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CDISC Implementation on a Rheumatoid
Arthritis Project Partnership
Patricia Gerend, Olivier Leconte, Chris Price, Michelle Zhang
Genentech, Inc. and Roche Products Limited
September 2009
Genentech Inc. Confidential
CDISC Background
 CDISC: Clinical Data Interchange Standards Consortium
 Founded around 1997
 Started by biotech / pharma staff
 Common standards would make sponsors more efficient
 Common standards would simplify FDA reviewers’ jobs
 Used nationally, somewhat internationally
 Used by industry, academic, coop, and regulatory groups
 Common standards would accommodate cross-company, crossmolecule monitoring
 Many CDISC branches
 SDTM (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
 Rheumatoid Arthritis new molecule
 Several new clinical studies getting started
 Decision to do all work on Roche system
 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, 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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Tasks Required for CDISC Implementation
 Intelligence gathering
 Documenting standards
 SDTM
 Modeling of CRFs
 Controlled Terminology
 Conversion Specifications
 Conversions
 ADaM
 Analysis Database Design
 Metadata and Specifications Structures
 Derivations
 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 Forum
 Occurs approximately quarterly
 Many SF bay area bio-pharm companies represented
 CDISC organization speakers
 Cross-pollination of ideas/approaches
 Discussions w/ internal staff versed in CDISC
 Reading CDISC guides (yes, including the 299-page
SDTM-Implementation Guide [IG]!)
 Well-organized
 Comprehensive
 Good examples
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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
 X_ for Interventions (example, XP for Previous Procedures)
 Y_ for Events (example, YI for Previous Immunizations)
 Z_ for Findings (example, ZJ for Tender and Swollen Joint Counts)
 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 Modeling
 For dates, Findings domains use xxDTC while Interventions and
Events domains use xxSTDTC/xxENDTC.
 User-defined domains are named Xx for Interventions, Yx for
Events, and Zx for Findings.
 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 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
Added VSPOS
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Etc.
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Controlled Terminology
 Two Controlled Terminology (CT) documents:
 CDISC organization
 Project
 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
C49503
C66767
CDISC
Preferred
Term
CDISC
Synonym(s)
Codelist
Extensible
(Yes/No)
Codelist
Name
CDISC
Submission
Value
Action Taken DOSE
with Study
INCREASED
Treatment
CDISC
Definition
NCI Preferred PreTerm
release/Produ
ction
DOSE
Action Taken Medication
Dose
INCREASED with Study
schedule
Increased
Treatment
modified either
by changing
the frequency,
strength, or
amount. (NCI)
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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
Dom Variable
ain
Seq
Label
Original Value
CDISC Std Value
AE
AEOUT
Outcome of RESOLVEDAdverse
NO
Event
SEQUELAE
AE
AECAT
Category for INFUSION
Adverse
RELATED
Event
REACTION
LB
LBTEST
1
Lab Test or BLOOD
Examination GLUCOSE
Name
GLUCOSE
LB
LBTESTCD
1
Lab Test or GLUC
Examination
Short Name
GLUC
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RECOVERED/RES
OLVED
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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 Log
 Put where accessible by whole team
 Include columns indicating problem, who needed to solve it, and
resolution
 Refer to it often when making decisions to ensure consistency in
project
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Issues Log Example
Issue Detail
Issue Type CRF Page / Raised by
Domain
For QS pages, Annotated Multi
change values of CRFs
QSEVLINT to
ISO8601 format
Chris Price
Actioned by
Actioned
when
Resolution
Comments
Patty Gerend
7/24/2008 Resolved
Status
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Raised when
7/15/2008
n/a
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SDTM Conversion Specifications
 While many conversions are not difficult (e.g., variable renames), 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
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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 members
 Project programmers can see entire picture of data derivations
 Project programmers can participate in conversions
 All data conversions/derivations are in one programming language
with programs residing in one location to facilitate audit trail
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ADaM
Analysis Database Design
Metadata and Specifications Structures
Derivations
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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 recreation 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 document
 2-Variable List document
 Value-level derivation info was embedded into the variable
derivation cells
 We have software to create this
 Familiar to FDA reviewers
 Consideration of a 3-table format for future:
 1-Data list document
 2-Variable list document
 3-Value list document
 Software may become more available to create this
 FDA will become more familiar with this in time
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ADaM Metadata Columns
 Dataset Metadata
 Name
 Description
 Location
 Structure
 Purpose
 Key Variables
 Documentation (e.g., Stat Plan, Reviewers’ Guide)
 Variable Metadata
 Name
 Label
 Type
 Controlled Terms or Formats
 Source or Derivation Method
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ADaM Dataset Structures
 Dataset structures
 ADaM structures are vertical
 Genentech has standard SAS software designed to create and
report horizontal analysis data
 Roche has standard SAS software designed to create and report
vertical analysis data
 Decision to use Roche software on Roche system
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ADaM Derivations Example
 Last Observation Carried Forward (LOCF)
 Always complicated regardless of data structure
 Used ADaM AVAL (analysis variable) and DTYPE (derivation type)
variables together to identify observed and LOCF’ed values
 In non-CDISC horizontal structures, only 1 variable was needed (it
was called LOCF)
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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 x
is 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
 Added 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 rows
 Example: Change from Baseline
 Added 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 a
new row
 Example: LOCF imputation of missing values is put into a new row
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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 have SDTM structure validated
 Plan to use Phase Forward’s WebSDM product
 Evaluation of SDTM structure adherence
 Production 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
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Efficiencies Gained
 SDTM
 First study took 8 months elapsed time
 Second study took 3 months elapsed time
 Third study took < 1 month elapsed time
 ADaM
 First study took 4 months elapsed time
 Second study took 2 months elapsed time
 Third study took 1 month elapsed time
 CDISC Overall
 No 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 studies
 Successful ADaM derivation on 3 studies, so far
 Intense CDISC learning across both companies
 Information to move forward with organization-wide CDISC
strategies
 Successes yet to come
 Electronic submission deliverables compilation
 FDA evaluation of our efforts
 Drug and indication approval!?
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Acknowledgements
 Genentech
 Ian Fleming
 Lauren Haworth
 Sandra Minjoe
 Rajkumar Sharma
 Peggy Wooster
 Susan Zhao
 I3Statprobe
 Chakrapani Kolluru
 PharmaStat
 John Brega
 Jane Diefenbach
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Contacts
Patricia L. Gerend
Chris Price
Senior Manager, Statistical
Programming & Analysis
Senior Programmer
Roche Products Limited
Genentech, Inc.
Welwyn Garden City, UK
South San Francisco, California, USA
[email protected]
[email protected]
+ 44 (0)1707 36 5801
650-225-6005
Michelle Zhang
Olivier Leconte
Senior Statistical Programmer Analyst
Programming Team Leader
Genentech, Inc.
Roche Products Limited
South San Francisco, CA, USA
Welwyn Garden City, UK
[email protected]
[email protected]
650-225-7414
+44 (0) 1707 36 5710
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Questions?
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