID),
66	new_vars = exprs(TRTSDT, TRTEDT, TRT01P, TRT01PN, TRT01A, TRT01AN,
67	SAFFL, ITTFL)
68	)
69	```
70
71	### Step 3 — Parameter assignment
72
73	Map SDTM test codes to ADaM parameters. Use `derive_vars_merged_lookup()` with a
74	lookup table driven by the ADaM spec. Do not use `derive_vars_merged()` here —
75	it is not a lookup function and will not correctly handle unmatched records. Do not use `case_when()` or
76	hardcoded `if_else()` chains.
77
78	```r
79	# REVIEW: PARAMCD mapping must match the ADaM spec parameter list exactly.
80	# Confirm VSTESTCD values in VS and align with ADaM PARAMCD conventions.
81	# Remove parameters not in scope for this study.
82	param_lookup <- tibble::tribble(
83	~VSTESTCD, ~PARAMCD, ~PARAM, ~PARAMN,
84	"SYSBP", "SYSBP", "Systolic Blood Pressure", 1L,
85	"DIABP", "DIABP", "Diastolic Blood Pressure", 2L,
86	"PULSE", "PULSE", "Pulse Rate", 3L,
87	"WEIGHT", "WEIGHT", "Weight", 4L,
88	"HEIGHT", "HEIGHT", "Height", 5L,
89	"TEMP", "TEMP", "Temperature", 6L
90	)
91
92	advs <- advs \|>
93	derive_vars_merged_lookup(
94	dataset_add = param_lookup,
95	by_vars = exprs(VSTESTCD),
96	new_vars = exprs(PARAMCD, PARAM, PARAMN)
97	) \|>
98	filter(!is.na(PARAMCD)) # drop records for out-of-scope tests
99	```
100
101	For ADLB, map from LBTESTCD. Include units in PARAM text per ADaM spec.
102
103	### Step 4 — Analysis value (AVAL, AVALC)
104
105	AVAL is the numeric analysis value. AVALC is the character analysis value.
106	Derive from the SDTM result variables, applying unit conversions if required.
107
108	```r
109	advs <- advs \|>
110	mutate(
111	AVAL = VSSTRESN, # numeric result in standard units
112	AVALC = VSSTRESC, # character result (for non-numeric or verbatim)
113	AVALU = VSSTRESU # analysis value units
114	)
115	```
116
117	For ADLB, use LBSTRESN and LBSTRESC. If unit standardisation is required
118	(e.g. converting mg/dL to mmol/L), apply before AVAL assignment a

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admiral-bds

88stars20forks

View on GitHub

How to install admiral-bds?

rconsortium/pharma-skills/admiral-bds

$npx -y skills add RConsortium/pharma-skills --skill admiral-bds

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# admiral-bds
2
3	> Shared conventions (library setup, pipe style, date rules, flag convention,
4	> `# REVIEW:` annotations, `stopifnot()` patterns) are defined in the parent
5	> [`../SKILL.md`](../SKILL.md). The workflow below is BDS-specific.
6
7	Derives CDISC-conformant BDS findings datasets using {admiral}. Outputs
8	executable, QC-ready R code for ADVS and ADLB with full derivation traceability.
9
10	See [bds-conventions reference](references/bds-conventions.md) for BDS variable
11	conventions and record structure. See
12	[`../admiral-adsl/references/admiral-functions.md`](../admiral-adsl/references/admiral-functions.md)
13	for function selection guidance shared across the admiral family.
14
15	---
16
17	## Inputs
18
19	Before generating code, confirm the following are available or explicitly noted
20	as absent:
21
22	\| Input \| Required \| Notes \|
23	\|---\|---\|---\|
24	\| VS or LB \| Yes \| Source SDTM domain for ADVS or ADLB respectively \|
25	\| ADSL \| Yes \| Provides TRTSDT, TRTEDT, treatment variables, and population flags \|
26	\| ADaM BDS spec \| Yes \| Parameter list, derivation rules, visit windows, baseline definition \|
27	\| Study context \| Yes \| Baseline window, analysis flag definitions, visit map \|
28
29	If ADSL is absent, stop and request it. ADSL variables are required before
30	baseline flagging and analysis flags can be derived.
31
32	---
33
34	## Workflow
35
36	Follow these steps in order. Generate code section by section, not as a single block.
37
38	### Step 1 — Setup and domain loading
39
40	```r
41	library(admiral)
42	library(dplyr)
43	library(lubridate)
44	library(pharmaversesdtm)
45
46	# Load source domain — replace with vs/lb per dataset being derived
47	vs <- pharmaversesdtm::vs
48	adsl <- <loaded ADSL dataset>
49
50	# Remove DOMAIN to avoid conflicts in derive_vars_merged() calls
51	vs <- select(vs, -DOMAIN)
52	```
53
54	### Step 2 — Merge ADSL backbone variables
55
56	Bring required ADSL variables into the source dataset before any derivations.
57	At minimum: TRTSDT, TRTEDT, population flags used as analysis set criteria.
58
59	```r
60	# REVIEW: Confirm which population flags and ADSL variables are required by
61	# the ADaM spec for this dataset. Add or remove from new_vars accordingly.
62	advs <- vs \|>
63	derive_vars_merged(
64	dataset_add = adsl,
65	by_vars = exprs(STUDYID, USUBJID),
66	new_vars = exprs(TRTSDT, TRTEDT, TRT01P, TRT01PN, TRT01A, TRT01AN,
67	SAFFL, ITTFL)
68	)
69	```
70
71	### Step 3 — Parameter assignment
72
73	Map SDTM test codes to ADaM parameters. Use `derive_vars_merged_lookup()` with a
74	lookup table driven by the ADaM spec. Do not use `derive_vars_merged()` here —
75	it is not a lookup function and will not correctly handle unmatched records. Do not use `case_when()` or
76	hardcoded `if_else()` chains.
77
78	```r
79	# REVIEW: PARAMCD mapping must match the ADaM spec parameter list exactly.
80	# Confirm VSTESTCD values in VS and align with ADaM PARAMCD conventions.
81	# Remove parameters not in scope for this study.
82	param_lookup <- tibble::tribble(
83	~VSTESTCD, ~PARAMCD, ~PARAM, ~PARAMN,
84	"SYSBP", "SYSBP", "Systolic Blood Pressure", 1L,
85	"DIABP", "DIABP", "Diastolic Blood Pressure", 2L,
86	"PULSE", "PULSE", "Pulse Rate", 3L,
87	"WEIGHT", "WEIGHT", "Weight", 4L,
88	"HEIGHT", "HEIGHT", "Height", 5L,
89	"TEMP", "TEMP", "Temperature", 6L
90	)
91
92	advs <- advs \|>
93	derive_vars_merged_lookup(
94	dataset_add = param_lookup,
95	by_vars = exprs(VSTESTCD),
96	new_vars = exprs(PARAMCD, PARAM, PARAMN)
97	) \|>
98	filter(!is.na(PARAMCD)) # drop records for out-of-scope tests
99	```
100
101	For ADLB, map from LBTESTCD. Include units in PARAM text per ADaM spec.
102
103	### Step 4 — Analysis value (AVAL, AVALC)
104
105	AVAL is the numeric analysis value. AVALC is the character analysis value.
106	Derive from the SDTM result variables, applying unit conversions if required.
107
108	```r
109	advs <- advs \|>
110	mutate(
111	AVAL = VSSTRESN, # numeric result in standard units
112	AVALC = VSSTRESC, # character result (for non-numeric or verbatim)
113	AVALU = VSSTRESU # analysis value units
114	)
115	```
116
117	For ADLB, use LBSTRESN and LBSTRESC. If unit standardisation is required
118	(e.g. converting mg/dL to mmol/L), apply before AVAL assignment a

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