CDISC ADaM 2.1 Implementation:
A Challenging Next Step in the Process
Presented by Tineke Callant
2014-03-14
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
2
Clinical Data Interchange Standards Consortium - Introduction
 1997 - Inception
 2000 - 32 global companies
CDISC is a global, open, multidisciplinary, non-profit
organization that has established standards to support the
acquisition, exchange, submission and archive of clinical
research data and metadata.
 2014 - ± 200 organizations




biotechnology and pharmaceutical development companies
device and diagnostic companies
CROs and technology providers
government institutions, academic research centers and other non-profit
organizations
3
Clinical Data Interchange Standards Consortium - Introduction
4
Clinical Data Interchange Standards Consortium - Introduction
 Mission statement
The CDISC mission is to develop and support global,
platform-independent data standards that enable
information system interoperability to improve medical
research and related areas of healthcare.
Data standards to improve clinical research
5
Clinical Data Interchange Standards Consortium - Introduction
- 2001: Biomedical Research Integrated Domain Group (BRIDG) Model
6
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
7
CDISC - Foundational standards
8
CDISC - Foundational standards
content
transport
9
CDISC - Foundational standards
10
CDISC - Foundational standards
 Study Data Tabulation Model (SDTM)
The content standard for regulatory submission of case
report form data tabulations from clinical research studies.
Datasets containing data collected during the study and
organized by clinical domain.
 Analysis Data Model (ADaM)
The content standard for regulatory submission of analysis
datasets and associated files.
Datasets used for statistical analysis and reporting by the
sponsor, submitted in addition to the SDTM domains.
11
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
12
CDISC ADaM V2.1 - Analysis data flow
ADaM
13
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
14
CDISC ADaM V2.1 - ADaM data structures
 The Subject-Level Analysis Dataset (ADSL) structure
 The Basic Data Structure (BDS)
 Other
15
CDISC ADaM V2.1 - ADaM data structures
The Subject-Level Analysis Dataset (ADSL) structure
 One record per subject
 Variables (required + other)





Study identifiers (e.g. DM.STUDYID)
Subject demographics (e.g. DM.AGE)
Population indicator(s) (e.g. RANDFL)
Treatment variables (e.g. DM.ARM)
Trial dates (e.g. RANDDT)
 Required in a CDISC-based submission
16
CDISC ADaM V2.1 - ADaM data structures
 The Subject-Level Analysis Dataset (ADSL) structure
 The Basic Data Structure (BDS)
 Other
17
CDISC ADaM V2.1 - ADaM data structures
The Basic Data Structure (BDS)
 One or more records per subject, per analysis parameter,
per analysis time point (conditionally required)
 Variables





e.g. PARAM and related variables
e.g. AVAL and AVALC and related variables
e.g. the subject identification
e.g. DTYPE
e.g. treatment variables, covariates
 Supports the majority of statistical analyses
18
CDISC ADaM V2.1 - ADaM data structures
 The Subject-Level Analysis Dataset (ADSL) structure
 The Basic Data Structure (BDS)
 Other
19
CDISC ADaM V2.1 - ADaM data structures
Other
 CDISC ADaM Basic Data Structure for Time-to-Event
Analysis Version 1.0 - May 8, 2012
 CDISC ADaM Data Structure for Adverse Event Analysis
Version 1.0 - May 10, 2012
20
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
21
CDISC ADaM V2.1 - Analysis data flow
ADaM
22
CDISC ADaM V2.1 - Traceability
 Understanding the relationship of element vs. predecessor
 Enabling transparancy
 Analysis results → Analysis datasets → SDTM
23
CDISC ADaM V2.1 - Traceability
Strategies for implementing SDTM and ADaM standards
Susan Kenny – Michael Litzsinger
 Parallel method
SDTM Domains
DBMS Extract
Analysis Datasets
 Retrospective method
DBMS Extract → Analysis Datasets → SDTM Domains
 Linear method
DBMS Extract → SDTM Domains → Analysis Datasets
 Hybrid method
DBMS Extract → SDTM Draft Domains → Analysis Datasets → SDTM Final Domains
24
CDISC ADaM V2.1 - Traceability
 Traceability
25
CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1
 Fundamental principles
– Provide traceability between the analysis data and its source
data
 Practical considerations
– Maintain the values and attributes of SDTM variables
 CDISC ADaM implementation guide (IG) V1.0
 General variable naming conventions
26
CDISC ADaM V2.1 - Traceability
General variable naming conventions
Any ADaM variable whose name is the
same as an SDTM variable must be a
copy of the SDTM variable, and its label,
meaning, and values must not be
modified
27
CDISC ADaM V2.1 - Traceability
Strategies for implementing SDTM and ADaM standards
Susan Kenny – Michael Litzsinger
 Parallel method
SDTM Domains
DBMS Extract
Analysis Datasets
 Retrospective method
DBMS Extract → Analysis Datasets → SDTM Domains
 Linear method
DBMS Extract → SDTM Domains → Analysis Datasets
 Hybrid method
DBMS Extract → SDTM Draft Domains → Analysis Datasets → SDTM Final Domains
28
CDISC ADaM V2.1 - Traceability
Strategies for implementing SDTM and ADaM standards
Susan Kenny – Michael Litzsinger
 Linear method
DBMS Extract → SDTM Domains → Analysis Datasets
 Traceability
 CDISC SDTM/ADaM Pilot Project
 Recommended
29
CDISC ADaM V2.1 - Traceability
Strategies for implementing SDTM and ADaM standards
Susan Kenny – Michael Litzsinger
 Hybrid method
DBMS Extract → SDTM Draft Domains → Analysis Datasets → SDTM Final Domains
 Traceability
 Amendment 1 SDTM V1.2 and SDTM IG V3.1.2
 Future?!?
30
CDISC ADaM V2.1 - Traceability
 Traceability → Recommended: Linear method
 Flexible
 Delivery of consistent analysis datasets
 Easy to use (Excel file)
 Easy to maintain (Excel file)
31
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
32
CDISC ADaM V2.1 - ADaM metadata
 Microsoft Office Excel spreadsheet as framework
 Metadata
33
CDISC ADaM V2.1 - ADaM metadata
 Microsoft Office Excel spreadsheet as framework
 analysis dataset
 %CHKSTRUCT(ds_ = )
 Automatization
 Compliance
 define.xml
34
CDISC ADaM V2.1 - ADaM metadata
 Analysis dataset metadata
 Analysis variable metadata
 Analysis parameter value-level metadata
 Analysis results metadata
35
CDISC ADaM V2.1 - ADaM metadata
Analysis dataset metadata
! ≠ SDTM !
The key variables should define uniqueness
 Illustration from CDISC ADaM V2.1
 Practical consideration: ADxxxxxx
36
CDISC ADaM V2.1 - ADaM metadata
Analysis dataset metadata
Analysis dataset naming convention
 ADxxxxxx
 The subject-level analysis dataset is named ADSL
 max. 8 characters
37
CDISC ADaM V2.1 - ADaM metadata
 Analysis dataset metadata
 Analysis variable metadata
 Analysis parameter value-level metadata
 Analysis results metadata
38
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata
 Illustration from CDISC ADaM V2.1
39
CDISC ADaM V2.1 - ADaM metadata
 Analysis dataset metadata
 Analysis variable metadata
 Analysis parameter value-level metadata
 Analysis results metadata
40
CDISC ADaM V2.1 - ADaM metadata
Analysis parameter value-level metadata
 Illustration from CDISC ADaM V2.1
41
CDISC ADaM V2.1 - ADaM metadata
 Analysis dataset metadata
 Analysis variable metadata
 Analysis parameter value-level metadata
 Analysis results metadata (not required)
42
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice
 Analysis dataset metadata
 Analysis variable metadata
Dataset name
Variable name
Variable label
Variable type
Display format
Codelist / Controlled terms
Source / Derivation
Parameter identifier (Basic Data Structure (BDS))
 Analysis results metadata (not required)
43
CDISC ADaM V2.1 - ADaM metadata
 Microsoft Office Excel spreadsheet as framework
 Metadata
44
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice
 SAS variable attributes
 To work in a SAS environment
–
–
–
–
–
–
–
NAME
TYPE
LENGTH
FORMAT
INFORMAT
LABEL
POSITION IN
OBSERVATION
– INDEX TYPE
 Analysis variable metadata fields
–
–
–
–
–
–
DATASET NAME
VARIABLE NAME
VARIABLE LABEL
VARIABLE TYPE
DISPLAY FORMAT
CODELIST /
CONTROLLED TERMS
– SOURCE / DERIVATION
– BASIC DATA STRUCTURE:
PARAMETER IDENTIFIER
45
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice
 Example
...
46
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice Subposition in observation
 Example
 ADSL – SITEGR* (Char) and SITEGR*N (Num)
* = a single digit [1-9]

SITEID

SITEID grouped together by city in the variable SITEGR1 (SITEGR1N)

SITEID grouped together by province in the variable SITEGR2 (SITEGR2N)
47
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice Subposition in observation
ORDER
1
1
2
2
%CHKSTRUCT(ds_ = ADSL)
48
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice Subposition in observation
ORDER
1
2
1
2
49
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice Subposition in observation
 Example

ADSL – SITEGR* (Char) and SITEGR*N (Num)
* = a single digit [1-9]
POSITION IN
OBSERVATION
SUBPOSITION IN
OBSERVATION
VARIABLE NAME
1
STUDYID
2
USUBJID
3
SITEID
4
1
SITEGR*
4
2
SITEGR*N
50
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice
 Example
...
51
CDISC ADaM V2.1 - ADaM metadata
Analysis variable metadata in practice - Core
CDISC SDTM
CDISC ADaM
Req - Required
Req - Required
The variable must be included in the
dataset and cannot be null for any record.
The variable must be included in the
dataset.
Exp - Expected
Cond - Conditionally required
... and may contain some null values.
... in certain circumstances.
Perm - Permissible
Perm - Permissible
The variable should be used in a domain
as appropriate when collected or derived.
The variable may be included in the
dataset, but is not required.
 Nulls are allowed
52
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
53
CHKSTRUCT macro
 Microsoft Office Excel spreadsheet as framework
 analysis dataset
 %CHKSTRUCT(ds_ = )
 Automatization
 Compliance
 define.xml
54
CHKSTRUCT macro - Automatization
4
6
5
7
1
2
3
Before
ORDER THE ANALYSIS VARIABLES
1
2
3
%CHKSTRUCT(ds_ = ADSL)
4
5
6
7
After
55
CHKSTRUCT macro - Automatization
Before
LABEL THE ANALYSIS VARIABLES
%CHKSTRUCT(ds_ = ADSL)
After
56
CHKSTRUCT macro - Automatization
Key variables
Before
2
1
3
4
7
5
6
9
8
10
SORT THE ANALYSIS DATASET
%CHKSTRUCT(ds_ = ADSL)
Key variables
1
2
After
3
4
5
6
7
8
9
10
57
CHKSTRUCT macro – Compliance
Analysis dataset
Analysis variable metadata
58
CHKSTRUCT macro – Compliance
Analysis dataset
Analysis variable metadata
59
CHKSTRUCT macro – Compliance
Analysis dataset
Analysis variable metadata
60
CHKSTRUCT macro
 Excel spreadsheet as framework
 Purpose
 Reference
 Automatization
 Compliance
61
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
62
Linear method - Challenges and solutions
Step 1
63
Linear method - Challenges and solutions
Step 1 - CDISC SDTM Implementation Guide
...
...
64
Linear method - Challenges and solutions
Step 1 - CDISC SDTM Implementation Guide
Any ADaM variable whose name is the
same as an SDTM variable must be a
copy of the SDTM variable, and its label,
meaning, and values must not be
modified
65
Linear method - Challenges and solutions
Step 1 - CDISC SDTM Implementation Guide
Challenge: Flexible variable length
...
...
...
66
Linear method - Challenges and solutions
Step 1 - CDISC SDTM Implementation Guide
Challenge: Flexible variable length
 CDISC SDTM IG
 Variables of the same name in split datasets should have the same
SAS Length attribute
 Version 5 SAS transport file format: max. 200 characters
 -- TESTCD and QNAM: max. 8 characters
 -- TEST and QLABEL: max. 40 characters
 Example: DM.RACE: $41, $50, and $200
 Amendment 1 to SDTM V1.2 and SDTM IG V3.1.2
 Version 5 SAS transport file format: max. 200 characters
! only if necessary !
67
Linear method - Challenges and solutions
Step 1 - CDISC SDTM Implementation Guide
Challenge: Flexible variable length
 Traceability
 Flexible
 Delivery of consistent analysis datasets
 Easy to use
 Easy to maintain
68
Linear method - Challenges and solutions
Step 1 - CDISC SDTM Implementation Guide
Solution: [sdtm] ↔ %CHKSTRUCT(ds_ = )
69
Linear method - Challenges and solutions
Step 1 - CDISC SDTM Implementation Guide
Challenge: Permissible variables
Example: LB.LBSCAT
Solution: [sdtm] ↔ %CHKSTRUCT(ds_ = )
70
Linear method - Challenges and solutions
Step 2
71
Linear method - Challenges and solutions
Step 2 - SUPP- QNAM
→ variable name
 QLABEL
→ variable label
 QVAL
→ variable type
→ variable length
e.g. SUPPDM SDTM dataset
e.g. ADSL ADaM dataset
72
Linear method - Challenges and solutions
Step 2 - SUPP-Challenge: Flexible code list
 QLABEL is different for the same QNAM
– Example
ELIGCONF
ELIGCONF
Subject Still Eligible
Still Fulfill Eligibility Criteria
 QLABEL format
– Example
RANDNO
RANDNO
RANDOMIZATION NUMBER
Randomization Number
 QLABEL changes during the course of a study
– Example
ELIGIBLE
Suject Eligible For Dosing
ELIGIBLE
Subject Eligible For Dosing
73
Linear method - Challenges and solutions
Step 2 - SUPP-Solution: [supp] ↔ %CHKSTRUCT(ds_ = )
74
Linear method - Challenges and solutions
Step 3
75
Linear method - Challenges and solutions - Step 3
ADaM
76
Linear method - Challenges and solutions - Step 3
Challenge: 12 SDTM → 12 ADaM?!?
6
SDTM
8
? ? ?
?
?
3
ADaM
?
1
?
?
?
7
10
9
? ? ?
4
12
2
5
11
77
Linear method - Challenges and solutions - Step 3
Solution: 1 central model + sponsor specific add-ons
domlist.sas7bdat
central
ADaM
model
varlist.sas7bdat
1
codelist.sas7bdat
domlist.sas7bdat
varlist.sas7bdat
2
codelist.sas7bdat
domlist.sas7bdat
sponsor
specific
add-on
varlist.sas7bdat
1
codelist.sas7bdat
1 Convert Excel file to SAS datasets (by ADaM administrator)
2 Combine central model and sponsor specific add-on (by study programmer)
78
Linear method - Challenges and solutions - Step 3
Solution: 1 central model + sponsor specific add-ons
 Traceability
 Flexible
 Delivery of consistent analysis datasets
 Easy to use
 Easy to maintain
79
Linear method - Challenges and solutions
Step 4
80
Linear method - Challenges and solutions - Step 4
Challenge: SDTM model no. 1, 2, 3 ... ?
6
SDTM
8
? ? ?
?
?
3
ADaM
?
1
?
?
?
7
10
9
? ? ?
4
12
2
5
11
81
Linear method - Challenges and solutions - Step 4
Solution: Central metadata repository
 CDISC metadata
 SDTM version
 SDTM metadata

...
 Study characteristics
 Therapeutic area
 Clinical phase
 Trial design characteristics

...
 Project metadata
 Study timelines
 Key Performance Indicators

...
82
Linear method - Challenges and solutions
Step 5
83
Linear method - Challenges and solutions – Step 5
Challenge: Future
84
Linear method - Challenges and solutions – Step 5
Challenge: Future
85
Agenda
 CDISC - Introduction
 CDISC - Foundational standards
 CDISC ADaM V2.1 - Analysis data flow
 CDISC ADaM V2.1 - ADaM data structures
 CDISC ADaM V2.1 - Traceability
 CDISC ADaM V2.1 - ADaM metadata
 CHKSTRUCT macro
 Linear method - Challenges and solutions
 Take home messages
86
Take home messages
Message no. 1
 SDTM and ADaM go hand in hand
 Thus, without a CDISC compliant SDTM database to start from, ADaM cannot exist
SDTM
ADaM
 But do realize a strong analysis data model needs more than a CDISC compliant SDTM
database alone
87
Take home messages
Message no. 2
 Linear method:
 Recommended
 Challenging
 Solution:
 SDTM: Central metadata repository
 ADaM: Automatization, e.g. [sdtm], [supp] …
Study medata differences are handled efficiently
88
E-mail:
tineke.callant@sgs.com
Internet:
www.sgs.com/cro
89