a brief history of cdisc implementation stephen harrisonportal.cdisc.org/cdisc user...
TRANSCRIPT
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AZ CDISC Implementation
A brief history of CDISC implementation
Stephen Harrison
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2
Overview
� Background
� CDISC Implementation Strategy
� First steps
� Business as usual
� ADaM or RDB?
� Lessons learned
� Summary
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3
Background
� Seven R&D sites all operating
in their own environments
� Creating and maintaining
similar tools across the R&D
sites
� Continuous duplication of effort
across regions
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A&RT Initiative
� Project initiation – April 2003
� Objective: Harmonise the A&R
process and environment across ALL R&D sites within AZ
� Multiple workstreams looking at
technology, process and standards
� Reporting Database (RDB) w/stream
� Deliver standardized reusable code or macros to automate production of analysis and report ready datasets
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Data Flow Process
� Previous data flow process was a simple route from existing CRFsto Clinical Study Reports/Higher Level Document outputs
� Reporting Database is created directly from the Module Package
� Remit of project was to use existing internal data standards
� Opportunity to implement CDISC standards
Analysis
Datasets/
RDB
Module
Package
RAW
Data
CSR/
HLD
Output
CRFs
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CDISC Implementation Strategy
� RDB completely described in terms of SDTM source – good for reviewer
� No need to construct SDTM at the end of the process
Analysis
Datasets/
RDB
Module
Package
RAW
Data
CRFs
CSR/
HLD
Output
SDTM
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CDISC Implementation Strategy
Analysis
Datasets/
RDB
Module
Package
RAW
Data
CRFs
CSR/
HLD
Output
SDTM
� RDB completely described in terms of SDTM source – good for reviewer
� No need to construct SDTM at the end of the process
� Linear process fulfils the requirement of traceability
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Longer term strategy
Analysis
Datasets/
RDB
Module
Package
RAW
Data
CRFs
CSR/
HLD
Output
SDTM
New
CRFs/
CDASH
Modified CRFs
� Underlying RAW data standards are SDTM friendly
� Transformation process is simplified
� CDASH - Clinical Data Acquisition Standards Harmonization
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Longer term strategy
Analysis
Datasets/
RDB
Module
Package
RAW
Data
CRFs
CSR/
HLD
Output
SDTM
New
CRFs/
CDASH
ADaM
� Adopt ADaM model, replacing internal data standards
� Utilise industry standard transformation and derivation processes
ADaM
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First steps
� Global team set up August 2005 to specify AZ business rules
� Application of SDTM Implementation Guide v3.1.1 from an AZ point of view
� Two team members also part of CDISC SDS team
� Inside track to SDTM
� Scope - all corporate and TA standard modules (>200)
� Mapping exercise took nearly 18 months to complete!
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Manual mapping document
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Business as usual
� Web Interface developed
� Metadata driven process
� RAW to SDTM and SDTM to RDB mapping function
� Inherit Corporate data standards and maps down to project or study
level
� Metadata used by “code builder” to create executable code
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Windows
PMPL
Data Standards
A&RTApplication
Database
Datasets Variables
RAW Data Metadata
Study
Project Import
A&RT Web Interface
Variables
Dataset
Variables
Dataset
CSV file
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Standards and Reuse of Code
Corporate
Therapy Area
Project
Study
• Data standards
• Mappings
• Data standards
• Mappings
• Data standards
• Mappings
• Data standards
• Mappings
•Locked dataset definitions
•Locked Corporate map
•Locked dataset definitions
•Locked TA map
•Locked dataset definitions
•Locked Project map
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Inheritance – SDTM
RAW Metadata
Study 2Study 1
TA (Respiratory)
Corporate
RESPHIS
DEM
AELOGDEM DEM
AELOG
Project
RESPHISDEM
HISM
RESPHIS
AELOG
PULM
PULMAELOG
SDTM Metadata
Corporate
DM AE MH
PF CF
mapping
Project
DM AE MHPF CF
PULM
Study 1
DM AE
Study 2
DM AE
MHPF CF
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Example RAW – SDTM map
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Define Simple Mapping
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Define Macro Mapping
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Transposition Groups
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A&RT Application DatabaseWeb
Interface
(Oracle)
UNIX
(SAS)
SDTMDatabase
RAWDatabase
ReportingDatabase
Program Builder
Execute job
SAS code
Execute job
SAS code
Load
RAW
Data
Import
RAW
Data
Metadata
Create Mapping Metadata
SDTM � RDB
Create Mapping Metadata
RAW � SDTM
A&RT Mapping Process
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ADaM or RDB?
� Well established reporting requirements
� AZ Reporting Database standards defined and in use before CDISC considered
� Perception that ADaM model still quite unstable and subject to significant change
� Unlike SDTM, no regulatory pressure to implement ADaM
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Reporting Database
RAWModulePackageDatasets
Etc…
WBDC LAB
GRand
AMOS
CROSDTMData
Domains
SupplementalQualifiers
UnalteredSource
Datain SDTMformat
Su
pp
lem
en
tal
Qu
alifi
ers
Ke
y I
D V
ari
ab
les
DerivedVariables
DerivedObservations
Study Database
(RAW Data) Mapping toSDTM
Reporting Database
Deri
ved
Vari
ab
les
Superset New Dataset
+
R_AE
R_DM
R_VS
Etc.
RD_xx
RH_xx
Etc.
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Reporting Datasets (R_)
� Datasets must remain fundamentally unchanged from the SDTM source data. An R_ dataset is a superset of the SDTM dataset
SDTM RDB
� Original SDTM dataset name retained, but prefixed with R_
� All information from SUPP-- datasets re-attached to parent RDB dataset
Observations
R_VS
(Superset)
VS VS
Su
pp
VS
SuppVS
Vari
ab
les
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RDB General Conventions
� All reporting must take place directly from Reporting Database defined at study level
� All variables used for reporting must be created in relevant reporting dataset
� Subject datasets must have at least 1 observation per randomized subject
� All SDTM data must be present in Reporting Database
� Original SDTM data cannot be amended, but new variables or observations can be created as needed (e.g., imputing dates)
� All naming conventions defined by SDTM must be followed when generating additional variables
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RDB Common Dataset Features
� Datasets taken from source database – name prefixed with R_ (e.g., DM becomes R_DM)
� New derived datasets – name prefixed with RD_(e.g., RD_SUBJ)
� Transposed datasets – name prefixed with RH_ (e.g., R_LB becomes RH_LB)
� Datasets must contain Key variables to uniquely identify every observation
� Duplication of variables across multiple datasets should be avoided (except for Key and Cross variables)
� Duplication of source (SDTM) variables should be avoided
� Variables defined at a higher level must not have attributes changed, except in the following circumstances:
� Length may be increased
� Algorithm may be project-specific
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RDB Use of Codes and Decodes
� Historically, codes and decodes used widely
� Associated using SAS formats
� Loses all meaning outside of SAS
� SDTM does not use codes and decodes
� Variables defined using explicit text values to describe observations
� Clear, unambiguous and interpretable irrespective of the tools or software used
� RDB based on SDTM
� Codes and decodes not used in final reporting datasets
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Transposed Datasets
� RAW datasets may be transposed to contain re-structured RAW data (e.g., RH_dataset = horizontal structure, RV_dataset = vertical)
� Normally only considered for Findings domains
� Original dataset must still exist as R_dataset
� May make reporting easier (e.g., lab parameters reported as columns)
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Transposed Datasets
� Carefully consider whether transposed data is essential and/or appropriate
� Duplicates data
� Variable names driven by --TESTCD can be meaningless, e.g.,:
� Significant loss of information
� e.g., original results, units, reference ranges, analysis flags,etc.
� Contravenes CDISC SDTM convention to store units as a separate variable qualifier to the test result
Unique
subject
Identifier
Visit
name
Alanine
Aminotranferase
(ukat/L)
Albumin
(g/L)
Alkaline
Phosphotase
(ukat/L)
Aspartate
Aminotranferase
(ukat/L)
USUBJID VISIT L01101 L01118 L01104 L01102
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Example SDTM to RDB map
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Lessons learned
� Mapping takes a lot of effort!
� Ambiguity in guidance
� Individual opinions and interpretations
� Get your conventions right
� Often had to revisit decisions as experience grew
� Big differences between CRF and SDTM standards:
� Purpose: data collection vs. data storage
� Coding: codes vs. text(e.g., 1, 2, 3 vs. mild, moderate, severe)
� Structure: horizontal vs. vertical
� SDTM IG v3.1.2 a big improvement
� Introduction of Clinical Findings (CF) domain really helped with many difficult mappings
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Changes for SDTM IG v3.1.2 – CF
General Observation ClassesSpecial Purpose
Datasets
Interventions Events Findings Demographics
Comments
Related
Records
Supplemental
Qualifiers
Trial Design
Clinical Findings (CF) Domain
� Findings about Events or Interventions that don’t fit
in SDTM domain variables for those classes
� CFOBJ (Object of Measurement): Event or
Intervention that is the subject of the test evaluation
� Mandatory, but won’t necessarily have a
parent record in another domain
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MHTERM MHOCCUR MHPRESP
MHSTDTC
MHCAT
Changes for SDTM IG v3.1.2 – CF
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CFOBJ
CFTESTCD = OCCUR
CFORRES = answer provided in checkbox
CFCAT
Changes for SDTM IG v3.1.2 – CF
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CFOBJ
CFTEST
CFORRES
CFCAT
Changes for SDTM IG v3.1.2 – CF
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ExampleRow USUBJID CFSEQ CFOBJ CFTEST CFTESTCD CFDTC
1 D06-608-123 1 HYPERTENSION OCCURRENCE OCCUR 2006-08-28
2 D06-608-123 2MYOCARDIAL
INFARCTIONOCCURRENCE OCCUR 2006-08-28
3 D06-608-123 3MYOCARDIAL
INFARCTION
DATE OF MOST
RECENT MIMY_LDAT 2006-06-20
4 D06-608-123 4MYOCARDIAL
INFARCTIONNUMBER OF MI MYNO 2006-08-28
Row USUBJID VISITNUM CFORRES CFSTRESC CFCAT
1 D06-608-123 1 CURRENT CURRENTSPECIFIC CV MEDICAL AND
SURGICAL HISTORY
2 D06-608-123 1 PAST PASTSPECIFIC CV MEDICAL AND
SURGICAL HISTORY
3 D06-608-123 1 2006-06-20 2006-06-20SPECIFIC CV MEDICAL AND
SURGICAL HISTORY
4 D06-608-123 1 2 2SPECIFIC CV MEDICAL AND
SURGICAL HISTORY
(continued)
Changes for SDTM IG v3.1.2 – CF
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Summary
� CDISC Implementation is a huge
task
� AZ strategy allows for step-wise
implementation
� CDASH
� ADaM
� Mapping tool really assists process
� Easy inheritance
� Reuse of standards and code
� SDTM IG v3.1.2 big improvement
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Questions and Answers
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38Thank You