Metadata Management – Our Journey Thus Far Archana Bhaskaran Oncology Database Development Operations 28-Jan-2015

Disclaimer The opinions expressed in this presentation and on the following slides are solely those of the presenter and not necessarily those of Novartis. Novartis does not guarantee the accuracy or reliability of the information provided herein.

Evolution of metadata management – Beginning, Current and Future Agenda Introduction Evolution of metadata management – Beginning, Current and Future Key Benefits Achieved and Current Challenges Governance process

Introduction Present a high level overview of Novartis roadmap on metadata management and associated processes from inception to present. What is Metadata? Metadata is information about the data. Metadata describes the domains and data elements used in clinical trials Example of metadata: variable names, type, derivation algorithm, codelists, etc.

MDR Roadmap Novartis has defined an end to end process that governs the framework to define, maintain and use of metadata within a single repository to support all upstream and downstream processes and tools. Beginning: iMDR - Interim MetaData Repository (Excel spreadsheets) Current: MDR - off-the-shelf Oracle based global metadata management tool customized for Novartis Future: MDR – off-the-shelf Oracle based ‘Single Repository’ that enables the management of Global standards and study level Metadata

Metadata Hub Data Warehouse MDR Like a center screw in a wheel Data Collection (CDMS) Data Warehouse MDR Standards Development / Modeling Submission (SDTM, ADaM) Like a center screw in a wheel the Metadata (Standards) in the MDR drives all Implementation programs and data flow (hence process)

Type of metadata maintained Clinical Data Elements (CDE) Controlled Terminology (CT) Derivations and Imputations (DI) Reference Tables (Lab/Non-Lab/Questionnaires) Master Data Domains (MDD) External Partners

Metadata Management – Our Beginning(2009).. iMDR – Interim MetaData Repository Contained only global level metadata with no version control. Metadata was defined and managed in excel spreadsheets for each type of metadata. E.g. CDE, CT, etc. All files were stored and maintained in a central sharepoint location Give a high level overview of maintenance within SP

Metadata Management – Our Beginning (contd) Domain Sheet - Clinical Data Elements Tab Data Elements were defined at Domain level. One spreadsheet for every domain referred as the domain sheet. E.g. AE, CM, etc. The domain sheets contained metadata from collection through submission (SDTM) ADaM metadata was maintained separately in Excel spreadsheets. Each domain sheet contained two main tabs: Clinical Data Elements and Derivations/Imputations. Clinical Data Elements – Contained data elements from collection and submission (SDTM). Each element had 53 attributes that were defined Derivation/imputations – Contained details on the derivations for elements that were derived or imputed. Each derivation had 19 attributes that were defined.

Metadata Management – Our Beginning (contd) Domain Sheet - Clinical Data Elements Tab The Clinical Data Elements Tab

Metadata Management – Our Beginning (contd Metadata Management – Our Beginning (contd.) Domain Sheet – Derivations/Imputations Tab This sheet provides information regarding the derivations and imputations used within the data domain. Derivation: a sequence of steps, logical or computational, from one result to another. The result is a new variable. Example of this method is AGE_DRV - If Date of Birth is present and non- missing then the derived variable will be calculated as Visit Date of Visit 1 minus the Date of Birth.

Metadata Management – Our Beginning (contd Metadata Management – Our Beginning (contd.) Domain Sheet – Derivations/Imputations Tab Imputation: Creating a value when all or part of the original variable is missing. Example of this method is commonly found within dates Data collected Mar1960 Data imputed 15Mar1960

Metadata Management – Our Beginning (contd.) Controlled Terminology Defines the codelist choices that are allowed in a domain for a given CDE. Master controlled terminology was maintained in Excel sheet. Subsets (groups) of codelist values were maintained within CDMS system. This sheet contained 24 attributes for each value defined in a codelist.

Metadata Management – Our Beginning (contd.) QC measures QC checks – SAS based programs to check for the completeness of the domain sheets based on pre- established rules. Manual Review

Metadata Management – Current (2013) Metadata defined and maintained in an off-the-shelf Oracle based tool customized for Novartis. A Central global metadata repository that holds all metadata that is easily accessible to all end users The same concept from iMDR was adapted into the MDR tool. The views were customized to match the iMDR sheets for ease of review and use by end users Following key enhancements were made to the tool: Enabled workflow process for adding and approving new objects User friendly screens for codelist and lab reference tables were added Introduced online validation checks and QC reports Updated interface views for use by downstream systems

Metadata Management – Current Clinical Data Element View Derivations/Imputation View

Key Benefits Achieved Controlled and Audit trailed environment Better quality gained by having on-line checks and built in QC reports Efficiency gained in defining and maintenance of metadata. E.g. Codelist, lab reference tables, etc. User friendly data entry screens for codelists and reference tables Efficiency gained by providing improved interface views to the downstream systems Reduced manual review steps Instant access to all end users to browse metadata

Current Challenges Data entry screens for CDE not user friendly Batch load functionality requires lot of pre-processing steps due to de-normalized structure of MDR Option to subset elements and codelists does not exist Dependent objects and attributes need to be manually created. This entails 40% of the objects within the CDE Unable to manage study level metadata Transformation challenges – horizontal to vertical, handling blank records, etc. Other challenges include Versioning, lack of interface with upstream/downstream systems, ADaM metadata housed outside of MDR, etc.

Metadata Management - Future Support both Global Data Standards and Study level metadata needs Support global & study level governance workflows Enable integration with CDMS and CDISC - SHARE Enable integration with upstream and downstream systems Create views to support Define.xml requirements Integrate ADaM Metadata and Structures

Metadata Management - Future Proposed process flow

Governance workflow model Request Decision Feedback * CSU encompasses: Clinical Science Unit Business Unit (i.e. Oncology) Translational Sciences (TS) Requestor (CTH/CTL) CSU Data Standards Governance Standards Extended Team Onc. Standards - approves (from a scientific & franchise specific standpoint) requests DSG - final approval DSG - Standards Experts who approve requests from CSUs, with input from Extended Team, as required (senior representatives with a background in Clinical Science, Biostatistics, Statistical Reporting, Data Management Ext. Team - Provides adhoc expertise in specific functional area (e.g. Imaging, lab data, biomarker development) Oncology - Integrated Disease Area strategy team (IDS) Requester - Identifies new needs for data element /codelist for safety / indication standards

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