San Francisco, California
May 10-13, 2020
Note: The conference was cancelled due to the COVID-19 pandemic. However, many presenters still took the time and effort to provide a written paper for our conference proceedings, for which the conference committee is grateful.
Advanced Programming
AP-005. %MVMODELS: a Macro for Survival and Logistic AnalysisJeffrey Meyers, Mayo Clinic
AP-007. Quick, Call the "FUZZ": Using Fuzzy Logic
Richann Watson, DataRich Consulting
Louise Hadden, Abt Associates Inc.
AP-018. Like, Learn to Love SAS® Like
Louise Hadden, Abt Associates Inc.
AP-022. It’s All about the Base—Procedures, Part 2
Jane Eslinger, SAS Institute
AP-073. Fuzzy Matching Programming Techniques Using SAS® Software
Stephen Sloan, Accenture
Kirk Paul Lafler, Software Intelligence Corporation
AP-083. Sometimes SQL Really Is Better: A Beginner's Guide to SQL Coding for DATA Step Users
Brett Jepson, Cytel, Inc.
AP-090. Dating for SAS Programmers
Josh Horstman, Nested Loop Consulting
AP-093. One Macro to create more flexible Macro Arrays and simplify coding
Siqi Huang, Boehringer Ingelheim
AP-129. Shell Script automation for SDTM/ADaM and TLGs
Pradeep Acharya, Ephicacy Lifescience Analytics
Aniket Patil, Pfizer Inc
AP-136. Transpose Procedure: Turning it Around Again
Janet Stuelpner, SAS
AP-240. Next Level Programming-Reusability and Importance of Custom Checks
Akhil Vijayan, Genpro Research Inc.
Limna Salim, Genpro Life Sciences
AP-251. How to Achieve More with Less Code
Timothy Harrington, Navitas Data Sciences
AP-255. Python-izing the SAS Programmer 2: Objects, Data Processing, and XML
Mike Molter, PRA Health Sciences
AP-264. Hidden Gems in SAS Display Manager: Old Wine in New Bottle
Ramki Muthu, Senior SAS Programmer
AP-278. SAS Formats: Same Name, Different Definitions FORMAT-ters of Inconvenience
Jackie Fitzpatrick, SCHARP
Applications Development
AD-046. Normal is Boring, Let’s be Shiny: Managing Projects in Statistical Programming Using the RStudio® Shiny® AppGirish Kankipati, Seattle Genetics Inc
Hao Meng, Seattle Genetics Inc
AD-052. ‘This Is Not the Date We Need. Let’s Backdate’: An Approach to Derive First Disease Progression Date in Solid Tumor Trials
Girish Kankipati, Seattle Genetics Inc
Boxun Zhang, Seattle Genetics
AD-055. A Set of VBA Macros to Compare RTF Files in a Batch
Jeff Xia, Merck & Co., Inc.
Shunbing Zhao, Merck & Co., Inc.
AD-071. Using SAS ® to Create a Build Combinations Tool to Support Modularity
Stephen Sloan, Accenture
AD-084. Metadata-driven Modular Macro Design for SDTM and ADaM
Ellen Lin, Seattle Genetics, Inc.
Aditya Tella, Seattle Genetics, Inc.
Yeshashwini Chenna, Seattle Genetics, Inc.
Michael Hagendoorn, Seattle Genetics, Inc.
AD-088. Demographic Table and Subgroup Summary Macro %TABLEN
Jeffrey Meyers, Mayo Clinic
AD-106. Macro To Produce Sas®-Readable Table Of Content From Tlf Shells
Igor Goldfarb, Accenture
Ella Zelichonok, Naxion
AD-114. Chasing Master Data Interoperability: Facilitating Master Data Management Objectives Through CSV Control Tables that Contain Data Rules that Support SAS® and Python Data-Driven Software Design
Troy Hughes, Datmesis Analytics
AD-130. A Novel Solution for Converting Case Report Form Data to SDTM using Configurable Transformations
Sara Shoemaker, Fred Hutch / SCHARP
Matthew Martin, Fred Hutch / SCHARP
Robert Kleemann, Fred Hutch / SCHARP
David Costanzo, Fred Hutch / SCHARP
Tobin Stelling, Fred Hutch / SCHARP
AD-142. Data Library Comparison Macro %COMPARE_ALL
Jeffrey Meyers, Mayo Clinic
AD-146. MKADRGM: A Macro to Transform Drug-Level SDTM Data into Traceable, Regimen-Level ADaM Data Sets
Sara McCallum, Harvard T.H. Chan School of Public Health, Center for Biostatistics in AIDS Research (CBAR)
AD-171. Clinical Database Metadata Quality Control: Two Approaches using SAS and Python
Craig Chin, Fred Hutch
Lawrence Madziwa, FRED HUTCH
AD-208. Programming Patient Narratives Using Microsoft Word XML files
Yuping Wu, PRA Health Science
Jan Skowronski, Genmal A/S
AD-296. SAS Migration from Unix to Windows and Back
Valerie Williams, ICON Clinical Research
AD-298. RStats: A R-Shiny application for statistical analysis
Sean Yang, Syneos Health Clinical
Hrideep Antony, Syneos Health USA
Aman Bahl, SYNEOS HEALTH
AD-308. Using Data-Driven Python to Automate and Monitor SAS Jobs
Julie Stofel, Fred Hutchinson Cancer Research Center
AD-341. Opening Doors for Automation with Python and REST: A SharePoint Example
Mike Stackhouse, atorus
AD-347. A Tool to Organize SAS Programs, Output and More for a Clinical Study
Yang Gao, Merck & Co., Inc.
Artificial Intelligence (Machine Learning)
AI-025. Gradient Descent: Using SAS for Gradient BoostingKaren Walker, Walker Consulting LLC
AI-058. Pattern Detection for Monitoring Adverse Events in Clinical Trials - Using Real Time, Real World Data
Surabhi Dutta, Industry
AI-224. How to let Machine Learn Clinical Data Review as it can Support Reshaping the Future of Clinical Data Cleaning Process
Mirai Kikawa, Novartis Pharma K.K.
Yuichi Nakajima, Novartis
AI-242. Automate your Safety tables using Artificial Intelligence & Machine Learning
Roshan Stanly, Genpro Research Inc.
Ajith Baby Sadasivan, Genpro Research
Limna Salim, Genpro Life Sciences
Data Standards
DS-023. Data Transformation: Best Practices for When to Transform Your DataJanet Stuelpner, SAS
Olivier Bouchard, SAS
Mira Shapiro, Analytic Designers LLC
DS-031. Ensuring Consistency Across CDISC Dataset Programming Processes
Jennifer Fulton, Westat
DS-062. Untangling the Subject Elements Domain
Christine McNichol, Covance
DS-080. Standardised MedDRA Queries (SMQs): Programmers Approach from Statistical Analysis Plan (SAP) to Analysis Dataset and Reporting
Sumit Pratap Pradhan, SYNEOS HEALTH
DS-082. Implementation of Immune Response Evaluation Criteria in Solid Tumors (iRECIST) in Efficacy Analysis of Oncology Studies
Weiwei Guo, Merck & Co., Inc.
DS-109. Impact of WHODrug B3/C3 Format on Coding of Concomitant Medications
Lyma Faroz, Seattle Genetics
Jinit Mistry, Seattle Genetics
DS-110. Demystifying SDTM OE, MI, and PR Domains
Lyma Faroz, Seattle Genetics
Sruthi Kola, SVU
DS-117. CDISC-compliant Implementation of iRECIST and LYRIC for Immunomodulatory Therapy Trials
Kuldeep Sen, Seattle Genetics
Sumida Urval, Seattle Genetics
Yang Wang, Seattle Genetics
DS-133. Is Your Dataset Analysis-Ready?
Kapila Patel, Syneos Health
Nancy Brucken, Clinical Solutions Group, Inc.
DS-195. Standardizing Patient Reported Outcomes (PROs)
Charumathy Sreeraman, Ephicacy Lifescience Analytics
DS-196. Simplifying PGx SDTM Domains for Molecular biology of Disease data (MBIO).
Sowmya Srinivasa Mukundan, Ephicacy
Charumathy Sreeraman, Ephicacy Lifescience Analytics
DS-235. Tackle Oncology Dose Intensity Analysis from EDC to ADaM
Song Liu, BeiGene
Mijun Hu, BeiGene Corporation
Jieli Fang, BeiGene Cororperation
Cindy Song, BeiGene
Quting Zhang, Beigene
DS-248. Best practices for annotated CRFs
Amy Garrett, Pinnacle 21
DS-261. SUPPQUAL datasets: good bad and ugly
Sergiy Sirichenko, Pinnacle 21
DS-306. SDTM to ADaM Programming: Take the Leap!
Jennifer McGrogan, Covance, Inc.
Alyssa Wittle, Covance, Inc.
DS-329. Overcoming Pitfalls of DS: Shackling 'the Elephant in the room'
Soumya Rajesh, SimulStat
Michael Wise, Syneos Health
DS-344. Trial Sets in Human Clinical Trials
Fred Wood, Data Standards Consulting Group
DS-365. Creating SDTMs and ADaMs CodeList Lookup Tables
Sunil Gupta, TalentMine
Data Visualization and Reporting
DV-004. Library Datasets Summary Macro %DATA_SPECSJeffrey Meyers, Mayo Clinic
DV-006. What's Your Favorite Color? Controlling the Appearance of a Graph
Richann Watson, DataRich Consulting
DV-009. Great Time to Learn GTL: A Step-by-Step Approach to Creating the Impossible
Richann Watson, DataRich Consulting
DV-050. Oncology Graphs-Creation (Using SAS and R), Interpretation and QA
Taniya Muliyil, Bristol Meyers Squibb
DV-057. R for Clinical Reporting, Yes – Let’s Explore it!
Hao Meng, Seattle Genetics Inc.
Yating Gu, Seattle Genetics, Inc.
Yeshashwini Chenna, Seattle Genetics
DV-066. Simplifying the Derivation of Best Overall Response per RECIST 1.1 and iRECIST in Solid Tumor Clinical Studies
Xiangchen (Bob) Cui, Alkermes, Inc
Sri Pavan Vemuri, Alkermes
DV-132. Automation of Flowchart using SAS
Xingshu Zhu, Merck & Co., Inc.
Bo Zheng, Merck & Co., Inc.
DV-135. Making Customized ICH Listings with ODS RTF
Huei-Ling Chen, Merck & Co., Inc.
William Wei, Merck & Co., Inc.
DV-148. Butterfly Plot for Comparing Two Treatment Responses
Raghava Pamulapati, Merck & Co., Inc.
DV-157. Automating of Two Key Components in Analysis Data Reviewer’s Guide
Shunbing Zhao, Merck & Co., Inc.
Jeff Xia, Merck & Co., Inc.
DV-158. Enhanced Visualization of Clinical Pharmacokinetics Analysis by SAS GTL
Min Xia, PPD
DV-163. Special Plots methods with diabetes disease data
Yida Bao, Auburn University
Zheran Wang, Auburn University
Jingping Guo, Conglomerate company
Philippe Gaillard, Auburn University
DV-164. Using R Markdown to Generate Clinical Trials Summary Reports
Radhika Etikala, Statistical Center for HIV/AIDS Research and Prevention (SCHARP) at Fred Hutch
Xuehan Zhang, Statistical Center for HIV/AIDS Research and Prevention (SCHARP) at Fred Hutch
DV-166. An Introduction to the ODS Destination for Word
[Additional Materials (ZIP Archive)]
David Kelley, SAS
DV-198. r2rtf – an R Package to Produce Rich Text Format (RTF) Tables and Figures
Siruo Wang, Johns Hopkins Bloomberg School of Public Health
Keaven Anderson, Merck & Co., Inc.
Yilong Zhang, Merck & Co., Inc.
Simiao Ye, Merck & Co., Inc.
DV-283. Programming Technique for Line Plots with Superimposed Data Points
Chandana Sudini, Merck & Co., Inc.
Bindya Vaswani, Merck & Co., Inc.
DV-299. Effective Exposure-Response Data Visualization and Report by Combining the Power of R , SAS programming and VBScript
Shuozhi Zuo, Regeneron Pharmaceuticals
Hong Yan, Regeneron Pharmaceuticals
ePosters
EP-064. A Guide for the Guides: Implementing SDTM and ADaM standards for parallel and crossover studiesAzia Tariq, GlaxoSmithKline
Janaki Chintapalli, GlaxoSmithKline
EP-099. Color Data Listings and Color Patient Profiles
Charley Wu, Atara Biotherapeutics
EP-172. TDF – Overview and Status of the Test Data Factory Project, Standard Analyses & Code Sharing Working Group
Nancy Brucken, Clinical Solutions Group
Peter Schaefer, VCA-Plus, Inc.
Dante Di Tommaso, Omeros
EP-174. Standard Analyses and Code Sharing Working Group Update
Nancy Brucken, Clinical Solutions Group, Inc.
Dante Di Tommaso, Omeros
Jane Marrer, Merck & Co., Inc.
Mary Nilsson, Eli Lilly and Company
Jared Slain, MPI Research
Hanming Tu, Frontage
EP-176. 10 things you need to know about PMDA eSubmission
Yuichi Nakajima, Novartis
EP-177. Detecting Side Effects and Evaluating the Effectiveness of Drugs from Customers’ Online Reviews using Text Analytics, Sentiment Analysis and Machine Learning Models
Thu Dinh, Oklahoma State University
Goutam Chakraborty, Oklahoma State University
EP-337. Generating ADaM compliant ADSL Dataset by Using R
Vipin Kumpawat, Eliassen Group
Lalitkumar Bansal, Statum Analytics LLC
EP-362. SDSP: Sponsor and FDA Liaison
Bhanu Bayatapalli, University of Thiruvalluvar at INDIA
Shefalica Chand, Seattle Genetics, Inc.
Hands-On Training
HT-111. Yo Mama is Broke 'Cause Yo Daddy is Missing: Autonomously and Responsibly Responding to Missing or Invalid SAS® Data Sets Through Exception Handling RoutinesTroy Hughes, Datmesis Analytics
HT-139. You Are Using PROC GLM Too Much (and What You Should Be Using Instead)
Peter Flom, Peter Flom Consulting
Deanna Schreiber-Gregory, Juxdapoze, LLC
Leadership Skills
LS-008. Are you Ready? Preparing and Planning to Make the Most of your Conference ExperienceRichann Watson, DataRich Consulting
Louise Hadden, Abt Associates Inc.
LS-016. One Boys’ Dream: Hitting a Homerun in the Bottom of the Ninth Inning
Carey Smoak, S-Cubed
LS-037. Microsoft OneNote: A Treasure Box for Managers and Programmers
Jeff Xia, Merck & Co., Inc.
LS-059. An Effective Management Approach for a First-Time Study Lead
Himanshu Patel, Merck & Co., Inc.
Jeff Xia, Merck & Co., Inc.
LS-185. Leadership Lessons from Start-ups
Siva Ramamoorthy, Ephicacy Lifesciences Analytics
LS-265. Building a Strong Remote Working Culture – Statistical Programmers Viewpoint
Ravi Kankipati, Nola Services
Prasanna Sondur, Ephicacy LifeScience Analytics
LS-297. The Art of Work Life Balance.
Darpreet Kaur, Statistical Programmer
LS-359. Leading without Authority: Leadership At All Levels
Janette Garner, MyoKardia, Inc.
LS-364. Project Metrics- a powerful tool that supports workload management and resource planning for Biostats & Programming department.
Jian Hua (Daniel) Huang, BMS
Rajan Vohra, BMS
Andy Chopra, BMS
Medical Devices
MD-020. CDISC Standards for Medical Devices: Historical Perspective and Current StatusCarey Smoak, S-Cubed
MD-041. Successful US Submission of Medical Device Clinical Trial using CDISC
Phil Hall, Edwards Lifesciences
MD-360. An Overview of Medical Device Data Standards
Shilpakala Vasudevan, Ephicacy Lifescience Analytics
Quick Tips
QT-035. A SAS Macro for Calculating Confidence Limits and P-values Under Simon’s Two-Stage DesignAlex Karanevich, EMB Statistical Solutions
Michael Ames, EMB Statistical Solutions
QT-127. Remove Strikethrough Texts from Excel Documents by VBA Macro
Li Liu, Merck & Co.
QT-128. A Solution to Look-Ahead Observations
Yongjiang (Jerry) Xu, CSL Behring
Yanhua (Katie) Yu, PRA Health Sciences
QT-183. A Brief Understanding of DOSUBL beyond CALL EXECUTE
Ajay Sinha, Novartis
M Chanukya Samrat, Novartis
QT-209. Automation of Conversion of SAS Programs to Text files
Sachin Aggarwal, Rang Technologies
Sapan Shah, Rang Technologies
QT-213. A SAS Macro for Dynamic Assignment of Page Numbers
Manohar Modem, Cytel
Bhavana Bommisetty, Vita Data Sciences
QT-249. Text Wrangling with Regular Expressions: A Short Practical Introduction
Noory Kim, SDC
QT-253. Implementing a LEAD Function for Observations in a SAS DATA Step
Timothy Harrington, Navitas Data Sciences
QT-289. Highlight changes: An extension to PROC COMPARE
Abhinav Srivastva, Gilead Sciences
QT-313. PROC REPORT – Land of the Missing OBS Column
Ray Pass, Forma Therapeutics
QT-315. A SAS macro for tracking the Status of Table, Figure and Listing (TFL) Programming
Yuping Wu, PRA Health Science
Sayeed Nadim, PRA Health Science
QT-349. Macro for controlling Page Break options for Summarizing Data using Proc Report
Sachin Aggarwal, Rang Technologies
Sapan Shah, Rang Technologies
Real World Evidence and Big Data
RW-053. NHANES Dietary Supplement component: a parallel programming projectJayanth Iyengar, Data Systems Consultants LLC
RW-113. Better to Be Mocked Than Half-Cocked: Data Mocking Methods to Support Functional and Performance Testing of SAS® Software
Troy Hughes, Datmesis Analytics
RW-192. Natural History Study – A Gateway to Treat Rare Disease
Tabassum Ambia, Alnylam Pharmaceuticals, Inc
Software Demonstrations (Tutorials)
SD-281. A Single, Centralized, Biometrics Team Focused Collaboration System for Analysis ProjectsChris Hardwick, Zeroarc
Justin Slattery, Zeroarc
Hans Gutknecht, Zeroarc
Statistics and Analytics
SA-013. Diaries and Questionnaires: Challenges and SolutionsMarina Komaroff, Noven Pharmaceuticals
Sandeep Byreddy, Noven Pharmaceuticals, Inc.
SA-034. A Doctor's Dilemma: How Propensity Scores Can Help Control For Selection Bias in Medical Education
Deanna Schreiber-Gregory, Henry M Jackson Foundation for the Advancement of Military Medicine
SA-051. Calculation of Cochran–Mantel–Haenszel Statistics for Objective Response and Clinical Benefit Rates and the Effects of Stratification Factors
Girish Kankipati, Seattle Genetics Inc
Chia-Ling Ally Wu, Seattle Genetics
SA-072. Assigning agents to districts under multiple constraints using PROC CLP
Stephen Sloan, Accenture
Kevin Gillette, Accenture Federal Services
SA-103. Risk-based and Exposure-based Adjusted Safety Incidence Rates
Qiuhong Jia, Seattle Genetics
Fang-Ting Kuo, Seattle Genetics
Chia-Ling Ally Wu, Seattle Genetics
Ping Xu, Seattle Genetics
SA-112. Should I Wear Pants? And Where Should I Travel in the Portuguese Expanse? Automating Business Rules and Decision Rules Through Reusable Decision Table Data Structures
Troy Hughes, Datmesis Analytics
Louise Hadden, Abt Associates Inc.
SA-149. Sample size and HR confidence interval estimation through simulation of censored survival data controlling for censoring rate
Giulia Tonini, Menarini Ricerche
Letizia Nidiaci, Menarini Ricerche
Simona Scartoni, Menarini Ricerche
SA-207. A Brief Introduction to Performing Statistical Analysis in SAS, R & Python
Erica Goodrich, Brigham and Women's Hospital
Daniel Sturgeon, Priority Health
SA-225. Principal Stratum strategy for handle intercurrent events: a causal estimand to avoid biased estimates
Andrea Nizzardo, Menarini Ricerche
Giovanni Marino Merlo, Menarini Ricerche
Simona Scartoni, Menarini Ricerche
SA-262. Using SAS Simulations to determine appropriate Block Size for Subject Randomization Lists
Kevin Venner, Almac Clinical Technologies
Jennifer Ross, Almac Clinical Technologies
Kyle Huber, Almac Clinical Technologies
Noelle Sassany, Almac Clinical Technologies
Graham Nicholls, Almac Clinical Technologies
SA-284. Implementing Quality Tolerance Limits at a Large Pharmaceutical Company
Steven Gilbert, Pfizer
Strategic Implementation
SI-170. Moving A Hybrid Organization Towards CDISC StandardizationKobie O'Brian, SCHARP Fred Hutchinson
Sara Shoemaker, SCHARP Fred Hutchinson
Robert Kleemann, SCHARP Fred Hutchinson
Kate Ostbye, SCHARP Fred Hutchinson
SI-173. PROC Future Proof;
Amy Gillespie, Merck & Co., Inc.
Susan Kramlik, Merck & Co., Inc.
Suhas Sanjee, Merck & Co., Inc.
SI-206. Assessing Performance of Risk-based Testing
Amber Randall, SCHARP - Fred Hutch Cancer Research Center
Bill Coar, Axio Research
SI-241. You down to QC? Yeah, You know me!
Vaughn Eason, Catalyst Clinical Research, LLC
Jake Gallagher, Catalyst Clinical Research, LLC
Submission Standards
SS-030. End-to-end Prostate-Specific Antigen (PSA) Analysis in Clinical Trials: From Mock-ups to ADPSAJoy Zeng, Pfizer
Varaprasad Ilapogu, Ephicacy Consultancy Group
Xinping Cindy Wu, Pfizer
SS-045. Updates in SDTM IG V3.3: What Belongs Where – Practical Examples
Lucas Du, Vertex Pharmaceuticals INC
William Paget, Vertex Pharmaceuticals INC
Lingyun Chen, Vertex Pharmaceuticals INC
Todd Case, Vertex Pharmaceuticals Inc
SS-054. RTOR: Our Side of the Story
Shefalica Chand, Seattle Genetics, Inc.
Eric Song, Seattle Genetics, Inc.
SS-081. Why Are There So Many ADaM Documents, and How Do I Know Which to Use?
Sandra Minjoe, PRA Health Sciences
SS-097. Data Review: What’s Not Included in Pinnacle 21?
Jinit Mistry, Seattle genetics
Lyma Faroz, Seattle Genetics
Hao Meng, Seattle Genetics Inc.
SS-140. Pinnacle 21 Community v3.0 - A Users Perspective
Ajay Gupta, PPD Inc
SS-150. Challenges and solutions for e-data submission to PMDA even after submission to FDA
Akari Kamitani, Shionogi
HyeonJeong An, Shionogi Inc.
Yura Suzuki, Shionogi & Co., Ltd.
Malla Reddy Boda, Shionogi Inc.
Yoshitake Kitanishi, Shionogi & Co., Ltd.
SS-151. Supplementary Steps to Create a More Precise ADaM define.xml in Pinnacle 21 Enterprise
Majdoub Haloui, Merck & Co., Inc.
Hong Qi, Merck & Co., Inc.
SS-156. Analysis Package e-Submission – Planning and Execution
Abhilash Chimbirithy, Merck & Co., Inc.
Saigovind Chenna, Merck & Co., Inc.
Majdoub Haloui, Merck & Co. Inc.
SS-159. Automating CRF Annotations using Python
Hema Muthukumar, Statistical Center For HIV/AIDS Research and Prevention(SCHARP) at Fred Hutch
Kobie O'Brian, SCHARP, Fred Hutch
Julie Stofel, Fred Hutchinson Cancer Research Center
SS-197. Preparing a Successful BIMO Data Package
Elizabeth Li, PharmaStat, LLC
Carl Chesbrough, PharmaStat, LLC
Inka Leprince, PharmaStat, LLC
SS-317. Improving the Quality of Define.xml: A Comprehensive Checklist Before Submission
Ji Qi, BioPier Inc.
Yan Li, BioPier Inc.
Lixin Gao, BioPier Inc.
SS-325. Getting It Right: Refinement of SEND Validation Rules
Kristin Kelly, Pinnacle 21