EU Connect 2018

Future Forward, 4th - 7th November

Featuring 143 presentations, the 16 streams at this year’s EU Connect included content ranging from introductory presentations to those covering real business use cases, such as data mapping or missing data handling.

The most successful stream this year, with over 150 attendees, was Data Standards and Governance, traditionally the main working field for most of our PhUSE community participants. This continuing support from PhUSE members further reinforces the observation from the EU Connect 2018 Chair, Katja Glass, that “We need close collaboration and experience sharing to best prepare for future requirements and possibilities”.

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Papers and Presentations 

Best in stream winners are highlighted in bold.

Analytical Risk-based Monitoring

AR01: Developing an End-to-End Standard and Automated Risk-based Monitoring Process 
Giuseppe Di Monaco, UCB
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AR02: Welcome to Risk-based Monitoring (RBM) 2.0!
Francois Torche and Rich Davies, CluePoints
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AR03: The Importance of Being Ernest – Risk-based Approach (RBA)
William Andrew Lawton, Risk-Based Approach
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AR04: Central Statistical Surveillance in Clinical Trials
Kris Lauwers, Stefan Van Yper, Giordano Lo Regio, Janssen Research & Development
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AR05: Implementing Advanced Analytics and Machine Learning in Risk-based Strategies
Rajneesh Patil, IQVIA
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AR06: RBX: Risk-based Everything
Richard Young, Veeva Systems
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Analytics & Statistics

AS01: Overlooked Tiny Numeric Representation Errors in SAS May Lead to Substantially Misleading p-Value
Ying Zhou, Lali Sandalic, Holger Bohnemeier, David Haryanto, Jens Klement, PRA Health Sciences
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AS02: Sample Size: A Couple More Hints to Handle it Right Using SAS and R
Andrii Artemchuk, Intego Group
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AS03: Collinearity in Mixed Models
John Hendrickx, Nutricia Research
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AS04: Step-by-step Guide to Efficacy Analysis in Solid Tumors Oncology Clinical Trials
Anastasiia Tiurdo, Intego Group
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AS05: A Case Study of Fitting Time-Dependent Covariates in Cox Model Using SAS
Pankaj Kumar, GCE Solutions
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AS06: Methods for Survival Analysis with Competing Risks
Laura Marquis, Chrestos Concept
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AS07: Implementation of Data Cut-off in Analysis of Clinical Trials
Ann Croft, ARC Statistical Services
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Application Development

AD01: Exchange of SAS Programs between Local/Server based SAS and the SAS Life Science Analytical Framework
Frank Biedermann, Grunenthal
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AD02: Access Rights Management and Version Control in an Independent Programming Environment
Christian Sachara, Anke Grohl, Elena Berdichevskaya, ICON
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AD03: An Efficient way for Statistical Review using RShiny Application
Adarsh Nagare, Sameer Bamnote, Cytel
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AD04: Automating SDTM – Get it Prepared with Your Voice
Akhil Vijayan, Anish George, Anoop Ambika, Genpro Life Sciences
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AD05: Programming Development and Validation Tracking Application “iTrack”
Adrien Michon, Jennie McGuirk, ICON
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AD06: Assembling PDF Reports with PROC Documents
Sven Prasse, Entimo
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AD07: Post-Processing PDFs with Ghostscript
Jonathan North, S-Cubed
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Career Development

CD01: Collaborative Working Groups - Why you Should Take Part!
Beate Hientzsch, HMS Analytical Software
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CD02: Path of Change – From Market Research to Clinical Trials
Eniko Bront, Cmed
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CD03: You'll have to Check my Calendar: A Newbie's Insights into the World of Pharma
Rohan Palanganda, Roche
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CD04: Accelerate Your Career: Build Your Personal Brand
AJ De Montjoie
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CD05: The Role of a Project Progarmmer within GSK
Nadeem Gul, Sheetal Patel, GlaxoSmithKline
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CD06: Disruption Leads to Innovation: We Must Learn New Things!
Chris Decker, Ali Dootson, d-Wise
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CD07: Managing the Impossible: CRO Oversight
Detlef Nehrdich, Waife & Associates
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CD08: The Generation Game – The X, Y and Z of Line Managing Clinical Data Scientists
Diana Stuart, Veramed
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CD09: Do's and Don'ts for the First Time Line Manager - How to Maximize Your Chance of Success
Hannes Engberg Raeder, PRA Health Sciences
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CD10: Graduate Recruitment and Placement Experience
Nicola Davies, Amadeus Software
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CD11: How Do You Determine # SDTM’s ADaMs and TLFs and their Impact on Outsourcing?
Roy Ward, PPD
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Coding Tips & Tricks

CT01: Getting Organised for SAS Macro Development: Hints, Tips and Tools for a Structured Approach
Iain Humphreys, PRA Health Sciences
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CT03: The Lasagna Plot
Soujanya Konda, GlaxoSmithKline
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CT04: The Data Step Crystal Ball: Observing Future Rows
Michael Allie, AstraZeneca
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CT05: Do Loops in Proc DS2
Fred Ross, Rachel Linacre, George Allerston, Quanticate
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CT06: Time-management: Programming Techniques to Positively Impact Runtimes
Ian Sanderson, IQVIA
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CT07: Make Your Macro Great from the Very Beginning
Yuliia Bahatska, Syneos Health & Vladlen Ivanushkin, DataFocus
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CT08: Your Date or Mine?
Melanie O'Neill, PPD
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CT09: Please Put Your Data in an Upright Position – or How to Tackle Verticalisation
Kristina Zweier, Clinipace
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CT10: Big Data Set Seen as a Big Problem and How to Deal With Them
Daniil Shliakhow, Intego Group
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CT11: Fancy Data Visualisations Without Additional Toolkits
Kirsty Parker-Hodds, Veramed
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CT12: Increase Defensiveness of Your Code: Regular Expressions
Valeriia Oreshko, Covance & Daryna Khololovych, Intego Group
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CT13: Macro Variables and the Many Ways to Create Them
Sarah Berenbrinck, Berenbrinck
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CT14: Adjusting for Daylight Saving Times
Guido Wendland, Grunenthal
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CT15: Optimising data sets using the most succinct PROC SQL code possible (Paper not available)
Jason Rudd, PPD
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Data Handling

DH01: AstraZeneca Early Clinical Biometrics Journey into Integrated Clinical Data Environment
Ruhul Amin, Per-Arne Stahl, AstraZeneca
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DH02: Data Engineering Project (Educating For the Future PhUSE Working Group)
Guy Garrett, Achieve Intelligence and Bev Hayes, Janssen Research & Development
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DH03: ValueListDefs MethodDefs and WhereClauses: How to Define Data Traceability in a Metadata Repository
Judith Goud, Nurocor
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DH04: Text as Data in the Context of Anonymising Clinical Study Reports
Lukasz Kniola, Biogen and Woo Song, Tim Perin, Xogene
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DH05: Adverse Drug Reactions on Social Media: Bias and Limitation
Erwan Le Covec, Stephane Chollet, Keyrus Biopharma
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DH06: Can You Cut It? Implementing the Data Cut-off
Lewis Meares, Phastar
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DH07: Handling CRF Design Changes in a Live Environment
Andre Snelting, Nieke De Ruiter, OCS Life Sciences
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DH08: Data Transparency – Breaking Down Data Silos for Improved Insight
Peter Tormay, Anna Berg, Capish
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DH09: Automated Creation of Submission-ready Artifacts
Silas McKee, Lourdes Devenney, Accenture
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Data Standards and Governance

DS01: SDTM Legacy Data Conversion
Markus Stoll, Merck & Laura Phelan, Angelo Tinazzi, Cytel
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DS02: Practical Benefits of EC: Building a Comprehensive Exposure Story
Zegher Vereecke & Donald Benoot, SGS Life Sciences
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DS03: The Oncology Specific Domains TU TS and RS: What to Know as a Statistical Analyst
Frieder Wolff & Vitali Gering, Chrestos Concept
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DS04: The Use of SDTM Standards for Non-submission Purposes
Paul Vervuren, Nutricia Research & Lieke Gijsbers, OCS Life Sciences
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DS05: Conformance Rules – How to Follow and Comply when Specified by Multiple Sources?
Francis Dsa, Novo Nordisk
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DS06: Best Practice for Explaining Validation Results in the Study Data Reviewer’s Guide
Kristin Kelly & Michael Beers, Pinnacle 21
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DS07: Generating Define.xml and Analysis Result Metadata using Specifications Datasets and TFL Annotation
Mark Crangle, ICON
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DS08: SDTMIG v3.3: New domains - new benefits
Nick De Donder, Business & Decision Life Sciences
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DS09: Incremental Changes: ADaMIG V1.2 Update
Terek Peterson, Covance & Brian Harris, MedImmune
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DS10: Cost-Benefit Analysis of using CDISC Standards in Trial Set-up
Jasmine Kestemont, Innovion
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DS11: Updates on Validation of ADaM Data
Max Kanvesky & Sergiy Sirichenko, Pinnacle 21
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DS12: How to Manage Changes to CDISC Standards
Sune Dandanell & Mikkel Traun, Novo Nordisk
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DS13: CDISC Standards: Creating Clarity Out of Confusion
Kit Howard, CDISC
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Data Visualisations

DV01Turning Fantasy into Reality – Creating Custom Data Visualisations for Future Reporting
James Diserens, Veramed
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DV02: Animate Your Data!
Kriss Harris, SAS Specialists & Richann Watson, DataRich Consulting
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DV03: A Combined Waterfall and Swimmer Graph
Sanjay Matange, SAS Institute
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DV04: Forest Plots in Survival Subgroup Analyses
Michael Brockmann & Julia Benzing, Chrestos Concept
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DV05: Using plotly.js to Visualise SAS Data
Volker Harm, Bayer
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DV06: Streaming Tables and Listings by PROC STREAM
Charumathy Sreeraman & Samundeeswari Raja, Ephicacy
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DV07: Effective Data Modelling for Effective Data Visualisation
Eva Kelty & Peter Tormay, Capish
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DV08: Bridging the Visual Analytics-EDC Gap to Accelerate Centralised Data Monitoring
Luis Torres, Covance
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DV09: Data Visualisation in a Risk-Based Approach to Support Decision Making
Joke De Wever, Sabine Jonas & Irina Sargsyan, Business & Decision Life Sciences
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DV10: Safety Signal Detection Visualisations using Spotfire including R
Michaela Mertes, Roche
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DV11: Adverse Event Data – gRaphically
Lina Rajput & Prajakta Chitale, Cytel
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Machine Learning

ML01: Introduction to Machine Learning
Nicolas Dupuis, d-Wise & Kevin Lee, Clindata Insight
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ML02: Want to Get Started with Deep Learning? Prototyping a Deep Learning Image Classifier
Thomas Ellebaek, Ferring Pharmaceuticals
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ML03: Application of Artificial Intelligence Methods in Pharma
Azad Dehghan, DeepCognito
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ML04: Automate an extended-SDTM Mapping in Clinical Trials using Machine Learning
Fanyi Zhang, Michael Elashoff & Hrishikesh Karvir, Medidata
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ML05: Machine Learning Driven Clinical Insights – What When and How?
Linda Preve, Accenture
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ML06: Machine Learning with SAS
Cathal Gallagher, d-Wise
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ML07: Handling Missing Data in Clinical Trials Using Topological Data Analysis
Sergey Glushakov, Iryna Kotenko & Andrey Rekalo, Intego Group
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ML08: Serious Adverse Events Identification: A Machine Learning Approach
Jingshu Liu, Laura Katz & PJ Allen, Medidata
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ML09: A Learned Approach to CDISC Specifications
Steve Noga, Brandon Welch & Jeff Abolafia, Rho
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Poster Presentation

PP01: Template-Based File Generation
Christoph Baumer, inVentive Health Clinical
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PP02: Guideline for Submission Ready aCRF
Christina Paul, Grünenthal, Stefanie Sturm, HMS Analytical Software & Eva Bervar, Bayer
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PP03: Journey to the Centre of the Protocol: Explorations in Text Mining for Study eCRF Development
Rodrick Bower, Roche
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PP04: Unique Technology for the Future
Catharina Dahlbo, Henrik Drews & Amanuel Workneh, Capish
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PP05: New Approach to Graph Databases
Catharina Dahlbo, Henrik Drews & Anna Berg, Capish
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PP06: How to write ADaM Specifications Like a Ninja
Caroline Francis, Sunshine Programming
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PP07: Designing and Implementing an 'Information Hub' for Statistics and Programming
Bradley Harris, GlaxoSmithKline
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PP08: Navigating the Mountains and Valleys of Resource Demand; a Roadmap for the Future
Lucy Hoch & Yvonne Jangvik, AstraZeneca
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PP09: Data Validity Frame Concept
Andras Kasa, Grünenthal
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PP10: DV Domain and the Classification of Protocol Deviations
Norbert Martini, Christina Lange & Hagen Reis, ICON
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PP11: Robust Test Data
Jennie McGuirk, ICON
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PP12: Need for Speed – Accelerating Centralised Monitoring
Parag Naithani & Pramod Chittam, Ferring Pharmaceuticals
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PP13: Overall Survival Adjustment to Mitigate the Impact of Crossover from the Control Arm to the Experimental Arm. The Two-stage Method
Antonio Nieto & Javier Gómez, PharmaMar
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PP14: Subpopulation Treatment Effect Pattern Plot (STEPP) for Categorisation of Baseline Characteristics
Andrea Parodi & Dmitri Petratchenko, Valos
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PP15: Stop Copying CDISC Standards
Craig Parry, Syne qua non
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PP17: The 5R’s of Innovative Digital Technologies
Anna Hedman, Tracey Lavery & Charlotte Lomberg, AstraZeneca
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PP18: How to Automate Validation with Pinnacle 21 Command Line Interface and SAS®
Amy Garrett & Aleksey Vinokurov, Pinnacle 21
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PP19: Cool Stuff in SAS Studio
Alastair Scarlett & Cathal Gallagher, d-Wise
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PP20: Producing Panel by Dose Plots Using SGPANEL and SGRENDER
Rajashekar Reddy Sunkara, ICON
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PP21: Program Validation – Care for a Black Sheep
Andrea Weber, PRA Health Sciences
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PP22: Tree Traversal: Why You Should Never Transpose in PROC SQL
Jessica Whittaker-Dixon & Will Greenway, Quanticate
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PP24: First Aid Kid for an Observational Study
Yuliia Bahatska, Syneos Health & Vladlen Ivanushkin, DataFocus
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PP25: Data Mapping using Machine Learning
Daniel Choi, Eric Bolender & Stijn Rogiers, SAS Institute
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PP26: The « CDISC Stupidario » (the CDISC Nonsense)
Angelo Pinazzi, Cytel
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PP27: Semantic integration for Healthcare and Pharma Enterprises
Alexis Dimitriadis & Christian Blaschke, Semantic Web Company
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PP28: Affairs in Medical Affairs
Vibhavari Inamdar, Cytel
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PP29: Streamline the Collaboration – From Whitepaper targets (TFL) to Sharable Scripts
Chris Price, Roche & Hanming Tu, Frontage Lab
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Real-world Evidence

RW01: The Salford Lung Study: Challenges and Complexities with the Reporting of Electronic Medical Record Data in a Pioneering and Novel Approach to Clinical Trials
Daniel Kwok & Jennifer Colthart, GlaxoSmithKline
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RW02: Use of HL7 FHIR as eSource to Pre-populate CDASH Case Report Forms Using a CDISC ODM API
Sam Hume, CDISC, Jeff Abolfia, Rho & Geoff Low, Medidata
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RW03: Propensity Score Matching
Stephanie Fechtner, Bayer
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RW04: RWE: What we Have Observed
Peter Grolimund, Teradata
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RW05: Enhancing the Application of Real-world Evidence with Episode of Care Analytics
David Olaleye & Lina Clover, SAS Institute
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RW06: Experiences and Strategies in Linking Real-world Data from Different Sources
Berber Snoeijer, PHARMO Institute
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Submissions & Agencies

SA01: The EMA Health Canada and FDA: Three Agencies Tackle Data Sharing, Synergies, and Differences
Jean-Marc Ferran, Qualiance
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SA02: Challenges of Submitting Electronic Study Data to Two Authorities: the PMDA & FDA
Torsten Petsching, Boehringer Ingelheim
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SA03: Standardised Data Sample: Key to Improving the Submission Strategy
Prafulla Girase & Loanna Koft, Biogen
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SA04: Do's and Don'ts of Define.xml
David Roulstone, Pinnacle 21
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SA05: How to Easily Generate Define.pdf from Define.xml – Submission Request
Ralf Goetzelmann & Endri Elnadav, Bayer
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SA06: Practical Lessons Learned from Recent NDA and BLA Submissions to the FDA
Carey Smoak, S-cubed & Lisa Brooks, Iris Statistical Computing
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SA07: Is it Possible to Make a Global CDISC Submission?
Marianne Carames, Novo Nordisk
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SA08: Lessons Learned for Successful e-Study Data Submission to the PMDA toward the End of Transitional Period
Takashi Kitahara, Novartis & Ayako Noda, Janssen Pharmaceuticals
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SA09: Modernising the New Drug Regulatory Program at the FDA/CDER
Lilliam Rosario, FDA 
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Software Demonstration

SD01: AI-powered Xbiom 3.1 Smart Curation & Transformation of Data
Kurien Abraham, Nicolas Philippe & Arpitha Hanumanthaiah, PointCross Life Sciences
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SD02: Using SAS Advanced Analytic to Gain Insight into the Opioid Epidemic
Andy Bayliss, SAS Institute
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SD03: Mapping Programs – Written by MDR
Jörn Bilow, Entimo
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SD04: Generating Tables Listings and Figures Without Any Programs
Shafi Chowdhury & Md Zobair, Shafi Consultancy
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SD05: Integrated Anonymisation with Quantitative Risk
Cathal Gallagher, d-Wise
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SD06: SHADOW: An Integrated Data and Document De-Identification Solution
Thomas Kalkas & Parveen Kumar, GCE Solutions
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SD07: New Approaches to Centralised Statistical Monitoring of Clinical Trails
Valérie Nedbal, SAS Institute 
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Standards Implementation

SI01: Top Five Challenges in Complying with CDISC Standards
Trine Danø Klingberg & Mikkel Traun, Novo Nordisk
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SI02: eSource to SDTM: Trade-offs and Pay-offs
Donald Benoot & Swapna Pothula, SGS Life Sciences
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SI03: A Critique of the Use of the Medical Device SDTM Domains in the Therapeutic Area User Guides
Carey Smoak, S-Cubed
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SI04: Optimizing the Traceability Method in ADaM Parallel Conversions
Roxane Debrus, Business & Decision Life Sciences
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SI05: Define’ing the Future
Johan Schoeman & Nicola Perry, GlaxoSmithKline
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SI06: Define.XML-centric Dataset Development
Leonid Tseytlin & Yevhen Pavlenko, CS
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SI07: Diagnostics of Technical Errors in define.xml File
Sergiy Sirichenko, Pinnacle 21
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SI08: Structured and Standardized Study Definition Drives Early Study Setup for Added Business Benefits
Djenan Ganic, intilaris LifeSciences & Tanja Petrowitsch, Bayer
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SI09: From Machine-readable CDISC Standard Specifications to the e-Protocol
Jozef Aerts, University of Applied Sciences FH Joanneum
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SI10: Implementation of a Metadata-based Approach to Statistical Planning Analysis and Reporting
Frank Freischlager & Hanspeter Schnitzer, Estimondo
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SI11: CDISC SDTM Data Automation Tool
Brinitha Sathyabhama, COGNUM Decision Solutions
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SI12: Into the Fire Linking CDISC & FHIR
Dave Iberson-Hurst, Assero
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SI13: Easing Your Pain with Biomedical Concepts
Kirsten Langendorg, S-Cubed
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SI14: APESS: A Unified Methodology for the Development and Review of Quantitative Analysis SOPs
Andy Richardardson, d-Wise & Anne Mounier & Anthony Rossinin, Novartis
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SI15: A Proprietary CDASH/SDTM-hybrid Data Model to Expedite Clinical Data Review
Lieke Gijsbers, OCS Life Sciences & Tom Van Der Spiegel, Jannsen Pharmaceutical
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Scripts & Macros

SM01: iGET it! Downloading Data from a Centralised Repository using PROC HTTP
Neville Cope, AstraZeneca
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SM02: The “Unveiled” Secrets of “Windows Batch Scripting”
Nicolas Rouille, Angelo Tinazzi & Laura Phelan, Cytel
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SM03: Guardians of the Formatting Awesome Documents Vol.1!
Robert Janisch, Bayer
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SM04: To VBA or not to VBA? That is the Question.
Piotr Karasiewicz, Quanticate
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SM05: A Free Tool to Automatically Generate User Documentation for SAS Macro Libraries
Loizos Nicolaou, Renos Kakoullis & Julien Vanwinsberghe, ClinBAY
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SM06: Speed up Evaluation by Parallelisation
Michael Weiss, Bayer
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SM07: How to Write SAS Code using Microsoft Visual Basic for Applications
Hansjörg Frenzel, PRA Health Sciences
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SM08: SAS and UNIX: Rerun your Batches in a Smarter Way
Yaroslav Haiovyi, Intego Group
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Trends & Technology

TT01: 2018 Clinical Data Landscape Survey
James McDermott & Guy Garrett, Achieve Intelligence & Danti Di Tommaso, Novartis
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TT02: SAS Life Science Analytical Framework (LSAF) in a nutritional clinical research setting
Mickaël Hartweg, Nestlé Research Centre & Justine Morlet, NESTEC
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TT03: Crowd-Sourced ADaM-Programming and Standardization: A Design Study
Holger Langkabel, Roche
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TT04: Overcoming Resistance to Technology Change: A Linked Data Perspective
Tim Williams, UCB
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TT05: Status on Adoption of Cloud Technology in the Life Sciences Industry
Anders Vidstrup, NNIT
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TT06: Breaking free from Microsoft Word 
Dominik Habel, Bayer
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TT07: The MetaDataHub - Integrating searching and presenting study descriptive information
Elisabeth Nyman, Kerstin Forsberg & Michail Doulis, AstraZeneca
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TT08: Simulating Enrollment Plans
Jim Box, SAS Institute
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TT09: Study URI
Kerstin Forseberg & Daniel Goude, AstraZeneca
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TT10: Utilization of Python in Clinical Studies by SASPy
Yuichi Nakajima, Novartis
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TT11: Clinical Trials on Blockchain
Rohit Banga & Mohit Juneja, LyfeScience
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