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CONFIDENTIAL

Integration of SDTM File Creation into Study Analysis: A Practical Approach. Anna Kunina, Edzel Cabanero, Efim Dynin, Lindley Frahm 04Apr2008. CONFIDENTIAL. Introduction. About CV Therapeutics

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CONFIDENTIAL

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  1. Integration of SDTM File Creation into Study Analysis: A Practical Approach Anna Kunina, Edzel Cabanero, Efim Dynin, Lindley Frahm 04Apr2008 CONFIDENTIAL

  2. Introduction • 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.

  3. CRTs/SDTM submissions based on CDISC • 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.

  4. Submissions based on CDISC version 2 Method #1– parallel aproach SAS Programs Analysis Files TLFs Raw Data SAS Programs CRTs/ SDTM Files

  5. Advantages • 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

  6. Method #2 – ‘Mesh Approach’ Raw Data Analysis Files TLFs SAS Programs * SDTM Files * A single SAS Program creates both an Analysis File and an SDTM file.

  7. Advantages • 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 • 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.

  8. Method #3 recommended by CDISC – ‘SDTM Files first’ Raw Data Analysis Files SDTM Files TLFs

  9. Advantages • Consistency between SDTM files and analysis is built in the process. • SDTM files prepared well in advance before submission.

  10. Disadvantages • 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.

  11. Method #4 currently used at CVT – Pre-SDTM files Raw Data Pre-SDTM Files Analysis Files TLFs SDTM Files

  12. 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.

  13. Advantages • 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.

  14. Disadvantages • Requires extra time for planning, programming and validation of Pre-SDTM files. • Actual SDTM file creation is on critical path for submission.

  15. Process of creating SDTM files Variable Definition Templates SDTM files Variable Definition Tables PS_ files Variable Definition Tables Raw Data Pre-SDTM Files Main Domains Programs SAS Programs SDTM Files SUPPQUAL Programs

  16. Lessons learned • 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.

  17. Database, data extracts and SDTM files • 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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