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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

2 Genentech Inc. Confidential

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.


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


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


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


Tasks Required for CDISC Implementation

Intelligence gathering

Documenting standards

SDTMModeling of CRFsControlled TerminologyConversion SpecificationsConversions

ADaMAnalysis Database DesignMetadata and Specifications StructuresDerivations

Electronic submission to FDA


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


SDTM

Modeling

Controlled Terminology

Documentation

Conversion Specifications

Conversions


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


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


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




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.




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.


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.


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


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


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)


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


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


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


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


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


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


ADaM

Analysis Database Design

Metadata and Specifications Structures

Derivations


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


ADaM Challenges

Metadata documentation

Vertical structures

LOCF (last observation carried forward) derivations

Analysis flags

Addition of rows versus columns


ADaM Metadata Documentation

Derivation text guidelines

Specifications structure decisions


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 "=.".


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


ADaM Metadata Columns

Dataset MetadataNameDescriptionLocation StructurePurposeKey VariablesDocumentation (e.g., Stat Plan, Reviewers’ Guide)

Variable MetadataNameLabelTypeControlled Terms or FormatsSource or Derivation Method


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


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)


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


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


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


E-Sub

SDTM

ADaM


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


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


Project Assessment

Efficiencies

Conclusions


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


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!?


Acknowledgements

GenentechIan FlemingLauren HaworthSandra Minjoe (formerly)Rajkumar SharmaPeggy WoosterSusan Zhao

RocheFrederik Malfait

I3StatprobeChakrapani Kolluru

PharmaStatJohn BregaJane Diefenbach


Contacts

Patricia L. GerendSenior Manager, Statistical

Programming & Analysis

Genentech, Inc.

South San Francisco, California, USA

[email protected]

650-225-6005

Olivier LeconteProgramming Team Leader

Roche Products Limited

Welwyn Garden City, UK

[email protected]

+44 (0) 1707 36 5710

Chris PriceSenior Programmer

Roche Products Limited

Welwyn Garden City, UK

[email protected]

+ 44 (0)1707 36 5801

Michelle ZhangSenior Statistical Programmer Analyst

Genentech, Inc.

South San Francisco, CA, USA

[email protected]

650-225-7414


Questions?

Documents

CDISC Implementation on a Rheumatoid Arthritis Project ...cdiscportal.digitalinfuzion.com/CDISC User Networks/North America... · CDISC Implementation on a Rheumatoid Arthritis Project