Master DM for CR

Master Data

Management for

Clinical Research

Xavier Gobert Business Unit DirectorConsulting ServicesBusiness & Decision Life Sciences

Tel +32 2 774 11 00 Fax +32 2 774 11 99

[email protected]

Sint-Lambertusstraat 141 Rue Saint-

Lambert1200 Brussels

www.businessdecision-lifesciences.com

Peter Van ReuselBusiness Unit DirectorCRO ServicesBusiness & Decision Life Sciences

Tel +32 2 774 11 00 Fax +32 2 774 11 99Mobile +32 476 54 59 17

[email protected]

Agenda

1 Introduction

2 Metadata & Data Flow

4 Processes

6 Conclusions

3 Business Rules & Standards

5 Tools

Agenda

1 Introduction


4 Processes

6 Conclusions


5 Tools

Trends in pharmaceutical R&D outsourcing

Pharmaceutical companies continue to increase the percentage of their R&D work that is outsourced

� improve productivity of their R&D efforts

� tremendous pressure to reduce costs and become leaner

Functional outsourcing /

Programmatic outsourcing / Embedded outsourcing /

…

Paradigm shift in outsourcing

Master Data Management

MASTER DATA MANAGEMENT is able to address this new challenge

� About MetaData: Data about the containers of data

� Orchestration of business rules & external standardsusing technologies adapted to processes to transform data into an enterprise asset that yields business value for the organization.

1. Expand data quality metrics to measure accuracy,

conformity, integrity of clinical information,

2. Efficiently exchange data between partners,

3. Be compliant with regulations

Agenda

1 Introduction


4 Processes

6 Conclusions


5 Tools

Metadata & Data Flow

1

2

1

2

3

4

4

Metadata & Data Flow

� The Data Standards Library used to generate metadata

� Generating study metadata by selecting CRF templates from the Data Standards Library

� Study Metadata Repository is used to measure the consistency of metadata // Study metadata is sent to external or internal partners for database build

� After study build, data and metadata will be compared against the Study Metadata Repository & validated against the Data Standards Library

Agenda

1 Introduction


4 Processes

6 Conclusions


5 Tools

Business Rules & Stds : Data Standards Library (DSL)

� DSS contains :

� Standard CRF templates

(CDASH)

•using CDASH metadata

•with clustered SDTM

metadata

•annotated for CDASH & SDTM

� Metadata definitions (SDTM,

Therapeutic area)

•SDTM Standards

� Therapeutic Area Standards

used to create study metadata

Business Rules & Stds : Standard CRF Templates

� Standard CRF Template Library :

� using CDASH metadata

� with clustered SDTM metadata

� annotated for CDASH

� annotated for SDTM

• CRF template using CDASH annotations






Standard CRF Templates - CDASH







METADATA DEFINITIONS

DATA STANDARDS

LIBRARY

STANDARD CRF TEMPLATE

SDTM 1.2

SDTM IG 3.1.2

THERAPEUTIC

AREA METADATA

Business Rules & Stds : Metadata Definitions in DSL

5 levels of metadata

SDTM IG 3.1.2 and TA metadata stored in

the same physical tables

Business Rules & Stds : Metadata Definitions in DSL

Table Metadata

Purpose

Keys

Structure

Class

Dataset Label

Dataset Name

Variable Metadata

Origin

Comment

Controlled Terminology

Type

Variable Label

Variable Name

Role

Value Level Metadata


Comment

Type

Label

Value

Source Variable

Origin

Code TextCode Value


Computation Method

Reference Name

Computational Algorithms

Metadata Definitions : Table metadata

Metadata Definitions : Variable metadata

Metadata Definitions : Variable Level metadata

Metadata Definitions : Controlled Terminology

Metadata Definitions : Computational Algorithm

Business Rules & Stds : Study Metadata Repository (SMR)

� SMR contains :

� Study specifications/parameters :

• Define Values: Study name, MedDRA & WHODrug version,

metadata versions, …

• Trial Summary Values: Age Group, Trial Indication, Planned

Number of Subjects, …

� 5 levels of metadata in physical tables

METADATA DEFINITIONS

� Visit schedule and selected CRF templates

VISIT SCHEDULECRF TEMPLATES

� 5 levels of metadata in physical tables

� Study specifications/parameters :

• Define Values: Study name, MedDRA & WHODrug

version, metadata versions, …

• Trial Summary Values: Age Group, Trial Indication,

Planned Number of Subjects, …

� Visit schedule and selected CRF templates

� Possibility to add study specific non-standard specifications

Agenda

1 Introduction


4 Processes

6 Conclusions


5 Tools

Processes : Build Study Master File

� Generation of study metadata

requires :

� visit schedule and CRF

templates

� study parameters

Processes : Insure Consistency

� Study Metadata Repository is used to measure the

consistency of metadata between studies

� Comparison reports

� study metadata vs DSL

� study metadata

across SMR

� 3 different statuses :

� Ok : metadata present in Data Standards Library

� Study Specific : metadata not present in the Data Standards Library

� Difference : difference in the metadata value

Comparison with the Data Standards Library

Comparison across studies

� 2 different statuses:

� Ok : no metadata conflict

� Difference : difference in metadata value between

studies

� Study metadata specifications

� define.xml (5 levels of

metadata)

� list of CRF templates

� visit schedule

Processes : Distribute Master File

� Study metadata is sent to external or internal partners for

database build

� After study build, data and metadata will be:

� comparison against the Study Metadata Repository

� validation against the Data Standards Library

Processes : Insure Quality

Comparison of study data and metadata

� 4 different statuses :

� Ok : no conflict in metadata

� Difference : difference in the metadata value

� Missing : metadata not in study data

� Addition : metadata not in study specifications

Validation Report

Validation Report

Agenda

1 Introduction


4 Processes

6 Conclusions


5 Tools

Tools & Technologies

Should be Implemented in a controlled environment based on public and recognized standards in open and

interoperational formats

� SAS Drug Development, Clinical DevelopmentCenter from Phase Forward, Life Sciences Data Hub from Oracle

� Integrate CDISC standards

� Use XML, Java

Agenda

1 Introduction


4 Processes

6 Conclusions


5 Tools

Conclusions

Instituting a comprehensive Master Data Management program helps an organization actively manage their data

as an enterprise asset

� Maximize re-usability and increase efficiency of data management processes

� Building a foundation facilitating future efficiencies (eProtocol, Data as a Clinical Data Repository, …)

� The use of the study metadata repository to generate studyspecifications and to control consistencies across studies

� Similar process can be designed for analysis data or preclinical data

Conclusions

Innovation based on harmonization and standardization is the most efficient way to bypass this way of thinking

business

Thank you for your attention

Documents

Master DM for CR