Workflow Automation and/or Optimisation 16th May 2017 2017 SDE/Basel Brochure.pdf · Switzerland...

Switzerland 2017 | PhUSE Single Day Event | 1

BaselSingle Day Event

Workflow Automation and/or Optimisation16th May 2017Novartis Offices, Basel

2 | PhUSE Single Day Event | Switzerland 2017 Switzerland 2017 | PhUSE Single Day Event | 3

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SAS® Life Sciences Analytics Framework

Better trials.Less error.


Dear Single Day Event Attendees,

It is our pleasure to welcome you to the PhUSE Single Day Event (SDE) in

Basel. This event will serve as a forum for interactive discussions, mindshare

and knowledge enhancement.

The theme for this year is “Workflow automation and/or optimisation”, to

respond to the ongoing problem of facing repetitive tasks and manual steps

that could be optimised in the biometrics environment, where fundamental

processes have not changed.

Content from our agenda will address current challenges, concerns and

issues related to key topics around technology, metadata and processes.

Out of a broad range of topics, we have selected presentations such as

Data-integration Automation; Automated Self-documenting Programs,

Automated Table Shells, Landing Zones and the Importance of Standards;

Metadata-driven Programming of Clinical Statistical Results Displays; An

Iterative Approach to Workflow Automation and Optimisation; Metadata-

driven Automation of TFL Programs; Metadata-driven Process from Global

Definition to Analysis Specifics; and Autoquery Manager. We will also have

demos such as The eRAP Tool: An Aid to Improving the Quality and Delivery

of Analysis Plans and From Standards Management to Final Analysis:

Workflow-driven Automation in Primary Research.

We hope you find this agenda interesting and will join us in making this

PhUSE SDE an enjoyable and productive experience!

We look forward to meeting you in Basel.

Best regards,

Anne & Nicolas

Basel Single Day Event Chairs 2017

IntroductionYour guide to the day

AgendaTime Title and Speaker

08:30–09:00 Registration and coffee

09:00–09:30 Welcome

Anne Mounier & Nicolas Dupuis, Novartis

09:30–10:00 Review and Highlights of the 2016 Survey of the Top 20 Biopharmaceutical

Companies' Statistical Computing Environments (SCEs)

Jean-Marc Ferran & James McDermott, JM Partners

10:00–10:30 Data-integration Automation

Florence Buchheit & Gregory Pinault, Novartis

10:30–11:00 Morning break

11:00–11:30 Automated Self-documenting Programs, Automated Table Shells,

Landing Zones and the Importance of Standards

Christoph Ziegler, Roche

11:30–12:00 Metadata-driven Programming of Clinical Statistical Results Displays

Florian Storch, Analytical Software

12:00–12:30 The eRAP Tool: Demo of an Aid to Improving the Quality

and Delivery of Analysis Plans

Michael Roughton, Novartis

12:30–13:30 Lunch

13:30–14:00 An Iterative Approach to Workflow Automation and Optimisation

Dave Iberson-Hurst, Assero Limited

14:00–14:30 Metadata-driven Automation of TFL Programs

Rafi Rahi, Shafi Consultancy Limited

14:30–15:00 Demo: From Standards Management to Final Analysis:

Workflow-driven Automation in Primary Research

Daniel Christen, SAS

15:00–15:30 Afternoon break

15:30–16:00 Metadata-driven Process from Global Definition to Analysis Specifics

Marjorie Nowak & Aurélien Guillouche, Novartis

16:00–16:30 Autoquery Manager: Self-healing Software & High-performance Reporting

Ceferino Garcia, Roche

16:30–17:00 PhUSE

Jules van der Zalm, PhUSE

17:00–17:30 Closing and Get-together

Anne MounierNovartis

Nicolas DupuisNovartis

Welcome to Basel


The Committee Speakers and abstracts

Abstract

The authors will present their recent 2016 SCE survey, on

behalf of Novartis, of the system owners of the 20 largest

biopharmaceutical companies. They will examine any observed

differences between the needs of the estimated 20,000 users of

SCEs in the companies surveyed and the industry in general. This

presentation will feature trends in the pharmaceutical industry

with regards to definition, usage and areas for development of

SCEs, whether they are commercial-off-the-shelf (COTS) or

custom solutions or a composite of interoperable solutions. This

session will also allow the attendees to share their experience,

expectations and visions of the challenges and opportunities for

the next generation of SCEs.

Biographies

Jean-Marc Ferran is an independent consultant based in

Copenhagen, with 14 years’ experience in the life sciences

industry. Prior to starting his company Qualiance, he worked

as a statistician, standards manager and director of statistical

programming at Novo Nordisk and Ferring Pharmaceuticals.

Jean-Marc has more than 10 years’ experience working on SCE

implementation projects across various sponsors in business

lead and technical lead positions. He also leads the PhUSE De-

identification Working Group. Prior to joining the PhUSE Board of

Directors in 2014 as Special Projects Director and since 2016 as

Strategic Partnerships Director, Jean-Marc was a member of the

PhUSE Annual Conference Committee and chaired the Annual

Conference in 2012 in Budapest. He recently joined forces with

James McDermott to co-found a new consultancy called JM

Partners.

James McDermott is an independent consultant based in

Oxford, UK with 25 years’ experience in the life sciences

industry. A Chartered Statistician and a Chartered Director,

James plays a leading role in shaping the biometrics sector of

our industry. James was the founder of Oxford Pharmaceutical

Sciences (now Quanticate) in 1996 and co-founder of PhUSE

with Stephen Bamford in 2004. For the last eight years, James

has worked on many systems implementation projects across

various sponsor companies. The competencies required

include systems governance, software development life cycle,

process improvement, organisational change, computer systems

validation, compliance and data integrity. He recently joined

forces with Jean-Marc Ferran to co-found a new consultancy

called JM Partners.

Abstract

Automated self-documenting programs: how to achieve SAS

programs documenting themselves without maintaining separate

documentation (e.g. Word files).

Automated table shells: how to achieve harmony between

the actual tables/listings produced with the specifications (as

defined in the statistical analysis plan).

Landing zones: this deals with project management and

documentation/information-sharing, etc.

Standards: key of automated workflows.

Biography

From 2003 to 2004, Christoph Ziegler worked part-time at the

University of Ulm in Germany in the Department of Biometrics

and Medical Documentation, developing SAS macros and

supporting statisticians within the department, where he also

completed his “Medical documentation and computer science”

studies.

In 2004 Christoph joined the pharmaceutical industry

(Boehringer Ingelheim, Germany) as a SAS programmer, where

he developed standard reporting macros. In 2005 he joined

the statistical programming group within Boehringer, where he

worked on the reporting of Phase I to III studies. In 2006 he took

over as the sub-team programming lead in cardiology within

Boehringer.

Christoph joined F. Hoffmann-La Roche (Roche) in 2006 as

a statistical programmer, where he worked as lead project

programmer for different international teams and held the

position of Biometrics Functional Team Lead for the projects in

Basel, Switzerland. In 2016, Christoph took on the position of

Manager, Statistical Programming.

Abstract

Most pharmaceutical companies invest considerable amounts of

resources to automatise the reporting of clinical study results.

Over the past years, methods such as the implementation of

CDISC, use of metadata, and tools to generate standard tables,

figures and listings, etc. have enabled a more consistent and

efficient way to report these results. Nevertheless, the demand

for analysis across trials is increasing, creating the need to

further optimise data-integration processes. Identified challenges

range from increasing volumes and novel sources of data to

the development of ready-to-use data sets on a tight schedule.

The objectives of this presentation are to introduce the Novartis

approach to address these emerging needs and solicit feedback

and knowledge-sharing. Principles of process implementation,

tools and practical case studies will be discussed.

Biography

Florence Buchheit is a Lead Programmer at Novartis. She has

13 years’ experience in the pharmaceutical industry. She has

successfully led pooling activities for multiple purposes such as

publications, Periodic Safety Update Reports (PSURs) and

US/Europe/Japan submissions. She recently led the creation and

regular updates of a CDISC-compliant compound pool involving

more than 50 studies in several indications and including

safety and efficacy domains. Florence is also involved in the

development and improvement of data-integration processes at

Novartis.

Gregory Pinault is a statistician by training and a technology

enthusiast. He started his career in 2001 as a statistician in

a CRO and then joined Novartis’ programming team in early

development projects a few years later. In 2006, he started

focusing on Modeling & Simulation’s data requirements across

several compounds and indications, multiple data domains

and the full range of development phases. In 2009, he

started to look into sustainable data-integration methodology.

Gregory now holds a Director position in Data Integration and

Pharmacometrics and leads a team of programmers, with the

goal to develop efficient and sustainable purposed integration

of clinical data at scale. His technological skills include

programming in SAS, Python, R and Javascript, under multiple

platforms and various applications, essentially oriented towards

data science and web-based applications.

Review and Highlights of the 2016 Survey of the Top 20 Biopharmaceutical Companies' Statistical Computing Environments (SCEs)

Automated Self-documenting Programs, Automated Table Shells, Landing Zones and the Importance of Standards

Data-integration Automation

09:30–10:00 11:00–11:3010:00–10:30

Jean-Marc Ferran & James McDermott, JM Partners

Christoph Ziegler, Roche

Florence Buchheit & Gregory Pinault, Novartis


Speakers and abstracts Speakers and abstracts

Abstract

For all Novartis studies, the trial statistician must produce a TFL

(Tables, Figures & Listings) shells document, which gives details

about each output required for the analysis of the study. In the

past, producing the TFL shells was often a time-consuming and

inefficient process.

Novartis has now rolled out the eRAP tool, which is designed

to streamline the production of the TFL shells document. A

demonstration of the tool will be given, along with a focus on

three key areas in which the tool has a positive benefit:

• Efficiency: helping to speed up the production of the document,

new outputs and the review & approval process

• Quality: ensuring that Novartis standard outputs are routinely

used, and identifying new outputs which need approval

• Consistency: cultivating the use of milestone and project-

specific analysis plans for consistent reporting across the

Company.

Biography

Michael Roughton has a master’s degree in statistics from the

University of Southampton in the UK. He has worked for Novartis

Oncology as a Trial Statistician for almost four years. Michael

was involved in the testing and development of a new tool for

creating analysis plans at Novartis, and also gave training to other

statisticians on how to use it.

Abstract

As pressure increases on speed of TFL delivery, how

do we programmers manage to complete multiple

studies while at the same time maintain quality? The

use of SDTM and ADaM data sets allows us to have

standard input data structures for TFL programs,

which means that we can finally automate the

generation of TFL programs. Defining standard output

templates and associated macros are the only key

missing ingredients. Automation ensures consistency

both within and across studies, and reduces the effort

required both for production and validation of TFLs.

This will then finally give us the possibility to devote

more time to new and innovative analysis to further

the reporting of clinical trials.

Biography

Rafi Rahi has been a Statistical Programmer for eight

years at Shafi Consultancy. He has worked in many

different therapeutic areas and phases of clinical

trials, and specialises in developing SAS training

materials. He has been heavily involved in risk-based

monitoring over the last few years and thinks this can

have huge potential for clinical trials to improve the

quality of data.

Rafi loves training people and has trained many

people in SAS programming over the years. He has

also produced many SAS training videos designed to

teach small modules at a time. He regularly attends

PhUSE events and loves meeting people from the

industry and sharing knowledge and ideas.

Abstract

The industry has long talked about ‘end to end’ and ‘metadata-driven

approaches’ as a way of automating and optimising workflows within clinical

research. Meanwhile, the FDA talks about issues with the data quality and

integrity of electronic submissions and has recently announced further rules

and checks that are to be performed on submissions. Sponsors want to

ensure they pass these checks. This presentation will describe significant

tangible progress that has been made in moving towards the vision against

this backdrop of increased data scrutiny, and quickly outline the vision to

align the audience. It will mainly detail the current progress, demonstrating

improved version control and visibility of the impact of change including

changes across CDISC terminology and SDTM releases. The creation of

CRFs, the automated creation of annotated CRFs and the ability to build

studies will also be discussed and demonstrated. Automated traceability and

version control will be running themes.

The presentation will then consider some immediate next steps (iterations)

regarding the automated creation of Define-XML from a study build, ongoing

work in the PhUSE CSS forum on the feasibility of the creation of SDTM

domains from captured data and the potential to link back to the protocol.

The focus will then be on the longer-term vision and next steps of including

analysis and TFLs into the picture, as the iterations in capability take us

towards the end-to-end goal.

The summary will review the presentation and discuss how this approach

can help a sponsor meet their submission goals.

Biography

Dave Iberson-Hurst has over 35 years’ industry experience, with nearly 20

years in the pharmaceutical industry, during which time he has been using

and helping develop the CDISC standards.

He is now exclusively focused on the effective management and use of

metadata within pharmaceutical companies to improve process and data

quality. He currently volunteers with the CDISC XML Technology and

Define-XML teams and is a member of the CDISC Technical Leadership

Committee. Dave also participates in the PhUSE FDA Computational

Science Symposium Linked Data & Graph Databases and Emerging Trends

and Technologies Working Groups.

The eRAP Tool: Demo of an Aid to Improving the Quality and Delivery of Analysis Plans

Metadata-driven Automation of TFL Programs

An Iterative Approach to Workflow Automation and Optimisation

12:00–12:30 14:00–14:3013:30–14:00

Michael Roughton, Novartis

Rafi Rahi, Shafi Consultancy LimitedDave Iberson-Hurst, Assero Limited

Abstract

A typical task for a statistical programmer is to perform statistical

analyses of clinical trials. They are responsible for creating

analysis data sets and developing SAS programs to display the

results of the statistical analysis – based on several specification

documents (e.g. ADS plan, SAP).

Cutting information from the specification documents and

pasting it into SAS programs is not only tedious and time-

consuming, but also error-prone and ineffective. For example, if

the text or the order of footnotes is altered, the change must be

applied to all SAS programs which use the same specifications

for a table creation.

To get around this issue, a metadata-driven programming

concept was developed as part of a master’s thesis. The idea

is to implement an automated table creation of statistical

results displays for the Clinical Trial Report by using metadata.

This presentation provides the methods and results of the

implementation.

Biography

Florian graduated with a master’s degree in medical biometry/

biostatistics from the University of Bremen earlier this year. He

gained professional experience by writing his master’s thesis on

metadata-driven programming in the Statistical Programming

department at Boehringer Ingelheim, and during several

internships. Since February 2017, Florian has been working as a

Statistical Programmer at HMS Analytical Software.

Metadata-driven Programming of Clinical Statistical Results Displays

11:30–12:00

Florian Storch, Analytical Software


Speakers and abstracts Thanks

ContributorsThank you to all our contributors for their generous support

Abstract

The whole of the industry is trying to standardise the clinical study process

including the creation of SDTM and ADaM data sets, following CDISC

guidance and health authority requirements. A possible solution is to

automate this creation using a metadata-driven approach. When dealing

with metadata, several questions arise: What is the content of the metadata

needed? What metadata flow shall be in place? How do we adapt to clinical

study needs in regard to the collected data, the reporting needs and the

study-specific needs? What are the ADaM specificities in terms of metadata:

grouping, analysis sets and criteria, creation of analysis parameters, etc.?

How do we implement them? The concept presented tries to cover all these

questions in order to present a global metadata-driven approach for the

reporting activity from collection to submission.

Biography

Marjorie Nowak has 17 years’ SAS programming experience, including more

than eight years at Novartis. She previously worked for pharma industries

and CROs doing SAS programming for data management, as well as

validation of IT systems and SAS tools for clinical trials. At Novartis, Marjorie

has been leading clinical trial/project programming activities, gaining

experience in CDISC standards and in using metadata for a more efficient

and standardised automation of SDTM and ADaM data set creation. For the

past few years, as a member of the Reporting Data Standards team, she has

been co-organising Basel CDISC-awareness workshops, providing support

to study teams for eData submissions and contributing to the definition of

Novartis CDISC-compliant data standards and metadata. Last year Marjorie

also joined the PhUSE CSS Working Group Optimizing the Use of Data

Standards, as a member of the SDTM/ADaM Implementation FAQ sub-team.

Aurélien Guillouche has nine years’ SAS programming experience at

Novartis. His first position in the pharmaceutical industry was as a statistical

programmer intern working on oncology legacy clinical studies, where he

mainly worked on the creation of Case Report Tabulations (Define package).

He became permanent and continued his work, as well as SAS programming,

to review, create and validate programs for data sets and tables, figures and

listings (TFLs) until becoming a Trial Programmer. After more than two years

in this role, Aurélien moved to the Statistical Programming Standards Group,

where he was introduced to Computer Systems Validation (CSV) and project

management. Aurélien has been project manager on several applications

including the Case Report Tabulation and the CTC Grading. He has been

working for a few years on the CDISC implementation of metadata and,

being part of the CDISC programming support, Aurélien is now part of the

CDISC workshops training team.

Abstract

The Autoquery Manager is the query system you always wanted…

but never dared to ask for: a central repository for your safety

queries that automatically generates reports and distributes the

results on schedules you define. Set it and forget it! It knows

when it gets ill and it knows how to heal itself. It runs 7 days a

week, 24 hours a day, highly available and secure. It runs high

performance, executing thousands of queries per day. It scales

easily according to your needs without sophisticated servers.

It leads a frugal life and does not require any payment of third-

party licences. It provides a modern graphical user interface for

all required maintenance tasks. It does not solve all the problems

in your life, but it definitely makes your job easier!

Biography

Ceferino García studied computer science at the University of

Zurich. In the mid-eighties he wrote his licentiate about artificial

intelligence (AI) and started a career as a software developer/

architect. He has built successful software for high-speed trading

systems in the Swiss financial sector. Today, Ceferino works

as a consultant for Hoffmann-La Roche as a solution architect

and software developer in the .NET Framework/C# domain.

He is responsible for the system architecture and software

development of the Autoquery Manager application.

Metadata-driven Process from Global Definition to Analysis Specifics

Autoquery Manager: Self-healing Software & High-performance Reporting

15:30–16:00 16:00–16:30

Marjorie Nowak & Aurélien Guillouche, NovartisCeferino Garcia, Roche

Abstract

SAS® Life Science Analytics Framework is a secure

and collaborative clinical data framework that can

harbour all clinical data information coming from EDC

systems such as Medidata Rave and Oracle InForm

and from other external systems and providers

including deliverables from CROs.

This clinical data can be transformed or matched

against organisational and study-specific clinical

data standards. The SAS® Life Science Analytics

Framework allows converting the incoming data to

standard SDTM domains by means of SAS programs,

and performing clinical data management activities.

These activities can be tied to specific triggers and

workflows.

Biography

Daniel Christen is the Technical Account Manager

for Novartis at SAS Switzerland. He joined SAS in

May 2010 as Senior Solutions Architect, to support

SAS’ life sciences customers. Daniel’s business

background is in biostatistics for clinical research and

development.

Before joining SAS, he worked for Eli Lilly and

Company in Indianapolis, IN, where he was also

responsible for coordinating all aspects of their

functional outsourcing projects and managing

relationships with business partners for Lilly’s

biostatistics department.

Demo: From Standards Management to Final Analysis: Workflow-driven Automation in Primary Research

14:30–15:00

Daniel Christen, SAS


Things to come

Looking forward

Visit the PhUSE website for dates and further information –

phuse.eu/single-day-events-2017

India SDE

Mumbai

China SDE

Shanghai

November USA SDE

Chapel Hill, NC

Belgium SDE

Beerse

June Denmark SDE

Copenhagen

USA SDE

Raritan, NJ

India SDE

Hyderabad

CSS EU

London, UK

Mon 19th-Tues 20th

July USA SDE

North Chicago, IL

May China SDE

Beijing

UK SDE

London

August USA SDE

Whippany, NJ

October PhUSE Annual

Conference

Edinburgh, UK

Sun 8th–Wed 11th

September France SDE

Chilly-Mazarin

phuse.eu

PhUSE EU CSS

Computational Science Symposium19th–20th June 2017Grange Tower Bridge Hotel, London


In your own words

NotesIn your own words

16 | PhUSE Single Day Event | Switzerland 2017

Conference Chair Jules van der Zalm, OCS ConsultingConference Co-Chair Katja Glaß, Bayer PharmaFor more information visit the PhUSE website phuse.eu/annual-conference.aspx

Annual Conference

Edinburgh Castle

Digital Innovation in Healthcare8th–11th OctoberEdinburgh International Conference Centre

Edinburgh 2017

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