Papers and Presentations 

Unfortunatley, due to the global Covid-19 situation, the PHUSE Board took the difficult decision to cancel the PHUSE US Connect 2020, that was due to take place in Orlando, Florida in March.

To ensure registered attendees are still able to benefit from the planned content, the PHUSE Board and Connect Committee have worked hard to make as much of the content as possible available.

Below you will find the presentations and papers and the digital recordings can be found on the PHUSETube channel.

 

Analytics, Big Data & Statistics

AS01: R Shiny Applications Within Ophthalmology
Julia Dedic, Roche
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AR02: Big Lab Date at Statistical Reviewers' Fingertips Using R Shiny
Aditya Rachoori & Mukul Mittal, Sarah Cannon
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AS03: Reproducible Open-source Environments for Advance Analytics
Sean Lopp, RStudio
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AS04: Data Science Project (Educating for the Future Working Group)
Aldir Medeiros Filho, Independent & Sascha Ahrweiler, Bayer
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AS05: How GSK Tech is Democratizing Data Science 
Pragathi Venkata, GlaxoSmithKline
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AS06: Population PK Dataset: Basic Concepts and Challenges from the Perspective of a SAS Programmer
Namrata Pokhrel, GlaxoSmithKline
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AS07: A Programmer's Journey through the BIMO Submission Process
Raghavender Ranga, Vertex
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AS08: Using Machine Learning for Automated Monitoring of Social Media for Pharmacovigilance
Rohit Banga, Bayer
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AS10: Using RWD in the Form of EHR Data to Predict Adverse Events and Outcomes
Corey Young & Susan Olson, EdjAnalytics
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AS11: Incidence Rate – Incidence Proportion Prevalence Rate and Other Related Statistics
Nadiia Pukhliar, Intego Group
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AS12: Assessing Non-inferiority and Equivalence from Survival Data
Chintan Arora & Parveen Kumar, GenInvo
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AS13: Demystifying the Data World of Biobanks with Machine Learning
Bob Engle & Himanshu Pandya, Biogen
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AS14: Penalized Likelihood Logistic Regression for Sparse Data Using Data Priors
Martin Dörr, Clinipace
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AS15: The Importance of General Linear Models (GLMs) in Clinical Research
Ritesh Dhimmar & Venkata Ikkurthy, Bayer 
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Centralized Monitoring 

CM01: Rerouting Your Risk-based Monitoring Strategy: Overcoming Implementation Roadblocks with New Technologies and Approaches
Jonathan Andrus, Clinical Ink
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CM02: Connected Monitoring
Mike Henderson, SAS Institute
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CM03: A Journey Through End to End Data Flow
Marcin Makowski & Stayce Murray, UCB
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CM04: Working Smarter with Data to Optimize Onsite Monitoring
Nicole Stansbury, Syneos Health
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CM06: An Interactive Visualization Application for Patient-reported Outcomes with Spotfire
Matt Buckley, Roche
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CM07: Evolution of Data: From Extraction to Visualization
Ashish Kumar & James Dale, Alkermes
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CM08: Developing a Site Performance Tool Based on Centrally Available Data
Julian Ernesto Ortiz Rodriguez & Katarzyna Cieslak Janssen Research & Development 
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CM09: Building Your Central Monitoring Team: Players, Skills, Structure and Communication
Christina Dinger & Lynn Demings, PRA Health Sciences
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CM10: Custom Monitoring Solutions in R: How to Create Powerful Dashboards Using R Markdown
Kelly O'Briant, RStudio
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Coding Tips & Tricks

CT01: An Automated Code Translation Approach in SDTM Specifications Using %translate Macro
Aman Bahl & Hrideep Antony, Syneos Health 
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CT02: Fill Patterns to the Oncology Plots Using SAS
Dharmendra Tirumalasetti, Softworld & Vikram Chanduri, Vita Data Sciences
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CT03: Leveraging Metadata for SDTM QC Automation
Sunil Gupta, TalentMine
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CT04: Let Your SDTM or ADaM SAS Code Update Itself Whenever the Specs Change
Joseph Hinson, TalentMine
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CT05: Generating TFLs in R – Challenges and Successes Compared to SAS
Amol Wayker, d-wise & Andrew Miskell, Eli Lilly
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CT06: Code Generator to Check Truncated Values in Derived Datasets
Proma Mukherjee, Parexel 
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CT07: Accessible Reporting by PROC STREAM
Charumathy Sreeraman & Samundeeswari Raja, Ephicacy
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CT08: Patient Privacy – The Topmost Priority!
Shiva Kumar Gogu, Vita Data Sciences 
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CT09: SmartLog (A Unix-based Utility)
Satish Honrao, Syneos Health 
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CT10: LOCF Programming with 6-step Index Mapping
Homer Wang, Covance 
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CT11: Automation of Quality Checks for Ongoing CSR Data
Lavanya Peddibhotla & Savithri Jajam, Covance 
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CT12: Best Practices for Building and Testing Interactive Web Applications in R with Shiny
Kelly O'Briant, RStudio
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Data Handling

DH01: Managing Data Flow for Wearables, Apps and Sensors
Mayur Tripathi & Prashanth Jayadevamurthy, IQVIA
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DH02: Data Standards for eCOA in Clinical Trials
Terek Peterson & Frank Menius, YPrime 
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DH03: eSource Arising: Clinical Data Automation Possibilities Beyond Data Capture
Annelies Van Zeveren & Sofie Stynen, SGS Life Sciences
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DH04: Data Compliance: Hidden Benefits
John Ross & Priyank Patel, Accenture
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DH05: Handling Missing Data in Clinical Trials: Techniques and Methods
Naveen Kommuru & Pennidhi Karlakunta, Merck & Co. Inc
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DH06: No More Merging! (Property Graph Database Demystified)
Alexey Kuznetsov, Grunenthal 
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DH07: Migrating to LSAF – The Danone Nutricia Research Journey
Jean-Marc Ferran, Qualiance & Paul Vervuren, Danone Nutricia Research 
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DH08: Are We There Yet? Evaluating New Non-EDC Data Sources for Clinical Trial Submission Feasibility
Allison Covucci, BMS & Vijay Pasapula Gilead Sciences
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DH09: Early Detection and Prevention of Compliance Issues
Vara Sakampally, Syneos Health
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DH10: A Visualization Approach to Clinical Trials with Digital Data
Siddharth Lokineni & Vara Sakampally, Syneos Health
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DH11: Evolution of Clinical Data Disclosure and Transparency
Sneha Paradkar, Accenture 
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DH12: Validation Consistency and Conformance Checking of SEND Datasets
Isaac Mativo & Mohit Matthew, PointCross Life Sciences
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DH13: Data Load Automation
Jennifer Desai, Janssen Research & Development
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DH14: Is R Language a Reliable and Efficient Tool for Programming SAS Datasets or is it just Art for Art’s Sake?
Piotr Podlewski, Quanticate 
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DH15: Macro Trio in SAS: Dataset Comparison and Reporting
Nagaraju Mancha, Ephicacy
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DH16: Get to the Bottom of Lab Toxicity Grading: Challenges and Implementation of CTCAE Version 5
Xiaoyin Zhong, GlaxoSmithKline
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Data Standards and Governance 

DS01: Let Us Trace Back to the Beginning from the End
Madhusudhan Papasani & Swathi Kotla, Merck & Co. Inc
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DS02: What You Need to Know About Submitting Data to the FDA
Janet Low & Pritesh Solanki, Merck & Co. Inc
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DS03: Handling Duplicate Records in SDTM for Effective Resolution and Explanation in Data Submission
Varun Debbeti, Cytel
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DS04: How to Handle the Challenges for the Content of Specific Datasets in FDA Submission for HIV Drugs
Fangfang Du, GlaxoSmithKiline
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DS05: Implementing New Technical Guidance: A Sponsor Experience with the April 2018 CBER Vaccine Guidance
Liping Zhang & Sandra VanPelt Ngyuen, Pfizer
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DS06: Complexities in Adhering to Data Standards for ISS NDA Submissions
Vidya Muthukumar, IQVIA
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DS07: Common JumpStart Data Quality Findings
Aaron Belowich, IBM & Andrea Arbelaez, FDA
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DS08: CDISC Implementation Primer
Bess Leroy & Rebecca Baker, CDISC
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DS09: SUPPQUAL Datasets: The Good, the Bad and the Ugly
Sergiy Sirichenko, Pinnacle 21
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DS10: Untangling the Subject Elements Domain
Christine McNichol, Covance 
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DS11: ADaM Data in High Definition
Varia Cartledge, Alexion Pharmaceuticals
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DS12: Using Standardization and Automation to Streamline Generation of DSTM/ADaM Codelists
Eric Du, Gilead Sciences
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DS13: Implementation of Metadata Repository & Protocol Automation at Bayer Pharma
Girish Rajeev & Julius Kusserow, Bayer
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DS14: Common Sense Tips for Weaving Governance into an MDR
Ken Stoltzfus, Nurocor
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DS15: CDISC & Sponsor Terminology: Version Management For the Future
Adam De Neergaard, S-cubed
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Data Visualisations

DV01: Visualizing Clinical Operational Metrics to Enable Decision-making and Accountability
Dan Boisvert & Mike Gonzales, Biogen 
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DV02: Streaming Data to Visualize Impact Analysis
Ben Bocchicchio & Mike Henderson, SAS Institute & Sandeep Juneja, Independent
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DV03: Using Sankey Diagrams to Analyze Drug Pipelines
Tanmay Khole, Celgene
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DV04: Spotfire Patient Profile Graphs: An Abridged Effective Approach Using Scripting Languages
Anish Kuriachen & Hrideep Antony, Syneos Health
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DV05: End-to-End Interactive TLF Using R Shiny
Rohit Banga, Bayer
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DV06: An Approach to Dynamic Data Visualization with dplyr DT and Shiny
Megan Kendellen & Nicholas Masel & Richard Ryan, Janssen Research & Development 
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DV07: Visualizing Clinical Trial Design and Results in 20 Graphical Displays – Achieving 20-20 in 2020
Munish Mehra, Tigermed
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DV08: A Visual Tool to Aid Traceability
Cleopatra DeLeon & Jesse Pratt, PPD
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DV09: Interactive Adverse Event Data Visualization Using R Shiny
Aditya Rachoori & Mukul Mittal, Sarah Cannon
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DV10: Visualizing the Complexities of Genomics Datasets
Vibha Raghavan, Roche 
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DV11: Data Visualization for Medical Review
Bharath Sunkavalli, Syneos Health 
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DV12: Visualizing Tumor Response Data Using ggplot2 R Package with Examples
Christine S Teng, Merck & Co. Inc
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DV13: Best Practices for Interactive Analyses for Decision-making & Submissions: An Update from the PHUSE Data Visualization Project... Paper includes comments
Xiangyun Wang, Genentech & Zachary Skrivanek, Eli Lilly
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DV14: Volcano Plot: Demystifying Why and How
Chandana Sudini & Jyothi Nelakurthi, Merck & Co. Inc
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Machine Learning & Artificial Intelligence 

ML01: Metadata-based Auto-programming Process – Part 3: Virtual AI Assistant to Automate – TLG Generation
Harry Chen & Shobha Rani & Sumesh Kalappurakal, Janssen Research & Development 
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ML02: Natural Language Processing for Enhanced Treatment Decision
Phani Ponnapalli & Subrahmanyam Rayaprolu, Syneos Health 
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ML03: Virtual Assistant for Conversational Experience with Clinical Data
Amit Gulwadi & Srinivasan Anandakumar, Saama
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ML04: Accelerating Translational Research by Leveraging AI and Machine Learning
Christopher Asakiewicz, Stevens Institute of Technology
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ML05: Artificial Intelligence (AI)-enhanced Precision Medicine Drug Development
Mohammad Afshar, Ariana Pharmaceuticals
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ML06: A Real-world View of Cataracts
Jeffrey Headd & Walter Cedeno, Janssen Research & Development 
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ML07: Data Review of RECIST Criteria Using AI/ML – A Proof of Concept Example
Sam Tomioka, Sunovion Pharmaceuticals
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ML08: Feature of SDTM Mapping and Conversion Using NLP
Eric Weising, Ratilan Technologies & Shiva Katepalli, Sunovion Pharmaceuticals
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ML09: Machine Learning for CDISC Specification Recommendations
Brandon Welch & Preston Burns, Rho
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ML10: Smart, Digital Analysis & Reporting (A&R) Environments: They Are Upon Us
Christopher Colangelo, Pfizer
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ML11: Automation of QC Checks Within and Across Clinical Documents
Thomas Kalfas, GenInvo
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ML12: R for Deep Learning with Tensorflow with Applications in Cancer Immunotherapy
Phil Bowsher, RStudio
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ML13: Assessment of Machine Learning Methods in Coding of Concomitant Medications in Clinical Trials
Sanna Herrgard, Novo Nordisk
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ML14: Is AI Our Destiny? Data-driven Decision-making in a Dynamic Landscape Using SAS Viya
Aman Bahl & Hrideep Antony, Syneos Health 
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ML15: A Comparison of Machine Learning Algorithms for Binary Classification of Free-text Fields Using SAS R and Python
James Vasquez & Michael Rimler, GlaxoSmithKline 
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Poster Presentation

PP01: Data Wrangling – Combining and Restructuring QS-related ADaM Datasets for Pathway Predictive Modeling
Stanley Brill, Johnson & Johnson
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PP02: Educating for the Future: Data Engineering Techniques
Mohit Juneja, LyfeSciences & Renu Shukla, Johnson & Johnson 
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PP03: CDISC’s ADTTE Domain: Time-to-event Analysis from a Programmer’s Point of View
Frieder Wolff & Vitali Gering, Chrestos Concept
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PP04: Get in Your Lane! Building Swimmer Plots Using SAS 9.4
Kristi Garner & Sri Talluri, Covance 
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PP05: Titles Are a Programmer’s Best Friend
Gregory Nunn, Veramed
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PP07: Recommendations for GDPR Compliancy by the PHUSE Data Transparency Working Group GDPR Project
Arlene Coleman, Pfizer & Shannon Labout, Data Science Solutions LLC
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PP08: Employee Branding: Cherished Through Connect
Supriya Moghe, Accenture
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PP09: Be the Master for a Master Protocol Study
Ke Xiao, Boehringer Ingelheim
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PP10: Improving the Attractiveness of a CRO Career: The PHUSE CRO Summit
Christopher Hurley, MMS Holdings & Jeff Abolafia, Rho
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PP11: Adverse Event and Treatment Emergent Approaches in the Industry
Aimee Basile, Alkermes
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PP12: Oncology Data Through Customized Graphics
Hima Bhatis, ICON
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PP13: Challenges and Solutions While Mapping Laboratory Data
Ratheesh Gunda, Cytel
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PP14: Sample Size Re-estimation in Action: Design Consideration, Charter Development and Implementation
Adam Hamm, Cytel
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PP15: Making the Invisible Visible: People, Ideas and Conferences
Gayathri Kolandaivelu & Sangeeta Bhattacharya, Janssen Research & Development 
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PP16: FDA and PMDA Study Data Submission Distinctions
Kiran Kundarapu, Merck & Co. Inc & Nancy Baue, Boehringer Ingelheim
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PP17: An Overview of the OCS Core DataFitness Assessment Service
David Jacobs & Janie Ma & Rui Li, FDA
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PP18: Update on the PHUSE Standard Analyses & Code Sharing Working Group
Jane Marrer, Merck & Co. Inc
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PP19: Statistical Programming Team Contributions for RWE Regulatory Submissions
Lin Gao & Norihiko Oharu, Pfizer
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PP20: Machine Learning–AI is the Future – What Now?
Priyanka Adusumilli & Priyank Patel, Accenture
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PP21: Automation of Submission Package Quality Checks
Sendil Ramabadran, Johnson & Johnson
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PP22: Adapting to Adaptive Clinical Trials
Renu Shukla & Wenmei Ge, Janssen Research & Development
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PP23: Extracting CRF Annotations to Support Define-XML Generation and Review
Mike Tan & Richard Addy, Reata Pharmaceuticals
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PP24: Statistical Analysis in an Oncology Study for PCWG3 Patients
Tony Tian, Pfizer 
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PP25:Graphical Presentation of Patient Profiles in Dose Escalation Trials Using SAS GTL
Chiu Tsang & Rita Tsang, ICON
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PP26: Giving Back to the Community – Managing CDISC Terminology
Dorte Straarup, S-Cubed
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PP27: A Collaborative Approach to Analysis Standards
Margaret Merlino & Marleen Nijs, Janssen Research & Development 
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PP28: The Future of Pharma Through the Lens of Blockchain and AI Technology
Imran Rashid, Janssen research & Development 
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PP29: Interactive Real-world Data Analysis Using Machine Learning Algorithms
James Kim, Pfizer
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Software Demonstration 

SD01: Clarity Stack Clinical Technology – A Holistic View of Clinical Trial Operational Status and Patient Safety Data
Dirk Spruck & Lisa Jamba & Mark Gorton, Clinipace
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SD02: SPA – A Catalyst for Clinical Protocol Development
Suhas Giriraj, Ephicacy 
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SD03: Introducing the SOA App: Realize Study Automation Starting at the Clinical Trial Planning Stage
Barrie Nelson, Nurocor
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SD04: OneClinical Analytics for High Performance Trials – Moving from Data to Actionable Insights
John D'Antonio, Navitas
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SD05: CDISC Library Try-out: From Implementation to Evaluation of the API
Roman Radelicki, SGS Life Science
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SD06: Authoring ADaM Specs Within P21 Enterprise
Trevor Mankus, Pinnacle 21
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SD07: SASHelper: A Better SAS Dataset Viewer
Qian Zhao & Sairam Gorthi & Xiaoguang Yan, Janssen Research & Development 
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SD08: Atlas TDA: Clinical Data Science Platform
Iryna Kotenko & Sergey Glushakov, Intego Group 
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SD09: Regulatory Report Automation with R
Shimeng Huang & Suwen Li, Roche
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SD10: Analysis Studio: A Tool Framework for Supporting Regulatory Review
Derek Ahneman & Joy Li, FDA
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SD11: Quarterly Safety Reporting: Timely Analysis of Clinical Safety Data Using Qlik Sense Software
Yvonne Denkins, IQVIA 
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SD12: Big Data Analytics with JuliaDB
Vitali Marenny, Roche 
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SD13: Using AI and Machine Learning to Anonymize Data and Documents Together
Zach Weingarden, TrialAssure
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SD14: Life Science's Process Automation of Systems and Solutions
Lucky Singh & Ramesh Venkataramana, GenInvo
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Standards Implementation

SI01: Best Practices for Annotated CRFs
Amy Garrett, Pinnacle 21
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SI02: Mind the Gap: Raw Data Model
Julie Smiley, Oracle Health Sciences 
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SI03: FDA Study Data Submission Update
Heather Crandall, FDA
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SI04: Creating a Simplified TS Domain Using the R Shiny App
Hanming Tu, Frontage Lab
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SI05: Standardization = Automation: A Journey from Dataset Specification to SAS Code
Anastasiia Khmelnytska, Intego Group 
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SI06: How to Reduce Programming Time for ADaM Creation – Presenting STAG, a Tool-based Approach
Diganta Bose, Cytel
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SI07: Efficacy ADaMs in Oncology – Step by Step (Dataset by Dataset)
Ilya Krivelevich & Ran Xie & Simon Lin, Elsai
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SI08: Case Study: Integrated SDTM to Integrated ADaM to TFLs
Kesava Vajjala & Kishore Pothuri, Vita Data Sciences
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SI09: Lab Master List File Makes Easy
Clio Wu, FMD
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SI10: Automating the Analysis Data Reviewer’s Guide (ADRG) in Early-stage Development Studies 
Jing Su & Pranab K. Mitra, Merck & Co. Inc
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SI11: Standards Compliance – An Automated Way to Measure the Interoperability of Clinical Studies
Rammprasad Ganapathy, Genentech
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SI12: Tables, Figures & Listings Metadata and Traceability
Judith Goud & Stephen Castellano, Nurocor
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SI13: Automating Module 5 Packaging with Customizable Metadata
Dhananjay Chhatre & Kevin Miller, Gilead Sciences
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SI14: Confusing Data Validation Rules Explained Part 2
Michael Beers, Pinnacle 21
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Strategy & Programming 

SP01: Dual Roles: I’m Not Just a Programmer!
Hunter Vega & Laura Williams , CROS NT
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SP02: Project Programmer @GSK...the Journey Continues!
Eileen Ching & Raghu Kumbharathi, GlaxoSmithKline
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SP03: Programmer to SME – Career Game Changer
Charumathy Sreeraman & Divya Cetlur, Ephicacy
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SP04: Building a Deeper Bench: Writing ADaM Specifications
Kristen Harrington & Shane Rosanbalm, Rho
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SP05: Mindset for Effective Collaboration and Success in a Multinational Remote Team
Alona Bulychova, Intego Group 
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SP06: Overcoming Hurdles in Clinical Programming Project Management Using Microsoft Teams
Miten Tiwari, Janssen Research & Development 
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SP07: How to Build Compliance Mindset into a Team Habit
Xiaohui Wang, Merck & Co. Inc
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SP08: Feedback to Feed Back
Ankur Mathur, Bayer & Harper Forbes, Roche 
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SP09: Great! Not So Great! How to Capture it All in an Effective Lessons Learned Discussion
Monali Khanna, Covance 
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SP10: What Do Sponsors and CRO Programmers Think About Each Other and Their Primary Challenges?
Beena Josephine Rajeshwaran, PPD
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SP11: Documentation Applicability and Automation: How Best Practices Can Help You Achieve Best Results
Aadesh Shah & Susruth Ganoothula, GlaxoSmithKline
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SP12: A Dynamic-automated Approach to Reviewing RTF Files
Hrideep Antony, Syneos Health & Youying Yu, Eli Lilly 
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Submissions & Agencies

SA01: Real-time Oncology Review – Applicant Experience
Annie Varghese, Janssen Research & Development 
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SA02: The Misery of the Miscellaneous Folder in Module 5
Marguerite Kolb, Janssen Research & Development 
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SA03: Data Standards Used for Electronic Submission of Real-world Evidence in New Drug Applications (NDAs)
Kathy Wu & Nicole Thorne, Janssen Research & Development 
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SA04: FDA Study Data Guidance Update
Ethan Chen, FDA
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SA05: Getting It Right: Refinement of SEND Validation Rules
Kristin Kelly, Pinnacle 21
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SA06: A Case Study: Data Transparency of Clinical Trial Data
Sherry (Yong) Meeh, Janssen Research & Development 
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SA07: Regulatory Scrutiny on Data Integrity: Are You Safe?
Chris Decker & John Horton, d-wise
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SA08: An Update on CDISC-SEND Data Submissions to the FDA
Christopher Bartlett, Independent & Jesse Anderson, FDA
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SA09: The JumpStart Traceability Assessment
Tejas Patel & Sarah Ferkov, FDA
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SA10: Developing SEND for CBER – Collaborative Workstream Initiative Update
Lisa Lin, FDA & Susan DeHaven, Sanofi
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SA11: Programmatic Generation of Health Canada's Comparative Bioavailability Information Appendix B
Michael Rimler, GlaxoSmithKline 
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SA12: Managing Simultaneous Drug Submissions with Breakthrough Designations – Lessons Learned from a Programming Perspective
LiFeng Good, Pfizer 
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SA13: Getting Closer to Multi-regional Clinical Trials
Li Li Ling & Xiaohui Wang & Yuanyuan Dong, Merck & Co. Inc
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SA14: Industry Experiences Submitting Standardized Study Data to Regulatory Authorities
Janet Low, Merck & Co. Inc & Vincent Guo, Novartis
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Trends & Technology

TT01: Using Graph Technology for Metadata-driven Clinical Development
Frederik Malfait, Nurocor
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TT02: Digitalization of Clinical Study Report (CSR) Tables and Listings for Cross-report Verification
Iraj Mohebalian & Michael Badlani, Bayer
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TT03: Data Metadata and the Notion of Holons* (*Information Concepts)
Peter Tormay, Capish
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TT04: Integration of Biomarkers Data in Clinical Trials
Prakash Dev, Covance 
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TT05: Automated Units Verification, Units Conversion and Converted Results Verification Through the Unified Code for Units of Measure and LOINC
Sam Tomioka, Sunovion Pharmaceuticals
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TT06: CDISC 360: Using Biomedical Concept Metadata to Generate Case Report Forms and Dataset Definitions
Sam Hume, CDISC
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TT07: The Elusive Goal of Automation in SDTM Mapping: A CRO Perspective
Srighar Vijendra & Tyagrajan Swaminathan, Ephicacy
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TT08: Data Exchange in Clinical Trials – A Programming Perspective
Chao Wang, FMD
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TT09: All About Versioning – An Intro into Distributed Version Control
Mark Bynens & Sumesh Kalappurakal, Janssen Research & Development 
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TT10: Open Source Technologies in Clinical Research
Bruce Wienckowski & Michael Rimler, GlaxoSmithKline 
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TT11: R Syntax for SAS Programmers
Max Cherny, GlaxoSmithKline 
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TT12: R Package Oriented Software Development Life Cycle in Regulated Clinical Trial Environments
Rinki Jajoo & Yalin Zhu, Merck & Co. Inc
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TT13: Are There Any Better Options than SAS for TFLs? Yes, There R!
Niccolo Bassani, Quanticate
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TT14: R & Python for Clinical Workflows
Phil Bowsher, RStudio
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TT15: Good Cooperation Between SAS and R for Regulatory Submission
Jingyuan Chen, Roche
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TT16: Opening Doors for Automation with Python and REST: A SharePoint Example
Michael Stackhouse, Atorus
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TT17: Leveraging Python to Generate Define-XML v2.0
Kevin Miller, Gilead Sciences & Larry Sleeper, Catalyst
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TT18: Disruption is Personal: RWE and AI Changing the Game for Personalized Medicine
Arnab Goswami, ICON
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TT19: Design and Conduct of Clinical Trials: Past Present and Future. New Innovative Technology Methods
George Obsekov, Janssen Research & Development 
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