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 Analysis
Jeffrey 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® App
Girish 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
Shunbing Zhao, Merck & Co.

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 Boosting
Karen 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 Data
Janet 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

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, SyneosHealth

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-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_SPECS
Jeffrey 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
Bo Zheng, Merck

DV-135. Making Customized ICH Listings with ODS RTF
Huei-Ling Chen, Merck
William Wei, Merck & Co, Inc.

DV-148. Butterfly Plot for Comparing Two Treatment Responses
Raghava Pamulapati, Merck

DV-157. Automating of Two Key Components in Analysis Data Reviewer’s Guide
Shunbing Zhao, Merck & Co.
Jeff Xia, Merck

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., Kenilworth, NJ, USA
Yilong Zhang, Merck & Co., Inc., Kenilworth, NJ, USA
Simiao Ye, Merck & Co., Inc., Kenilworth, NJ

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 studies
Azia 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
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 Routines
Troy 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 Experience
Richann 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

LS-059. An Effective Management Approach for a First-Time Study Lead
Himanshu Patel, Merck & Co.
Jeff Xia, Merck

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 Status
Carey Smoak, S-Cubed

MD-041. Successful US Submission of Medical Device Clinical Trial using CDISC
Phil Hall, Edwards Lifesciences



Quick Tips

QT-035. A SAS Macro for Calculating Confidence Limits and P-values Under Simon’s Two-Stage Design
Alex Karanevich, EMB Statistical Solutions
Michael Ames, EMB Statistical Solutions

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 project
Jayanth 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 Projects
Chris Hardwick, Zeroarc
Justin Slattery, Zeroarc
Hans Gutknecht, Zeroarc



Statistics and Analytics

SA-013. Diaries and Questionnaires: Challenges and Solutions
Marina 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-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 ADPSA
Joy 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