Standards Driven Innovation

Frederik Malfait

IMOS Consulting GmbH, Hoffmann-La Roche AG

PhUSE Annual Conference 2014

Managing Standards

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Data Standards Value Proposition

• Standards are increasingly mandated – You have to do it anyway

– You can as well make the best of it

• Define once, use many times

• When submitting data and analysis results, standards can ensure – Compliance

– Consistency

– Correctness

• Have a repeatable process, do things faster

• Make data semantically meaningful – Precisely define models and content

– Store semantics with the data, so it can be remembered

– Supports understanding and transparency of the data

– Semantically align and integrate data from differen^t sources

• Standards based automation – Store data standards in a Metadata Registry

– Workflow automation

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4

Pieces of the Puzzle

Standards Management

Standards Governance

Standards

MDR

Standards Automation

Content

Management

Information

Architecture

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Models and Standards

M3: Meta-Meta-Model

M2: Meta-Model

M1: Model

M0: Model Runtime Data

CDISC

ISO 11179

RDF

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ISO 11179 Standard for Metadata Registry (MDR)

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RDF Meta-Model Schema

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RDF CDISC Schema

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Model Instantiation: SDTM

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Example - SDTM

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Example - SDTM

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Example - SDTM

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Example - SDTM

Roche Implementation

• Integrated data standards Roche and Genentech 2010

• MDR with operational metadata – Enterprise level application, fully validated system

– First production release 2011

• MDR components – Browser, web services, search, item level versioning

• MDR concepts – Prepared first half of 2013

– To be integrated in standards development over the next few weeks

• Model driven capabilities (2014-2015): use RDF to specify – Model driven read/write web servies + XSLT engine

– Model driven search

– Model driven validation rules

– Model driven UI

– Model driven security

• MDR used by other parties in Roche – Master data management

– Configuration of integrated document managment systems

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PhUSE CSS Semantic Technology WG

• Existing CDISC Standards in RDF – CDASH

– SDTM

– SEND

– ADaM

– Controlled Terminology

• Protocol and Schedule of Activities in RDF

• Analysis Results Metadata in RDF

• Link to EHR – Co-lead from CDISC

• Regulatory Guidance in RDF – Co-lead from FDA

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MDR Driven

Study Workflow Automation (SWA)

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Clinical Data – Future State

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External

Knowledge

Sources

Study

Design Protocol

Data

Analysis

Plan

Data

Collection

Design

Data

Collection

Data

Tabulation

Data

Analysis

Clinical Study

Report

Submission

Clinical

Development

Plan

Standards

& Metadata

Repository

Standards & Metadata Repository: Integrated Workflow

Building out the vision, step by step

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Standards can help drive automation of key clinical processes…

Automate existing information flow

MDR

Experiments

EDC Build

SDTMv

Key Benefit

Information

Standards

Extension

Study

Workflow

Automation

Roadmap

SoA

Submission

Study

Design Protocol

Data

Collection

Design

Data

Collection

Data

Tabulation

Data

Analysis

Clinical Study

Report

Standards

& Metadata

Repository

Data

Analysis

Plan

Clinical

Development

Plan

SWA Experiments Status

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Experiment Description Status

Exp. 1 Create a schema for a machine-readable SOA. Complete.

Exp. 2 Upload a SOA into the GDSR. Complete. Includes representation

of SOA in Word and HTML.

Experiment Set A: Creating a machine-readable SOA

Exp. 3 Use the GDSR to generate an operational annotated eCRF

of the Global Data Standards.

Complete.

Exp. 4 Use the GDSR to generate submission-ready annotated

eCRFs of the Global Data Standards.

Complete.

Exp. 5 Use the GDSR to generate ALS templates including edit

checks of the Global Data Collection Standards.

Complete (excluding edit checks).

Exp. 6 Develop a tool that will produce a mapping specification

and corresponding programming environment-independent

SAS program that transforms collected data to SDTMv.

Ongoing.

- Code list alignment opportunity

identified.

- Reuse of data element mappings

across forms

- Potential to generate SDTMv

annotations from mappings.

- Platform-independent

Experiment Set B: Leveraging Global Data Standards

SWA Experiments Status

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Experiment Description Status

Exp. 7 Design an interface that will enable the

definition eCRFs from Global Data Standards

or definition of new eCRFs.

First SME workshop completed.

Process analysis ongoing.

Exp. 8 Design an interface that will enable the

definition a non-CRF File Format

Specification.

First SME workshop completed.

Process analysis ongoing.

Exp. 9 Design an interface that will enable the

definition of a Visit Form Matrix.

First SME workshop completed.

Process analysis ongoing.

Exp. 10 Use the GDSR to generate a study-specific

ALS template from study-level metadata.

First SME workshop completed.

Process analysis ongoing.

Exp. 11 Use the GDSR to generate a non-CRF File

Format Specification from study-level

metadata.

First SME workshop completed.

Process analysis ongoing.

Experiment Set C: Expedite the Study Build

Schedule of Activities - SDM XML

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Schedule of Activities

22 Schedule of Activities

Web

Service

Operational CRF and Submission CRF

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Data

Collection

Web

Service

Rave Architect Loader Spreadsheet

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Data

Collection

Web

Service

Edit Checks

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Data

Collection

SDTM Transformations

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Data

Collection

Web

Service

Data

Tabulation

Source

Data Target Data

Mappings

SDTM Mappings

SDTM Transformations

• Reuse of existing data standards – Rave source data elements

– SDTM target data elements

• Generate easy to understand SAS code

• Maximize platform independent code

• Support multiple target platforms

• Possible extensions – Generate mapping specifications

– Generate conformance checks

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Schedule of Activities

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Arms and Epochs

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Planned Activites

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Time Points and Timing

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SoA Instance

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SoA Instance

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Findings

• Full protocol too large to attack at once

• Schedule of Activities key for downstream automation

• Move data collection standards from forms to modules, research concepts, and

data elements

• Hard, but necessary for first release – Handle protocol amendments

– Integrate with data standard request process for new items

– Enterprise integration (App, MDR, EDC, data platform, CTMS etc,)

• Business value – Less on the protocol side

– Massive on automated EDC build

– Massive on automated SDTM transformations

• Later stages – Link SoA to cost information

– Integrate endpoints and data analysis standards

– Component based authoring

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Protocol and SoA Concepts

• TransCelerate Common Protocol Template – Human readable protocol

– Endpoints

– Technology sub-team

• CDISC Protocol Concept List – Excel list, need to accellerate progress

– Started with concept comparisons in different settings

• PhUSE – Prepared first half of 2013

– Integrated in standards development, second half of 2014

• All these activities complement each other

• Concept Management is a key component to be successfull

• End to end standards – Create CDISC standards for TA endpoints and activities

– Link activities to data collection collection standards and metadata

– Link endpoints to data analysis standards and metadata

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Questions ?