Integration of SDTM File Creation into Study Analysis:
A Practical Approach
Anna Kunina, Edzel Cabanero, Efim Dynin, Lindley Frahm
04Apr2008
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Introduction
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About CV Therapeutics
– Company focused on applying molecular
cardiology to the discovery and development of
molecular drug for the treatment of cardiovascular
diseases.
– One product on the market, 3 submissions are
currently under review (1 in Europe, 2 in US)
About authors
– Statistical programmer-analysts who developed
and implemented presented approach at CVT
Biostatistics department.
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CRTs/SDTM submissions based on CDISC
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2 submissions based on CDISC version 2 – 2002,
2005. Prepared by Clinical Data Management in
SAS.
2 submissions based on CDISC version 3.1.1 – 2007.
Prepared by Statistical Programming in SAS.
In addition 5 individual studies SDTM files submitted
along with final study reports.
Overall SDTM files according to CDISC version 3.1.1
were submitted for 19 studies, 2 studies SDTM files
are currently under internal review.
Currently CVT does not submit ADaM files.
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Submissions based on CDISC version 2
Method #1– parallel aproach
Raw
Data
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SAS
Programs
Analysis
Files
SAS
Programs
CRTs/
SDTM
Files
TLFs
Advantages
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Easy to achieve consistency across studies (standard
programs)
SDTM files are created only if and when they are
needed.
Disadvantages
● Main problem – discrepancies between SDTM files
and analysis
- duplicate efforts to create analysis type variables in
SDTM, that have already been derived in analysis
- still no guarantee that analysis and SDTM files
match
- need to cross check
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Method #2 – ‘Mesh Approach’
Analysis
Files
Raw
Data
TLFs
SAS
Programs *
SDTM
Files
* A single SAS Program creates both an Analysis
File and an SDTM file.
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Advantages
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No problem of consistency between analysis and
SDTM files, no need for cross-checking.
SDTM files prepared well in advance before
submission.
Data checking fits well in the process.
Disadvantages
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Analysis files and SDTM files are too dependent on
each other, no room for flexibility. Even a small
change in one part leads to rerun of the whole
process.
SDTM files may not be required for the project.
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Method #3 recommended by CDISC – ‘SDTM
Files first’
Raw
Data
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SDTM
Files
Analysis
Files
TLFs
Advantages
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Consistency between SDTM files and analysis is built
in the process.
SDTM files prepared well in advance before
submission.
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Disadvantages
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Changes in SDTM files (change of CDISC version,
changes to have cross-studies consistency in
submission, etc.) do require rerun of analysis. Even if
there is no impact in content, the audit trail is
destroyed.
SDTM files may not be required for the project.
Extra manipulations to create supplemental qualifier
SDTM files and then integrate them back into
analysis files.
Need to link supplemental qualifiers with main
domains makes it difficult to implement data checking
at SDTM files level.
Need extra time to allow for early creation of final
SDTM files early on.
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Method #4 currently used at CVT – Pre-SDTM files
Analysis
Files
Raw
Data
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Pre-SDTM
Files
SDTM
Files
TLFs
Pre-SDTM file (e.g. PS_DM, PS_AE, etc.) is a superset
of all SDTM variables to be included in a given CDISC
domain (e.g. DM, AE, etc.) and analysis files; raw data
variables to be included in supplemental qualifier
SDTM files; raw data and derived variables to be used
to create analysis files or for data checking in SDTM
required data structure.
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Advantages
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Balance between dependency and flexibility.
- Consistency between SDTM and analysis files.
- No duplication in creating analysis type SDTM
variables and analysis files variables.
- Main domains files and supplemental qualifier files
created in the same process.
- Changes to SDTM files compared to Pre-SDTM
files are possible with saved audit trail.
SDTM files created if and when needed.
Pre-SDTM files convenient for data checking.
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Disadvantages
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Requires extra time for planning, programming and
validation of Pre-SDTM files.
Actual SDTM file creation is on critical path for
submission.
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Process of creating SDTM files
PS_ files
Variable
Definition
Tables
Variable
Definition
Templates
SDTM files
Variable
Definition
Tables
Main
Domains
Programs
Raw
Data
SDTM
Files
SAS
Programs Pre-SDTM
Files
SUPPQUAL
Programs
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Lessons learned
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Planning and preparation is a key.
Changes in SDTM files compared to Pre-SDTM files
do happen – minimize as much as possible.
Create ‘double-duty’ variables – use grouping
variables (--CAT, --SCAT, --SPID, --GRPID) to reflect
analysis.
Creating drafts of trial design domain files at the
stage of Pre-SDTM files is beneficial for analysis.
SE, SV are convenient tools for data checking.
Pre-SDTM files can serve as analysis files.
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Database, data extracts and SDTM files
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Make raw data variables from database the same as
SDTM file variables only if they are exactly the same.
Examples: -ORRES variables, AETERM.
If there any differences between CDISC requirements
and meta data collected in the study, raw data
variables should not match SDTM file variables.
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