PharmaSUG 2018 Conference Proceedings

Seattle, Washington
April 29 - May 2, 2018


Advanced Analytics

AA01. Multicollinearity: What Is It and What Can We Do About It?
Deanna Schreiber-Gregory, Henry M Jackson Foundation for the Advancement of Military Medicine

AA02. Patient-Level Longitudinal Analysis Plots Showing Adverse Event Timelines and Dose Titration Levels
Keith Brown, Dataceutics, Inc.
John R Gerlach, DataCeutics, Inc.

AA03. Advanced Analytics approach to explore CENSUS Data and know the population in order to determine the best opportunities to open the new Hospital
Krystian Matusz, Permanent TSB

AA04. Calculating Restricted Mean of Survival Time
Jiuzhou Wang, ImmunoGen, Inc.
Tony Qi, ImmunoGen, Inc.

AA05. Build Models without Code with the new SAS Visual Interface
Jim Box, SAS Institute

AA06. An Investigation of Distribution Distance Measures
Charity del Sol
James Blum, UNC Wilmington

AA07. From FREQing Slow to FREQing Fast: Facilitating a Four-Times-Faster FREQ with Divide-and-Conquer Parallel Processing
Troy Hughes, Datmesis Analytics

AA08. Using Agile Analytics for Data Discovery in the Pharma Industry
Bob Matsey, Teradata

AA09. The Applications of Tolerance Intervals: Make it Easy
Marina Komaroff, Noven Pharmaceuticals, Inc.

AA10. PK Data Explained
Timothy Harrington, Dataceutics, inc.

AA11. How PK and PD analyses drive dataset designs
David Radtke, Eli Lilly and Company

AA12. Programming Support for Anti-Drug Antibody Pharmacokinetics in Therapeutic Protein Drug Development
Linghui Zhang, Merck & Co., Inc.
Jiannan kang, Merck & Co., Inc.

AA13. Challenges & Strategies in PKPD Programming
Jing Su, Merck & Co., Inc.
Jiannan kang, Merck & Co., Inc.

AA14. Statistician in my Soul. Statistical Questions from SPA
Nadiia Hryhorenko, Experis / Intego Group, LLC

AA15. Preventing premature unblinding in PK/PD related studies
Debbie Sisco, Janssen Research & Development, LLC
Alice Zong, Janssen Research & Development, LLC

AA16. Data Standards for Population Pharmacokinetic (POPPK) and Non-Compartmental Analysis (NCA)
Neelima Thanneer, Bristol-Myers Squibb

AA17. A One-Sided Fisher’s Exact Test: A Tail of Clinical Worsening
William Coar, Axio Research

AA18. An Introduction to the Analysis of Rare Events
Nate Derby, Stakana Analytics

AA19. Generating Receiver Operating Characteristic (ROC) curve using SAS Macros
Jiaying Guo, Eli Lilly and Company
Isabella Wang, Eli Lilly and Company

AA20. Statistical Analysis and Visualization of Microbiome data in Clinical Trials
Athira Sudhakaran, Genpro Lifescience India Private Ltd
Limna Salim, Genpro Lifescience India Private Ltd



Application Development

AD01. Developing Large SAS® Macro Applications
John Ingersoll, Rho

AD02. Derivations of Response Status from SDTM Domains using RECIST 1.1
Christine Teng, Merck & Co., Inc.
Lei Pang, Merck & Co., Inc.

AD03. ADINTDT and ADTTE for Survival Sweep in Oncology Studies
Christine Teng, Merck & Co., Inc.
Wenyu Hu, Merck & Co., Inc.

AD04. A Linux shell script to automatically compare results of statistical analyses
Chen Wang, Gilead Sciences, Inc.
Shu Zan, Gilead Sciences, Inc.
Joshua Conrad, Gilead Sciences, Inc.

AD05. BEACH: an open platform for building interactive and automatic analysis powered by R/Shiny
Danni Yu, Eli Lilly and Company
Michael Man, Eli Lilly and Company

AD06. Building Simulated CRF Data for Study Mock-Ups
Ashley Kesler, PPD
Hisham Madi, Syneos Health
Jonathan Duggins, North Carolina State University
James Blum, UNC Wilmington

AD07. Pinching Off Your SAS® Log: Adapting from Loquacious to Laconic Logs To Facilitate Near-Real Time Log Parsing, Performance Analysis, and Dynamic Data-Driven Design and Optimization
Troy Hughes, Datmesis Analytics

AD08. Parallel Processing Your Way to Faster Software and a Big Fat Bonus: Demonstrations in Base SAS®
Troy Hughes, Datmesis Analytics

AD09. Macro-Supported Metadata-Driven Process for Mapping SDTM VISIT and VISITNUM
Pragathi Dayananda, Chiltern/Covance
Eric Crockett, Chiltern/Covance

AD10. A Method for Independent Program Validation utilising SAS®, R and Python
Aik Hoe Seah, H. Lundbeck A/S

AD11. A Simple Method for Integrating Analysis Result Metadata into Existing Define.xml 2.0
Jeff Xia, Merck

AD12. Why SAS Programmers Should Learn Python Too
Michael Stackhouse, Covance

AD13. An Automated Macro to Compare Data Transfers
Michael Stackhouse, Covance

AD14. Automatically Configure SDTM Specifications Using SAS® and VBA
Xingxing Wu, Eli Lilly and Company

AD15. Levista - An Integrated Programming Environment with Visualization
Priya Saradha, Levstat LLC

AD16. An Efficient Tool for Clinical Data Check
chao su, Merck

AD17. Managing Table Preparation and Related Coding Task in Multilingual Settings by Using SAS Unicode Version
Kai Koo, Abbott Vascular

AD18. A time saving approach to track data issues
Aishhwaryapriya Elamathivadivambigai, Seattle Genetics, Inc.

AD21. ADaM Define.xml v2.0 Validation – The Perl Way in SAS
Yuxin (Ellen) Jiang, Alkermes Inc.

AD22. Compare Pharmacokinetic Data Submission Processes in CDISC Environment
Xiaopeng Li, PPD
Shallabh Mehta, PPD
Edward Elam, PPD

AD23. Let’s Start Something New! A Beginner’s Guide to Programming in the SAS® CAS Environment
KEVIN RUSSELL, SAS

AD24. Statistical programmer application framework with R/Shiny
Ashok Gunuganti, Pfizer

AD25. A SAS® Macro to Generate Information Rich Kaplan-Meier Plots
Chia-Ling Ally Wu, Seattle Genetics

AD26. Making the most of SAS® Jobs in LSAF®
Greg Weber, DATACEUTICS, INC
Sonali Garg, Alexion Pharma

AD27. Insights to Meticulous Clean Patient Tracking via Analytics
Siddartha Kondapally, Vita Data Sciences (a division of Softworld, Inc.)
kishore Pothuri, Vita Data Sciences, a division of Softworld, Inc.
Bhavin Busa, Vita Data Sciences (a division of Softworld, Inc.)

AD28. An Exploratory Way to Draw a Flow Diagram of Two-Arm Disposition Using SAS® 9.4 M3
Huijuan He, Gilead Sciences

AD29. Generating Define.xml from Pinnacle 21 Validator
Pinky Anandani, Inclin, Inc

AD31. Automate Clinical Trial Data Issue Checking and Tracking
Krishna Avula, Regeneron Pharmaceuticals
Dale LeSueur, Regeneron Pharmaceuticals

AD32. Automation of STDM dataset integration and ADaM dataset formation
Rinki Jajoo, Merck
William Wei, Merck

AD33. Introducing Interactive Data Step Debugger: What you can do with SAS Data Step Debugger (SAS® Enterprise Guide® 7.13)
Kevin Kim, Synteract, Inc.

AD34. Mining Mass Fragments using SAS and Python for Metabolite Identification of Antibody-Drug Conjugates
Hao Sun
Kristen Cardinal



Beyond the Basics

BB01. User-defined Functions for Processing Lab Data
Richard Allen, Peak Stat

BB02. Dosing In NONMEM Datasets an Enigma
Sree Harsha Sreerama Reddy, Certara
Vishak Subramoney, Certara

BB03. Using the SAS® ODS Excel Destination Options to Enhance Your Excel Output
William Benjamin, Owl Computer Consultancy LLC

BB04. Seeing the Forest for the Trees: Part Deux of Defensive Coding by Example
Donna Levy, Syneos Health
Nancy Brucken, Syneos Health

BB05. A Conceptual Strategy and Macro Approach for Partial Date Handling in Data De-Identification
Amy Caison, PPD
Kelsey Reppert, PPD

BB06. ADaM Integration for Summary of Clinical Safety: The 'Unique Patient' Paradox
Aakar Shah, Pfizer
Tracy Sherman, Ephicacy Consulting Group

BB07. Set-up and usage ways of Jupyter Notebook for SAS
Alona Bulana, DOCS International Germany GmbH

BB08. UTF What? A Guide for Handling SAS Transcoding Errors with UTF-8 Encoded Data
Michael Stackhouse, Covance

BB09. Easy CSR In-Text Table Automation, Oh My
Janet Stuelpner, SAS Institute

BB10. Exploring techniques of using multiple SET statements in a DATA step
Yizhong Huang, Celgene Corporation

BB11. Know thy data - techniques for data exploration
charu shankar, SAS Institute

BB12. SAS automation techniques – specification driven programming for Lab CTC grade derivation and more
Yuqin Li, Eli Lilly

BB13. Report Compatibility: System to evaluate your code against volatile sources.
Joseph Cooney, ICON
Randy Santiano, Eli Lilly and Company
Craig Brelage, Eli Lilly

BB14. Tips and Fixes for Cross-Environment Batch Transfer of SAS Data
Yun Zhuo, Axio Research LLC

BB15. Get Your Sort On: Macro Driven Method for Automated Unique Sorting
Kevin Miller, Gilead Sciences

BB16. Merge with Caution: How to Avoid Common Problems when Combining SAS® Datasets
Josh Horstman, Nested Loop Consulting

BB17. Fifteen Functions to Supercharge Your SAS Code
Josh Horstman, Nested Loop Consulting

BB18. Sense and Censorability: Learn censoring techniques with ADTTE for your survival
Shilpakala Vasudevan, Ephicacy Lifescience Analytics

BB19. Speed Up SDTM/ADaM Dataset Development with If-Less Programming
Lei Zhang, Celgene Corp.

BB20. The Fundamentals of Macro Quoting Functions
Arthur Li, City of Hope National Medical Center

BB21. Creating Data Shells with the DS2 procedure
Matthew Wiedel, Syneos Health

BB22. Expand Your Skills from SAS® to R with No Complications
Andrii Korchak, Experis Clinical/Intego Group LLC

BB23. Beyond MERGE for Combining Datasets
David Franklin, IQVIA

BB24. Automated Validation of Complex Clinical Trials Made Easy
Richann Watson, DataRich Consulting
Josh Horstman, Nested Loop Consulting



Data Standards

DS01. Traceability: Some Thoughts and Examples for ADaM Needs
Richann Watson, DataRich Consulting
Kent Letourneau
Wayne Zhong, Accretion Softworks
Sandra Minjoe, PRA Health Sciences

DS02. Coming soon: ADNCA and the PK submission
Luke Reinbolt, Dataceutics
Liz MacDonald, Nuventra Pharma Sciences, Inc.

DS03. ADaM Structures for Integration: A Preview
Deborah Bauer, Sanofi
Kimberly Minkalis
Wayne Zhong, Accretion Softworks

DS04. Introducing Dynamic Data Quality Control Methods: Abstracting Data Validation through External Data
Troy Hughes, Datmesis Analytics

DS05. Avoiding Sinkholes: Common Mistakes During ADaM Data Set Implementation
Richann Watson, DataRich Consulting
Karl Miller, Syneos Health

DS06. Analysis of Oncology Studies for Programmers and Statisticians
kevin lee, Clindata Insight

DS07. Improving Traceability for Complex Algorithms in ADaM Datasets
Priya Saradha, Levstat LLC

DS08. ADaM data structure layout: common issues and ways to avoid
Jianfei Jiang, Eli Lilly and Company

DS09. Best Practices in Data Standards Governance
Melissa Martinez, SAS Institute

DS10. Designing Flexible Data Standards Models
Melissa Martinez, SAS Institute

DS11. Avoid chasing one’s Tail - Challenges and Solutions for managing changes in SDTM Standards Development
Erica Davis, Shire

DS12. An alternate way to create the standard SDTM domains
Sunil Kumar Pusarla, Omeros Corporation

DS13. Global checklist to QC SDTM Lab data
MURALI MARNENI, PPD
Sekhar Badam

DS14. Core and Extension studies – Challenges and solutions towards SDTM submission
MURALI MARNENI, PPD

DS15. Doctor's ‘Prescription’ to Reengineer Process of Pinnacle21 Community Version Friendly ADaM Developm
Aakar Shah, Pfizer
Tracy Sherman, Ephicacy Consulting Group

DS16. SDTM EX and EC: Considerations When Submitting Exposure Data
Kristin Kelly, Pinnacle 21
Jerry Salyers

DS17. Creating an ADaM Data Set for Correlation Analyses
Chad Melson, Experis Clinical

DS18. Complexity in collection of PGx data and challenges in mapping to SDTM
Rama Kudaravalli, Syneos Health

DS19. Implementation of Data Cut Off in Analysis of Clinical Trials
Mei Dey, AstraZeneca

DS20. How to automate validation with Pinnacle 21 command line interface and SAS
Aleksey Vinokurov, Pinnacle 21

DS21. Standardization Efforts at Medtronic, its benefits and Lessons Learned
Firdouse Fathima, Medtronic

DS22. Implementation of ADaM Basic Data Structure on Genetic Variation Data for Pharmacogenomics Studies
Linghui Zhang, Merck & Co., Inc.

DS23. Complying with the ADaM Compliance Rules
Steven Kirby, Chiltern/Covance

DS24. Family of PARAM***: PARAM, PARAMCD, PARAMN, PARCATy(N), PARAMTYPE
Kamlesh Patel, Rang Technologies Inc



Data Visualization

DV01. Square Peg, Square Hole—Getting Tables to Fit on Slides in the ODS Destination for PowerPoint
Jane Eslinger, SAS Institute

DV02. Diving Deep into SAS® ODS Graphics Styles
Dan Heath, SAS Institute, Inc.

DV03. Creating a Publication Quality Graph Embedded in another Graph
John King, Ouachita Clinical Data Services, Inc.
Warren Kuhfeld, SAS

DV04. Advanced Visualization using TIBCO Spotfire® and SAS®
Ajay Gupta, PPD

DV06. Animated Multi-dimensional Scatter Plot Visualization for Longitudinal Clinical Trial Data Reporting
Tao Shu, Eli Lilly and Company
Jianfei Jiang, Eli Lilly and Company

DV07. Instant Gratification: Calculating Resource Needs Based on Operational Data using an Interactive Tool
Melissa Hill, Cd3 Inc.
Baker Bill, Cd3 Inc.

DV08. Can you easily create your own interactive dashboards in SAS? Is SAS 4GL all you need?
Piotr Karasiewicz, Quanticate

DV10. Advanced Clinical Graphs using Axis Tables
Sanjay Matange, SAs Institute Inc

DV11. Processing and Cleaning Streaming Data in SAS
Mehmet Kocak, The University of Tennessee Health Science Center

DV12. Using ODS LAYOUT, GTL, and ODSTEXT to Generate a Compact Graphical Codebook
Shane Rosanbalm, Rho, Inc.

DV13. Using JMP Scripting Language (JSL) to create an application interface for automated analysis of Lab Data in Clinical Trial
Dongsun Cao, UCB Biosciences

DV14. Welcome to the Three Ring %CIRCOS: An Example of Creating a Circular Graph without a Polar Axis
Jeffrey Meyers, Mayo Clinic

DV15. Visually Exploring Odds Ratios between Risk Factors for Fetal and Infant Mortality in Kent County, MI
Ruth Kurtycz, Spectrum Health

DV16. Graphic Visualization for Treatment Switch Pattern
Anni Weng, KMK Consulting Inc
Huanxue Zhou, KMK Consulting, Inc.

DV17. Graph Display of Patient Profile in SAS
Yanwei Han, Seqirus USA Inc.

DV18. The Changing Clinical Data Analytics Landscape - Visualization & Beyond
Siddartha Kondapally, Vita Data Sciences (a division of Softworld, Inc.)
kishore Pothuri, Vita Data Sciences, a division of Softworld, Inc.
Bhavin Busa, Vita Data Sciences (a division of Softworld, Inc.)

DV19. Using SAS GTL to Visualize Data When there is Too Much of It to Visualize
Nate Derby, Stakana Analytics
Perry Watts, Stakana Analytics

DV20. V is for Venn Diagrams
Kriss Harris, SAS Specialists Ltd

DV21. Telling Stories with Jupyter Notebook
kevin lee, Clindata Insight

DV22. Adding another dimension to oncology graphs: 3-D Waterfall Plots in SAS
Liz Thomas, Epizyme
Mark Woodruff, Epizyme

DV23. “A tale of a tail” or how to make your graph more informative
Iryna Kotenko, Intego Group

DV24. CONSORT Diagrams with SG Procedures
Prashant Hebbar, SAS
Sanjay Matange, SAs Institute Inc

DV25. Tumor Dashboard
Sudhir Singh, Pharmacyclics Inc.

DV26. Leveraging Standards for Effective Visualization of Early Efficacy in Clinical Trial Oncology Studies
Kelci Miclaus, SAS Institute Inc.
Lili Li, SAS Institute Inc.

DV27. Utilize Dummy Variables/Datasets in Graph Generation
Amos Shu, AstraZeneca



Hands-On Training

HT01. Manipulating Statistical and Other Procedure Output to Get the Results That You Need
Vince DelGobbo, SAS Institute Inc.

HT02. A Collection of Items from a Programmers’ Notebook
David Franklin, IQVIA
Cecilia Mauldin, Biogen

HT03. Shiny Happy People: Using RShiny and SDTM Data to generate a Quick Interactive Dashboard
Saranya Duraisamy, Biogen Inc
Nate Mockler, Biogen Inc

HT04. A Gentle Introduction to R From A SAS Programmer's Perspective
Saranya Duraisamy, Biogen Inc
Nate Mockler, Biogen Inc

HT05. Working with the SAS® ODS EXCEL Destination to Send Graphs, and Use Cascading Style Sheets When Writing to EXCEL Workbooks
William Benjamin, Owl Computer Consultancy LLC

HT06. How to Implement Analytics (Python, R, SAS and SPSS) with Big Data
(No paper available)
Bob Matsey, Teradata
Paul Segal, Teradata
Tho Nguyen, Teradata

HT07. R 101: The base Package
Arthur Li, City of Hope National Medical Center

HT08. Visualizing and Modeling Clinical Trial Data
Alex Ford, SAS Institute
Andrea Coombs, SAS Institute



Leadership

LD01. The Statistical Programming Summit
Todd Case, Vertex Pharmaceuticals

LD02. Customized Project Tracking with SAS® and Jira™
Nancy Brucken, Syneos Health

LD03. Are You Ready for It? Preparing for Your Next Technical Interview
Christine McNichol, Covance

LD04. Framework for Critical Thinking - Introducing Lean Philosophy to Statistical Programmers
Wenyun Ji, Amgen, Inc.

LD05. Accredited, Bona fide, Certified, Diploma’ed, and Edumacated: The ABCDEs of Automating the Validation and Monitoring of Professional Requirements for Employees and Job Candidates Through Dynamic, Data-Driven SAS® Reporting
Troy Hughes, Datmesis Analytics

LD06. Proactive Role of a Lead Programmer Could Help Prevent Potential Delay in Submission
Charan Vem, Pfizer
Ranjith Kalleda

LD07. Reflections on a Career in Clinical/Statistical/SAS Programming (35+ Years in the Making)
Alan Meier, MedImmune, LLC

LD08. Making Processes Accessible
Arthur Collins, Biogen

LD09. Hiring the best SAS programmer among today’s high competitive market
JIAN HUA (DANIEL) HUANG, Celgene

LD10. Satisfied customers leave, but raving fans stay...
Hrideep Antony, Syneos Health
AMAN BAHL, Syneos Health

LD11. How to Be a Great (Stats or Programming) Manager: Advice from an Underling
Mary Grovesteen, Triangle Biostatistics, LLC

LD12. What makes a good QC Programmer?
Timothy Harrington, Dataceutics, inc.

LD14. To Error is Human: An Overview of Human Errors in SAS® Programming and How to Mitigate Them
Nagadip Rao, Eliassen Group

LD15. A Statistician’s Guide to Leadership and Success
Heather Murphy, Syneos Health

LD16. AGING MAN_AGEMENT or millennial TECH_AGEMENT – road ahead for organizations
Charan Kumar K J, Ephicacy Lifescience Analytics Pvt. Ltd.

LD17. So You Want To Be An Independent Consultant
(No paper available)
Josh Horstman, Nested Loop Consulting

LD18. Mind Over Matter: Building an Army of Passionate Soldiers Dedicated to Saving Patients’ Lives
Shefalica Chand, Seattle Genetics

LD19. Statistical Programming: Revolution, Evolution, or Status Quo?
Jeff Abolafia, Rho, Inc.
Frank DiIorio, CodeCrafters, Inc.

LD20. Infrastructure for healthcare analytics: A foundational approach to development
Jennifer Popovic, RTI International

LD91. Leadership Panel
Mira Shapiro, Analytic Designers LLC
Frank DiIorio, CodeCrafters, Inc.
Nagadip Rao, Eliassen Group
Janet Stuelpner, SAS Institute
Timothy Harrington, Dataceutics, inc.



Medical Devices

MD01. SDTMs in a Medical Device Trial – A First Attempt
Phil Hall, Edwards Lifesciences

MD02. ADaM for Medical Devices: Extending the Current ADaM Structures
Julia Yang, Medtronic
Sandra Minjoe, PRA Health Sciences

MD03. Know your PET: From the Scans to SDTM Datasets
Balavenkata Pitchuka, Seattle Genetics Inc
Bhargav Koduru, Seattle Genetics Inc

MD05. Data Management and CDISC Formatting for Transdermal Patches
Lois Lynn, Noven Pharmaceuticals, Inc.

MD06. Challenges in Implementing Device Related ADaM and SDTM Standards in a Drug-Device Study
Maureen Maunsell, Shire
Wenying Tian, Shire
Karin LaPann, Shire

MD07. When a Clinical Trial Lasts Forever: A Reporting Solution for Post-Market Surveillance of Cardiac Rhythm Devices
Becky DeBus, Medtronic

MD08. A Critique of the Use of the Medical Device SDTM Domains in Therapeutic Area User Guides
Carey Smoak, S-Cubed



Quick Tips

QT01. ODS/RTF Pagination Revisit
Ya Huang, Halozyme Therapeutics, Inc.
Bryan Callahan, Halozyme Therapeutics, Inc.

QT02. Improving Listing Generation with ODS EXCEL
Stephanie Sanchez, Ventana Medical Systems

QT03. Be a “Gyro”, Turn your SAS Report into a Sandwich
Michael Stout, Johnson & Johnson Medical Device Companies

QT04. Automating and Abstracting SAS Format Creation: Leveraging the CNTLIN Option To Build Dynamic SAS Formats That Clean, Convert, and Categorize Data
Troy Hughes, Datmesis Analytics

QT05. SAS® Macros of Performing Look-Ahead and Look-Back Reads
yanhong Liu, CCHMC

QT06. When Powerful SAS® Meets PowerShell®
Shunbing Zhao, Merck & Co., Inc.

QT07. No News Is Good News: A Macro to Reveal the Untold Normal Lab Results
Ming Yan, Eli Lilly and Company

QT08. A Macro to Create Program Inventory for Analysis Data Reviewer’s Guide
Xianhua Zeng, PAREXEL

QT09. Why choose between DATA Step or PROC SQL when you can have both?
charu shankar, SAS Institute

QT10. Interactive Programming Using Task in SAS Studio
Suwen Li, Hoffmann-La Roche Limited

QT11. Bring Unix into SAS
Jaiying Guo, Eli Lilly

QT12. One Level PDF Bookmark Created by ODS DOCUMENT and PROC DOCUMENT
Yanmei Zhang, Grifols Inc.
Titania Dumas-Roberson, Grifols Inc.

QT13. Tips and Tricks for Producing Time-Series Cohort Data
Nate Derby, Stakana Analytics

QT15. SASsy Data Checks
Amie Bissonett, Syneos Health

QT16. Tips and Tricks to Enhance RTF Output
Rohini Rao, Omeros Corporation

QT17. IF there is a better way than IF-THEN
Anni Weng, KMK Consulting Inc
Bob Tian, KMK Consulting Inc

QT18. Quickly create your own format with hundreds of values and other tips
Sergey Sian, IQVIA



Regulatory (FDA/PMDA)

REG01. Common Data Related Review Issues and Prevention: A Statistical Reviewer's Thoughts (slides)
Huanyu Chen, PhD, Statistical Reviewer, DB V/OTS/CDER/FDA

REG02. What Medical Reviewers Can Do With Standardized Data and Metadata Received in Module 5 (slides)
Eileen Navarro, MD, FACP, Medical Officer, Office of Computational Sciences, CDER/FDA

REG03. Electronic Data Submission and Utilization in Japan (slides)
Yuki Ando, PhD, Sr Scientist for Biostatistics, Office of Advanced Evaluation with Electronic Data, PMDA



Real World Evidence

RW01. Second Primary Malignancy (SPM) Analyses in a Disease Registry Study
Jane Lu, Celgene Corporation
Shankar Srinivasan, Celgene Corporation
Robert Knight, Sorrento Therapeutics

RW02. Combining Electronic Diary Seizure Data with Visit-Based Seizure Data in the MONEAD Study
Ryan May, The Emmes Corporation
Julia Skinner, The Emmes Corporation

RW04. Triangulating Multiple Sources of Contradictory Prescription Data – a Real-World Case Study
Vidhya Parameswaran, Fresenius Medical Care Canada

RW05. Real World Patients: The intersection of Real World Evidence and Episode of Care Analytics
Youngjin Park, SAS Institute
David Olaleye, SAS Institute

RW06. Improved Transparency in Key Operational Decisions in Real World Evidence
rebecca levin, United BioSource Corp
Irene Cosmatos, United BioSource Corporation



Strategic Implementation

SI01. Let’s get to the Source and streamline it to the End
Jennifer Price, Bioclinica

SI02. Risk-based Validation in Clinical Trial Reporting: Focus on What Matters Most
Amber Randall, Axio Research
William Coar, Axio Research

SI03. An eSubmission Tracking Tool for both Components and Timelines
Todd Case, Vertex Pharmaceuticals

SI04. Using Simulation to Create Enrollment Plans
Jim Box, SAS Institute

SI05. Infrastructure Designed to Maximize Workflow
Paul Hamilton, Omeros Corporation

SI06. Machine Learning – Why we should know and How it works
kevin lee, Clindata Insight

SI08. Incorporating Pinnacle21 ® With LSAF ®
Aleksey Vinokurov, Pinnacle 21
Sandeep Juneja, SAS
Sonali Garg, Alexion Pharma

SI09. Regulatory Compliance in the digital age – A cloud-based statistical computing environment (SCE) to the rescue
Sridhar Vijendra, Ephicacy Lifescience Analytics Pvt. Ltd.
Tyagrajan Swaminathan, Ephicacy Lifescience Analytics Pvt. Ltd.

SI10. TECHNIQUES FOR GLOBAL, ADAPTABLE, AND EFFICIENT PROGRAMMING
Sunil Kumar Pusarla, Omeros Corporation

SI11. The next generation "Smart Program Repository”
Hrideep Antony, Syneos Health
AMAN BAHL, Syneos Health

SI12. Exploring use of R for Clinical trials
Parveen Kumar, GCE Solutions
Kalpesh Prajapati, GCE Solutions

SI13. Validating R - Part of the Uphill Battle in the Pharmaceutical Industry
Peter Schaefer, VCA-Plus, Inc

SI14. Integrations Made Easier
Kirsty Lauderdale, Chiltern

SI15. One Project, Two Teams: The Unblind Leading the Blind Part 2: Options
Kristen Harrington, Rho, Inc.



Submission Standards

SS01. A Framework for Implementing [Conflicting] FDA Guidance
Todd Case, Vertex Pharmaceuticals

SS02. Preparing to Meet FDA Requirements for Submission of Standardized Data and Documentation
Steven Kirby, Chiltern/Covance
mario Widel, Covance

SS03. A CRO’s perspective on successful partnering to deliver SDTM/SEND contributions
kirsty payne, LGC
Helen Owen, LGC

SS04. Trial Summary: The Golden Gate towards a Successful Submission
Bhargav Koduru, Seattle Genetics Inc
Girish Kankipati, Seattle Genetics Inc

SS05. Challenges for Implementing Study Data Standardization Plan (SDSP) in Legacy Data Submission
Shengfeng Ho, RUNDO

SS06. Define.xml: what you should and should not be documenting in your define files
(No paper available)
David Roulstone, Pinnacle 21

SS07. To Createdefine.xml version 2 including Analysis Results Metadata with the SAS® Clinical Standard
(No paper available)
Murali Neela, GCE Solutions

SS08. Stay ahead of the game: Know the SDTM compliance check reject messages before creating your SDTM datasets
Lixiang Liu, Eli Lilly
Yuqin Li, Eli Lilly

SS09. define.xml (noun): Fear Inducing Task for SAS Programmers
Kjersten Offenbecker, Covance, Inc
Antonio Cardozo, Spaulding Clinical Research
Kirsty Lauderdale, Chiltern

SS11. Accessing the Metadata from Define-XML
Lex Jansen, SAS Institute Inc.

SS12. Improving Metadata Compliance and Assessing Quality Metrics with a Standards Library
Veena Nataraj, Shire
Erica Davis, Shire

SS13. Best Practice for Explaining Validation Results in the Study Data Reviewer’s Guide
Kristin Kelly, Pinnacle 21

SS14. Diagnostics of technical errors in define.xml file
Sergiy Sirichenko, Pinnacle 21 LLC

SS15. It’s all about getting the Source and Codelist Implementation right for ADaM Define.xml v2.0
Supriya Davuluri, PPD

SS16. Programmer’s Guide for OSI Deliverables – Creation of Site Level Summary Dataset and Automation of BIMO Listins Generation
Charanjit Kahlon, Vita Data Sciences, a division of Softworld, Inc
Kristie Kooken, Achaogen, Inc.
Bhavin Busa, Vita Data Sciences (a division of Softworld, Inc.)
Dharmendra Tirumalasetti, Vita Data Sciences, a division of Softworld, Inc

SS17. Why organizations need MDR systems to manage metadata?
Abhinav Jain, Ephicacy Consulting Group Inc

SS18. Confusing Data Validation Rules Explained
Michael Beers, Pinnacle 21

SS19. Want Submission-Ready Datasets Package from the Get-Go?
kishore Pothuri, Vita Data Sciences, a division of Softworld, Inc.
Bhavin Busa, Vita Data Sciences (a division of Softworld, Inc.)

SS20. Review programs used for submission and their input to the define-XML 2.0
Ari Knoph, Novo Nordisk A/S
Morten Hasselstrm Jensen, Novo Nordisk A/S
bo andersen, Novo Nordisk A/S

SS21. eSubmission - Are you really Compliant?
Majdoub Haloui, Merck & Co
Suahs Sanjee

SS22. Watch Out For Blind Spots While Keeping Up With the Speed of Evolving Standards and Regulations
Srinivas Veeragoni, Bayer U.S. LLC.



ePosters

EP02. Application of Survival Analysis in Multiple Events Using SAS
Jane Lu, AstraZeneca
David Shen

EP03. Link Up Sync Up: Calculation of concordance and discordance rates between Independent Review Facility (IRF) and Investigator site data using SAS®
Bhargav Koduru, Seattle Genetics Inc
Girish Kankipati, Seattle Genetics Inc

EP04. Implementing the Rank-Preserving Structural Failure Time Model in SAS and R
Bradford Danner, GCE Solutions, Inc.
Indrani Sarkar, GCE Solutions, Inc.

EP07. Adverse Event Analysis - One step forward
(No paper available)
Anuja Rasal

EP08. How to Continue to Use SAS System Viewer in SAS 94
Jeff Xia, Merck

EP09. Let’s Get FREQy with our Statistics: Data Driven Approach to Determining Appropriate Test Statistic
Lynn Mullins, PPD
Richann Watson, DataRich Consulting

EP10. Some Common Programming Errors and Possible Solutions That Could Impact a Successful NDA/BLA
Amos Shu, AstraZeneca

EP11. Integrating Analytics In Database with SAS, Hadoop, and the EDW in a Single Solution
(No paper available)
Bob Matsey, Teradata

EP12. A SAS Macro Application on Confidence Intervals for Binominal Proportion
Kaijun Zhang, FMD K&L Inc.
Sheng Zhang, FMD K&L Inc.

EP13. Purrfectly Fabulous Feline Functions
Louise Hadden, Abt Associates Inc.

EP15. Preparing the Office of Scientific Investigations (OSI) Requests for Submissions to FDA
ZIHUI LIN, Amgen Inc.
Wei Cui, Amgen Inc.
Yaling Teng, Amgen Inc.
Ran Li, Amgen Inc.

EP16. Discover the Deeper DATA Step
Timothy Harrington, Dataceutics, inc.

EP17. Creating and Customizing Graphics using Graph Template Language
Saihua Liu
Yanmei Zhang, Grifols Inc.
Titania Dumas-Roberson, Grifols Inc.

EP18. Great Time to Learn GTL
Richann Watson, DataRich Consulting
Kriss Harris, SAS Specialists Ltd

EP19. Useful Adverse Events (AE) data Diagnostics and Summarization
Abhinav Srivastva, Gilead Sciences Inc.

EP20. Analyzing Adverse Events of Special Interest using Lab Tests and Toxicity Grades
Vamsi Krishna Medarametla, Seattle Genetics, Inc.
Liz Thomas, Epizyme
Gokul Vasist, Seattle Genetics, Inc.

EP21. Experience of electronic data submission via Gateway to PMDA
Kumiko Kimura, Amgen Astellas BioPharma K.K.
Iori Sakakibara, Amgen Astellas BioPharma K.K.
Laurence Carpenter, Amgen Ltd

EP23. Summary Table for Displaying Results of a Logistic Regression Analysis
Lori Parsons, ICON Clinical Research

EP25. A SAS Macro to Create Validation Summary of Dataset Report
Zemin Zeng, Sanofi