admiral-adae

Name: admiral-adae
Author: RConsortium

88stars20forks

How to install admiral-adae?

rconsortium/pharma-skills/admiral-adae

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

Installs into the current project.

Summary

Derives an ADaM Adverse Events Analysis Dataset (ADAE) using the {admiral} R package and pharmaverse ecosystem. Use when a user needs to create ADAE from SDTM AE and supporting domains, derive standard adverse event analysis variables (severity, seriousness, treatment-emergent fl

Files · 1

View on GitHub

SKILL.md

1	# admiral-adae
2
3	Derives a CDISC-conformant ADAE dataset using {admiral}. Outputs executable,
4	QC-ready R code with derivation logic traceable to the ADaM specification.
5
6	The primary design challenge in ADAE is the treatment-emergent adverse event
7	(TEAE) flag (TRTEMFL) and its supporting date infrastructure. All date and study
8	day derivations must flow from this before any analysis variables are added.
9
10	---
11
12	## Inputs
13
14	Before generating code, confirm the following are available or explicitly noted
15	as absent:
16
17	\| Input \| Required \| Notes \|
18	\|---\|---\|---\|
19	\| AE \| Yes \| One record per AE per subject; subject spine for ADAE \|
20	\| ADSL \| Yes \| Provides TRTSDT, TRTEDT, TRT01P/A, population flags \|
21	\| MH \| No \| Medical history; needed for pre-existing condition flag (PREFL) \|
22	\| CM \| No \| Concomitant medications; sometimes linked to AE causality \|
23	\| ADaM ADAE spec \| Yes \| Variable list, derivation rules, TEAE definition, grading rules \|
24	\| Study context \| Yes \| TEAE window definition, SMQ/grouping flag scope, severity scale \|
25
26	If AE or ADSL are absent, stop and request them. If optional domains are
27	absent, omit the corresponding derivations and note this in code comments.
28
29	Note on pharmaversesdtm test data: The `pharmaversesdtm::ae` dataset does
30	not contain `AETOXGR`. Users running this skill against pharmaverse test data
31	should skip the AETOXGR derivation in Step 7. The derivation is retained in
32	the skill for use with real study data where NCI CTCAE grading was collected.
33
34	Critical ADSL dependency: ADAE must merge a defined set of ADSL variables
35	onto every AE record. Confirm with the statistician which ADSL variables are
36	required — at minimum: TRTSDT, TRTEDT, TRTSDTM, TRT01P, TRT01PN, TRT01A,
37	TRT01AN, and all population flags in scope (SAFFL, ITTFL).
38
39	---
40
41	## Workflow
42
43	Follow these steps in order. Generate code section by section, not as a single
44	block.
45
46	### Step 1 — Setup and domain loading
47
48	```r
49	library(admiral)
50	library(dplyr)
51	library(lubridate)
52	library(pharmaversesdtm)
53
54	# Load SDTM domains
55	ae <- pharmaversesdtm::ae
56	adsl <- adsl # assumed derived upstream; replace with path/load as needed
57	# mh <- pharmaversesdtm::mh # uncomment if pre-existing condition flag in scope
58
59	# Remove DOMAIN from AE to avoid variable conflicts in merges
60	ae <- ae \|> select(-DOMAIN)
61
62	# Confirm AE has at least one record
63	stopifnot(nrow(ae) > 0)
64	```
65
66	### Step 2 — Subject spine from AE
67
68	ADAE is a one-record-per-AE dataset; the subject spine is AE itself. Start
69	here and add ADSL variables in the next step.
70
71	```r
72	adae <- ae
73	```
74
75	### Step 3 — Merge ADSL variables
76
77	Merge a controlled subset of ADSL variables onto every AE record. Do not merge
78	all of ADSL — select only variables referenced in the ADAE derivation logic
79	and required for the output dataset per the ADaM spec.
80
81	```r
82	# REVIEW: Confirm which ADSL variables are required per the ADAE spec.
83	# The list below covers the minimum set for TEAE flag derivation and treatment
84	# labelling. Extend with population flags and other ADSL variables as needed.
85	adsl_vars <- exprs(
86	STUDYID, USUBJID,
87	TRTSDT, TRTEDT, TRTSDTM,
88	TRT01P, TRT01PN, TRT01A, TRT01AN,
89	SAFFL, ITTFL
90	)
91
92	adae <- adae \|>
93	derive_vars_merged(
94	dataset_add = adsl \|> select(!!!adsl_vars),
95	by_vars = exprs(STUDYID, USUBJID)
96	)
97	```
98
99	### Step 4 — AE date variables (ASTDT, ASTDTF, AENDT, AENDTF)
100
101	Derive analysis start and end dates from AE.AESTDTC and AE.AEENDTC. Always
102	use `derive_vars_dt()` — never `as.Date()` directly on DTC variables.
103
104	Use `date_imputation = "first"` for start dates and `"last"` for end dates per
105	CDISC convention. Always retain imputation flag variables (ASTDTF, AENDTF).
106
107	```r
108	adae <- adae \|>
109	derive_vars_dt(
110	dtc = AESTDTC,
111	new_vars_prefix = "AST",
112	date_imputation = "first",
113	flag_imputation = "auto"
114	) \|>
115	derive_vars_dt(
116	dtc = AEENDTC,
117	new_vars_prefix = "AEN",
118	date_imputation = "last",
119	flag_imputation = "auto"
120	)
121	```
122
123	### Step 5 — Study day variables (ASTDY, AENDY)
124
125	Use `derive_vars_dy()` relative to TRTSDT from ADSL. Do not compute study days
126	manually with date subtraction — this bypasses the Day 1 = first dose date
127	offset logic required

People who liked this also viewed

6 picks

Type

admiral-adae

88stars20forks

View on GitHub

How to install admiral-adae?

rconsortium/pharma-skills/admiral-adae

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

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# admiral-adae
2
3	Derives a CDISC-conformant ADAE dataset using {admiral}. Outputs executable,
4	QC-ready R code with derivation logic traceable to the ADaM specification.
5
6	The primary design challenge in ADAE is the treatment-emergent adverse event
7	(TEAE) flag (TRTEMFL) and its supporting date infrastructure. All date and study
8	day derivations must flow from this before any analysis variables are added.
9
10	---
11
12	## Inputs
13
14	Before generating code, confirm the following are available or explicitly noted
15	as absent:
16
17	\| Input \| Required \| Notes \|
18	\|---\|---\|---\|
19	\| AE \| Yes \| One record per AE per subject; subject spine for ADAE \|
20	\| ADSL \| Yes \| Provides TRTSDT, TRTEDT, TRT01P/A, population flags \|
21	\| MH \| No \| Medical history; needed for pre-existing condition flag (PREFL) \|
22	\| CM \| No \| Concomitant medications; sometimes linked to AE causality \|
23	\| ADaM ADAE spec \| Yes \| Variable list, derivation rules, TEAE definition, grading rules \|
24	\| Study context \| Yes \| TEAE window definition, SMQ/grouping flag scope, severity scale \|
25
26	If AE or ADSL are absent, stop and request them. If optional domains are
27	absent, omit the corresponding derivations and note this in code comments.
28
29	Note on pharmaversesdtm test data: The `pharmaversesdtm::ae` dataset does
30	not contain `AETOXGR`. Users running this skill against pharmaverse test data
31	should skip the AETOXGR derivation in Step 7. The derivation is retained in
32	the skill for use with real study data where NCI CTCAE grading was collected.
33
34	Critical ADSL dependency: ADAE must merge a defined set of ADSL variables
35	onto every AE record. Confirm with the statistician which ADSL variables are
36	required — at minimum: TRTSDT, TRTEDT, TRTSDTM, TRT01P, TRT01PN, TRT01A,
37	TRT01AN, and all population flags in scope (SAFFL, ITTFL).
38
39	---
40
41	## Workflow
42
43	Follow these steps in order. Generate code section by section, not as a single
44	block.
45
46	### Step 1 — Setup and domain loading
47
48	```r
49	library(admiral)
50	library(dplyr)
51	library(lubridate)
52	library(pharmaversesdtm)
53
54	# Load SDTM domains
55	ae <- pharmaversesdtm::ae
56	adsl <- adsl # assumed derived upstream; replace with path/load as needed
57	# mh <- pharmaversesdtm::mh # uncomment if pre-existing condition flag in scope
58
59	# Remove DOMAIN from AE to avoid variable conflicts in merges
60	ae <- ae \|> select(-DOMAIN)
61
62	# Confirm AE has at least one record
63	stopifnot(nrow(ae) > 0)
64	```
65
66	### Step 2 — Subject spine from AE
67
68	ADAE is a one-record-per-AE dataset; the subject spine is AE itself. Start
69	here and add ADSL variables in the next step.
70
71	```r
72	adae <- ae
73	```
74
75	### Step 3 — Merge ADSL variables
76
77	Merge a controlled subset of ADSL variables onto every AE record. Do not merge
78	all of ADSL — select only variables referenced in the ADAE derivation logic
79	and required for the output dataset per the ADaM spec.
80
81	```r
82	# REVIEW: Confirm which ADSL variables are required per the ADAE spec.
83	# The list below covers the minimum set for TEAE flag derivation and treatment
84	# labelling. Extend with population flags and other ADSL variables as needed.
85	adsl_vars <- exprs(
86	STUDYID, USUBJID,
87	TRTSDT, TRTEDT, TRTSDTM,
88	TRT01P, TRT01PN, TRT01A, TRT01AN,
89	SAFFL, ITTFL
90	)
91
92	adae <- adae \|>
93	derive_vars_merged(
94	dataset_add = adsl \|> select(!!!adsl_vars),
95	by_vars = exprs(STUDYID, USUBJID)
96	)
97	```
98
99	### Step 4 — AE date variables (ASTDT, ASTDTF, AENDT, AENDTF)
100
101	Derive analysis start and end dates from AE.AESTDTC and AE.AEENDTC. Always
102	use `derive_vars_dt()` — never `as.Date()` directly on DTC variables.
103
104	Use `date_imputation = "first"` for start dates and `"last"` for end dates per
105	CDISC convention. Always retain imputation flag variables (ASTDTF, AENDTF).
106
107	```r
108	adae <- adae \|>
109	derive_vars_dt(
110	dtc = AESTDTC,
111	new_vars_prefix = "AST",
112	date_imputation = "first",
113	flag_imputation = "auto"
114	) \|>
115	derive_vars_dt(
116	dtc = AEENDTC,
117	new_vars_prefix = "AEN",
118	date_imputation = "last",
119	flag_imputation = "auto"
120	)
121	```
122
123	### Step 5 — Study day variables (ASTDY, AENDY)
124
125	Use `derive_vars_dy()` relative to TRTSDT from ADSL. Do not compute study days
126	manually with date subtraction — this bypasses the Day 1 = first dose date
127	offset logic required

People who liked this also viewed

6 picks

Type