Genentech Inc. Confidential
CDISC Implementation on a Rheumatoid Arthritis Project Partnership
Patricia Gerend, Olivier Leconte, Chris Price, Michelle Zhang
Genentech, Inc. and Roche Products Limited
19 November 2009
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CDISC Background
CDISC: Clinical Data Interchange Standards Consortium
Founded around 1997
Started by biotech / pharma staffCommon standards would make sponsors more efficientCommon standards would simplify FDA reviewers’ jobs
Used nationally, somewhat internationally
Used by industry, academic, coop, and regulatory groupsCommon standards would accommodate cross-company, cross-molecule monitoring
Many CDISC branchesSDTM (Submission Data Tabulation Model – raw data)ADaM (Analysis Data Model – derived data)Others for protocols, information exchange, lab data, CRF data, etc.
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Project Background
Pharma / Biotech Collaboration: Roche and Genentech (pre-merger)
Rheumatoid Arthritis new molecule
Several new clinical studies getting started
Decision to work on Roche system since databases there
Different proprietary data standards at each company
New industry standard of CDISC
Neither company had production/filing CDISC experience
Genentech had performed 2 pilot CDISC projects, one with MetaXceed and another with PharmaStat, where vendors did modeling and programming
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CDISC: To Use or Not to Use
Decision to use CDISC 11/2007
Could be required by FDA at submission time
Avoids time and hassle of dealing with each other’s proprietary data standards
Provides growth opportunity for staff
Opportune timing since project just getting started
Quick management buy-in
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CDISC: How to Accomplish in a Partnership
Clarify decision making, roles/responsibilities, and accountability
GNE handles data decisions (SDTM and ADaM) since accountable to FDA for submitted data (EU does not receive data)Roche handles process and systems decisions since work done on Roche systems
Communicate, communicate, communicate
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Tasks Required for CDISC Implementation
Intelligence gathering
Documenting standards
SDTMModeling of CRFsControlled TerminologyConversion SpecificationsConversions
ADaMAnalysis Database DesignMetadata and Specifications StructuresDerivations
Electronic submission to FDA
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Intelligence Gathering
Formal training: f2f, on-line (see CDISC web site)
Attendance at Bay Area CDISC Implementation ForumOccurs approximately quarterlyMany SF bay area bio-pharm companies representedCDISC organization speakersCross-pollination of ideas/approaches
Discussions w/ internal staff versed in CDISC
Reading CDISC guides (yes, including the 299-page SDTM-Implementation Guide [IG]!)
Well-organizedComprehensiveGood examplesDoes not address all modeling issues
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SDTM
Modeling
Controlled Terminology
Documentation
Conversion Specifications
Conversions
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SDTM Modeling
Pick a version of the CDISC SDTM Implementation Guide (IG): v3.1.2
Pick a version of the CDISC Controlled Terminology (CT): Recent version issued before first database lock: 7 July 2009
Note: No link between IG and CT
Define naming conventions for user-defined data domains
Define standard ways to handle non-standard data, such as “Other, specify”
Document conventions, modeling decisions, changes, project-specific controlled terminology
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SDTM Modeling Documentation
Value of documentation, though sometimes tedious, cannot be overstated
Document name: SDTM Modeling Information
Document sections:Conventions for SDTM Modeling
CRF -> SDTM Domain Map
SDTM Domain -> CRF Map
Changes to Annotations since First Draft
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Conventions for SDTM Modeling
Conventions for SDTM ModelingFor dates, Findings domains use xxDTC while Interventions and Events domains use xxSTDTC/xxENDTC.
User-defined domain naming conventions– Xx for Interventions (e.g., XP for previous procedures)– Yx for Events (e.g., YI for Previous Immunizations)– Zx for Findings (e.g., ZJ for Tender/Swollen Joint Counts)
A Controlled Terminology spreadsheet for the project is maintained
All xxTEST and xxTESTCD variables are lengths $40 and $8 respectively (except for IE which can be longer)
Handling of “Other, specify” situations– If only 1 response, put into SUPPxx– If > 1 response, consider FA (Findings About) domain if
Findings data and other options
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CRF -> SDTM Domain Map
CRF -> SDTM Domain Map
Page CRF Name Domain
1 Informed Consent DS
2 Eligibility IE
3 Demographics and Subject Characteristics
DM,SC,SU
4 Rheumatoid Diagnosis History MH
5 Other Previous/Current Diseases
MH
Etc.
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SDTM Domain -> CRF Map
SDTM Domain -> CRF Map
Domain CRF Name Page
PE Physical Exam - Baseline 10
PE Physical Exam 36
VS Vital Signs – Baseline 11
VS Vital Signs 42
VS Vital Signs - Unscheduled 88
Etc.
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Changes in Annotations
Changes in Annotations since First Draft
Date CRF Change
1-10-2008 4 Removed EPOCH
1-10-2008 12 RA meds moved from XR to CM domain
4-11-2008 4 Added DSENDTC
4-11-2008 72 Changed RELTYPE from ONE to MANY
6-4-2008 25 Added VSPOS
Etc.
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Controlled Terminology
Two Controlled Terminology (CT) documents:CDISC organizationProject
Identify which version from CDISC organization to use across project
Identify and document terms specific to project to maintain consistency across studies
Map project values to CDISC CT where they exist
Put original values into --ORRES or SUPPQUAL if they differ substantially from CT values
Remember to check if a CDISC CT value list is extensible
Identify a Clinical Scientist to use for input into mappings from original to CT values
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CDISC Controlled Terminology Example
Code Codelist Code
Codelist Extensible (Yes/No)
Codelist Name
CDISC Submission Value
C49503 C66767 No Action Taken with Study Treatment
DOSE INCREASED
CDISC Preferred Term
CDISC Synonym(s)
CDISC Definition
NCI Preferred Term
Pre-release/Production
DOSE INCREASED
Action Taken with Study Treatment
Medication schedule modified either by changing the frequency, strength, or amount. (NCI)
Dose Increased
Production
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CDISC Controlled Terminology
Covers many SDTM variable values
Is updated often, much more so than data models
Generally new rows are added as opposed to changing existing information
Is fairly long (over 1,000 rows in the 7 July 2009 version)
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Project Controlled Terminology Examples
Project Controlled Terminology
Domain
Variable Seq Label Original Value CDISC Std Value
AE AEOUT Outcome of Adverse Event
RESOLVED-NO SEQUELAE
RECOVERED/RESOLVED
AE AECAT Category for Adverse Event
INFUSION RELATED REACTION
LB LBTEST 1 Lab Test or Examination Name
BLOOD GLUCOSE
GLUCOSE
LB LBTESTCD 1 Lab Test or Examination Short Name
GLUC GLUC
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Issues Log
On a large team, it is easy to lose track of issues when addressed via email
Create an Issues LogPut where accessible by whole teamInclude columns indicating problem, who needed to solve it, and resolutionRefer to it often when making decisions to ensure consistency inproject
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Issues Log Example
Issue Detail Issue Type CRF Page / Domain
Raised by Raised when
For QS pages, change values of QSEVLINT to ISO8601 format
Specs Multi Chris Price 7/15/2008
Actioned by Actionedwhen
Status Resolution Comments
Patty Gerend 7/24/2008 Resolved n/a
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SDTM Conversion Specifications
While many conversions are not difficult (e.g., variable re-names), some are, so documentation is helpful
Set up spreadsheet containing list of all possible variables in the domain and algorithms for populating them
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SDTM Conversion Specifications Example
Domain PE (Physical Exam)
STUDYID PEPE.STUDY
DOMAIN “PE”
USUBJID Concatenate PEPE.STUDY, PEPE.CRTN, and PEPE.PT separated by hyphens
PESEQ Unique sequence number of PE observation per subject. Create on each record sequentially.
PEGRPID Not mapped
PEORRES PEPE.PEABN
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SDTM Conversion SAS Programs
Base SAS was used to perform the conversions from Oracle Clinical extract data to SDTM
Advantages over GUI tool used by non-team membersProject programmers can see entire picture of data derivationsProject programmers can participate in conversionsAll data conversions/derivations are in one programming languagewith programs residing in one location to facilitate audit trailAutomated production from start to finish is accommodated
Genentech Inc. Confidential
ADaM
Analysis Database Design
Metadata and Specifications Structures
Derivations
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ADaM General Decisions
Used draft versions ADaM Modelv2.1 and ADaM Implementation Guide v1.0, which should be published end of 2009.
ADaM model accommodates many structures, including proprietary standards w/ CDISC naming conventions
ADaM model also pre-specifies a specific vertical structure containing population variables, treatment variables, and variables to help identify source of derivation
Decided to go with ADaM pre-specified structure for efficacy data to experience the CDISC process in its entirety
Decided to go with proprietary standards w/ CDISC names for safety to facilitate standard safety reporting
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ADaM Challenges
Metadata documentation
Vertical structures
LOCF (last observation carried forward) derivations
Analysis flags
Addition of rows versus columns
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ADaM Metadata Documentation
Derivation text guidelines
Specifications structure decisions
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ADaM Derivations Text Guidelines Examples
Text should be specific and detailed enough to allow re-creation of the derived variable by the reader.
References to source variable names from a dataset other than the one being described should be two-level; e.g., DM.RACE. If the source variable is from the same dataset as that being described, a one-level name is used; e.g., RACE.
Use common English descriptions of operators and other symbols rather than using computer terms or math symbols; e.g., "is missing" rather than "=.".
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ADaM Specifications
The following 2-table format was used:1-Data List document2-Variable List documentValue-level derivation info was embedded into the variable derivation cellsFamiliar to FDA reviewers
Consideration of a 3-table format for future:1-Data list document2-Variable list document3-Value list documentFDA will become more familiar with this in time
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ADaM Metadata Columns
Dataset MetadataNameDescriptionLocation StructurePurposeKey VariablesDocumentation (e.g., Stat Plan, Reviewers’ Guide)
Variable MetadataNameLabelTypeControlled Terms or FormatsSource or Derivation Method
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ADaM Dataset Structures
Dataset structuresADaM structure is verticalGenentech has standard SAS software designed to create and report horizontal analysis dataRoche has standard SAS software designed to create and report vertical analysis data
Use Roche software on Roche system
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ADaM Derivations Example
Last Observation Carried Forward (LOCF)Always complicated regardless of data structureUsed ADaM AVAL (analysis variable) and DTYPE (derivation type) variables together to identify observed and LOCF’ed valuesIn non-CDISC horizontal structures, only 1 variable was needed (it was called LOCF)
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ADaM Example
USUBJID PARAMCD AVISIT AVAL BASE CHG DTYPE ANL1FL(LOCF) ANL2FL(obs)
1000 CRP SCREEN 49.9 76.1 -26.2 1000 CRP BASELN 76.1 76.1 0 Y Y 1000 CRP WK 2 40.6 76.1 -35.5 Y Y1000 CRP WK 4 23.9 76.1 -52.2 Y Y1000 CRP WK 8 22.6 76.1 -53.51000 CRP WK 8 18.7 76.1 -57.4 Y Y1000 CRP WK 12 76.1 1000 CRP WK 12 18.7 76.1 -57.4 LOCF Y 1000 CRP WK 16 14.7 76.1 -61.4 Y Y 1000 CRP WK 24 12.8 76.1 -63.3 Y Y1000 CRP WK 32 10.1 76.1 -66.0 Y Y1000 CRP WK 40 76.1 1000 CRP WK 40 10.1 76.1 -66.0 LOCF Y 1000 CRP WK 48 76.11000 CRP WK 48 10.1 76.1 -66.0 LOCF Y
NOTE: CRP - C-Reactive Protein
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ADaM Analysis Flags
Many different ways of implementing ADaM model
Had to decide between creating analysis flags for all reasonable analyses or for just those pre-specified: created all that seemed reasonable
Example analysis flag: ANL1FL indicates LOCF, excluding rescue and withdrawal
Decided to have ANLxFL represent same concept across all ADaM datasets, even though this means the value of xis not necessarily sequential in each dataset
Example: ADDS1 contains ANL1FL, ANL2FL, ANL3FL, ANL4FL; ADDS2 contains ANL1FL and ANL4FL
ADaM model may still be evolving to handle more cases
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ADaM Addition of Rows Versus Columns
Add a new column for a parameter-invariant functions of AVAL (analysis value) or BASE (baseline value) on the same row
“Parameter-invariant” means the function does not change from parameter to parameter and the meaning of the function is the same on all rowsExample: Change from Baseline
Add a new row for functions that involve more than one parameter or that require a new parameter
Example: Total number of tender joints is derived from each individual joint score, so total number is a new parameter and anew rowExample: LOCF imputation of missing values is put into a new row
Genentech Inc. Confidential
E-Sub
SDTM
ADaM
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Electronic Submission to FDA: SDTM
Followed SDTM-IGv3.1.2 to the best of our abilities
Must still evaluate SDTM structure
Use Phase Forward’s WebSDM productEvaluation of SDTM structure adherenceProduction of define.xml
Will also generate define.pdf to accommodate reviewers
Will submit dataset list, variable list, and controlled terminology
Expectation for SDTM data to load into FDA’s Janus data warehouse for cross-company, cross-drug monitoring
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Electronic Submission to FDA: ADaM
Define.pdf, but not define.xml, will be generated and submitted
Define.xml production is time-consuming, costly, and problematic
Will submit dataset list and variable list
Not currently necessary for ADaM data to be in FDA’s Janus data warehouse
ADaM structure less stable than SDTM and could change later
Genentech Inc. Confidential
Project Assessment
Efficiencies
Conclusions
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Efficiencies Gained
SDTMFirst study took 8 months elapsed timeSecond study took 3 months elapsed timeThird study took < 1 month elapsed time
ADaMFirst study took 4 months elapsed timeSecond study took 2 months elapsed timeThird study took 1 month elapsed time
CDISC OverallNo haggling over each company’s proprietary data structures, so 6 months were saved here
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Conclusion
Results of decision to use CDISC with 2 companies not familiar with its structures
Successful SDTM conversion of 4 studiesSuccessful ADaM derivation on 3 studies, so farIntense CDISC learning across both companiesInformation to move forward with organization-wide CDISC strategies
Successes yet to comeElectronic submission deliverables compilationFDA evaluation of our effortsDrug and indication approval!?
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Acknowledgements
GenentechIan FlemingLauren HaworthSandra Minjoe (formerly)Rajkumar SharmaPeggy WoosterSusan Zhao
RocheFrederik Malfait
I3StatprobeChakrapani Kolluru
PharmaStatJohn BregaJane Diefenbach
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Contacts
Patricia L. GerendSenior Manager, Statistical
Programming & Analysis
Genentech, Inc.
South San Francisco, California, USA
650-225-6005
Olivier LeconteProgramming Team Leader
Roche Products Limited
Welwyn Garden City, UK
+44 (0) 1707 36 5710
Chris PriceSenior Programmer
Roche Products Limited
Welwyn Garden City, UK
+ 44 (0)1707 36 5801
Michelle ZhangSenior Statistical Programmer Analyst
Genentech, Inc.
South San Francisco, CA, USA
650-225-7414
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