Baltimore, Maryland
May 14-17, 2017
Applications Development
AD01. Here Comes The Smart Mock Table: A Novel Way of Creating Clinical Summary Tables Without Any Table Programming (No Kidding!)*** BEST PAPER ***
Joseph Hinson, inVentiv Health
AD02. Automated Generation of Clinical Study Reports using SAS® and RTF (Literate programming)
Rajaram Venkatesan, Cognizant Technology Solution
Julien Sauser, Nestle Research Center
Carlos Antonio De Castro, Nestle Research Center
AD03. Three Issues and Corresponding Work-Around Solution for Generating Define.xml 2.0 Using Pinnacle 21 Enterprise
Jeff Xia, Merck
Lugang Larry Xie, Merck & Co.
Shari Lisnov, Merck &Co.
AD04. A Vivid and Efficient Way to Highlight Changes in SAS Dataset Comparison
Jeff Xia, Merck
Lugang Larry Xie, Merck & Co.
Shunbing Zhao, Merck & Co.
AD05. An Efficient Solution to Efficacy ADaM Design and Implementation
Chengxin Li, Pfizer
Zhongwei Zhou, Pfizer
AD06. Obtaining an Automated Summary of PROC COMPARE Results: &SYSINFO and VB Script
Umesh Gautam, Trial Runners
Jeff Roberts, Chiltern International
AD07. Work with me here...(or there or anywhere): Practical Cloud Collaboration with JMP Clinical for Clinical Trial Data Reviews
Kelci Miclaus, SAS
Drew Foglia, SAS Institute, Inc.
AD08. Building a Better Dashboard Using SAS® Base Software
Kirk Paul Lafler, Software Intelligence Corporation
Josh Horstman, Nested Loop Consulting
Roger Muller, Data To Events, Inc
AD09. Important and Valuable Things You Can Do with SAS® Metadata DICTIONARY Tables and SASHELP Views
Kirk Paul Lafler, Software Intelligence Corporation
AD10. Laboratory Data Standardization with SAS
*** BEST PAPER ***
Charley Wu, Astellas
AD11. Generating Homeomorphically Irreducible Trees (Solving the Blackboard Problem in the Movie "Good Will Hunting")
John R Gerlach, Dataceutics, Inc.
AD12. Using SAS® ODS EXCEL Destination with SAS University Edition® to send graphs to Excel
William E Benjamin Jr, Owl Computer Consultancy LLC
AD13. Using SAS® for Application Programming Interface Requests
Mike Jadoo, SAS user
AD14. R Shiny Clinical Review Tools on the Horizon (slides)
Jimmy Wong, FDA
AD15. Excel-VBA Tool to Auto-Create Validation log and Review Form using List of TLF's.
Balaji Ayyappan, inVentiv Clinical
AD16. Migration to SAS Grid: Steps, Successes, and Obstacles for Performance Qualification Script Testing
Amanda Lopuski, Chiltern
Yongxuan Mike Tan, Chiltern
AD17. Metadata integrated programming
Jesper Zeth, Novo Nordisk A/S
Jan Skowronski, Novo Nordisk A/S
AD18. Leveraging Centralized Programming Resources Using SAS Integration Technologies
Tony Chang, Global Statistical Programming, Amgen Inc
AD19. SAS Application to Automate a Comprehensive Review of DEFINE and All of its Components
Walter Hufford, Novartis Pharmaceuticals
Vincent Guo, Novartis Pharmaceuticals
Mijun Hu, Novartis Pharmaceuticals
AD20. Challenge for Dramatic Streamlining of TLFs Creation Process with SAS and Java
Yohei Takanami, Takeda Pharmaceutical Company, Ltd.
Fumihiro Yamasaki, Takeda Pharmaceutical Company, Ltd.
AD21. Integration of multiple files into a study report using Word VBA macros
Heather Wood, DCRI
Jack Shostak, DCRI
AD22. A novel method to track mapping of all CRF variables into SDTM datasets.
Aishhwaryapriya Elamathivadivambigai, Seattle Genetics
AD23. Supporting the CDISC Validation Life-Cycle with Microsoft Excel VBA
Eric Crockett, Chiltern International
AD24. SAS Macro for Derivation of Best Overall Response per RECIST v. 1.1
Bob Zhong, Johnson & Johnson
Jiangfan Li, Johnson & Johnson
Hong Xie, Johnson & Johnson
Peter De Porre, Johnson & Johnson
Kenneth Maahs, Johnson & Johnson
Kyounghwa Bae, Johnson & Johnson
AD25. Controlled Terminology Without Excel
Mike Molter, Wright Ave Partners
AD26. Stop waiting - Get notification email at the end of SAS® code execution using SAS® EMAIL Engine
Nirav Darji, GCE Solutions pvt ltd
Beyond the Basics
BB01. One Project, Two Teams: The Unblind leading the BlindKristen Harrington, Rho, Inc.
BB02. Making Documents 'Intelligent' with Embedded Macro Calls, DOSUBL and Proc STREAM: An example with the CONSORT Flow Diagram
*** BEST PAPER ***
Joseph Hinson, inVentiv Health
BB03. The REPORT Procedure and ODS Destination for Microsoft Excel: The Smarter, Faster Way to Create First-Rate Excel Reports
Jane Eslinger, SAS Institute
BB04. Reporting Non-Printable and Special Characters for Review in Excel
Abhinav Srivastva, Gilead Sciences
BB05. I've Got to Hand It to You; Portable Programming Techniques
Art Carpenter, CA Occidental Consultants
BB06. Good artists copy;Great artists steal; (implement pseudo R ,Jquery & code in Base SAS without installing R or other Applications
Hui Liu, MSD
BB07. Simplifying Your %DO Loop with CALL EXECUTE
Arthur Li, City of Hope
BB08. Using Hash tables for AE search strategies
Vinodita Bongarala, Seattle Genetics
Liz Thomas, Seattle Geneticd
BB09. Expansion of Opportunities in Programming: DS2 Features and Examples of Usage Object Oriented Programming in SAS
Serhii Voievutskyi, Experis Clinical
BB10. A Macro to carry values through observations forwards or backwards over null values within a by group or a SAS(R) dataset.
Timothy Harrington, SAS Programmer
BB11. SAS Programmer's Guide to Life on the SAS Grid
*** BEST PAPER ***
Eric Brinsfield, Meridian Analytics
BB12. Programming LYRIC Response in Immunomodulatory Therapy Trials
Yang Wang, Seattle Genetics
BB13. Harnessing the Power of the Manifest File in the SAS® Life Science Analytics Framework
Kevin Clark, SAS Institute
BB14. A SAS®sy Study of eDiary Data
Amie Bissonett, inVentiv Health Clinical
BB15. A Case of assessing adverse events of interest based on their grade changes
Sriramu Kundoor, Seattle Genetics
BB16. Don't Get Lost! A High-efficiency, Low-tech Solution for Navigating Your Department's Many-Many-Many SOPs and Guidance Documents
Michael Hagendoorn, Amgen, Inc.
Tim Yerington, Amgen, Inc.
BB17. Writing Efficient Queries in SAS Using PROC SQL with Teradata
Mina Chen, Roche
BB18. Clinical Data Visualization using TIBCO Spotfire® and SAS®
Ajay Gupta, PPD Inc
BB20. Automating Title and Footnote Extraction Using Visual Basic for Applications (VBA) and SAS"
Tony Cardozo, Spaulding Clinical
BB21. Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS® Code
Josh Horstman, Nested Loop Consulting
Career Planning
CP01. Creating a Winning Resume Reflecting Your Skills and Experience With Honesty and Integrity.Kelly Spak, Chiltern International Ltd.
CP02. The Road Not Often Taken
Janet Stuelpner, SAS
CP03. What Makes a Candidate Stand Out?
*** BEST PAPER ***
Edward Slezinger, Fred Hutchinson Cancer Research Center - SCHARP
CP04. The Elusive Data Scientist: Real-world analytic competencies
Greg Nelson, ThotWave
CP05. Strategies and decisions for marketing yourself as an independent contractor
Kathy Bradrick, Triangle Biostatistics, LLC
CP06. The Road Not Taken: SAS for Pharmacometrics
Vishak Subramoney, Certara Strategic Consulting
Sree Harsha Sreerama Reddy, Certara Strategic Consulting
CP07. Becoming a Successful Manager of SAS® Programmers from an Ex-Programmer's Perspective
Christine Young, Chiltern, International
CP08. Schoveing Series 3: Living by Knowing When to STOP
Priscilla Gathoni, AstraZeneca, Statistical Programming
CP09. Show Me The Job!!!
Richelle Serrano, Clindata Insight
Kevin Lee, Clindata Insight
Data Integrity
DA01. Real Time Clinical Trial Oversight with SASAshok Gunuganti, Trevena
DA02. Let's Check Data Integrity Using Statistical (SAS®) Programmers with SAS®
Harivardhan Jampala, chiltern international Pvt ltd
DA03. Conversion of CDISC specifications to CDISC data - specifications driven SAS programming for CDISC data mapping
Yurong Dai, Eli Lilly
Jameson Cai, Eli Lilly
DA04. Data Change Report
Eric Kammer, Novartis
DA05. Clinical and Vendor Database Harmony; Can't we all just get along?
Brian Armstrong, QST Consultations, Ltd.
Renee Kerwin, QST Consultations, Ltd.
DA06. Check Your Data: Tools for Automating Data Assessment
*** BEST PAPER ***
Paul Stutzman, Axio Research
DA07. Data Integrity: One step before SDTM
Pavan Kathula, Anna University at Chennai
Sonal Torawane, University of Pune
Data Standards
DS-Panel. Gray Areas of ADaM and Ways to Provide ColorModerator: Susan Kenny, Maximum Likelihood, Inc.
Panelists:
- Nate Freimark, The Griesser Group
- Sandra Minjoe, Senior ADaM Consultant, Accenture Accelerated R&D Services
- Jack Shostak, DCRI
- John Troxell, Accenture
DS01. The Untapped Potential of the Protocol Representation Model
Frank Diiorio, CodeCrafters, Inc.
Jeffrey Abolafia, Rho, Inc.
DS02. CDISC's CDASH and SDTM: Why You Need Both!
(No paper available)
Kit Howard, CDISC
Shannon Labout, CDISC
DS03. SDTM - Just a Walk in the (Theme) Park, Exploring SDTM in the Most Magical Place on Earth
Christine Mcnichol, Chiltern International
DS04. The CDISC Trial Design Model (TDM), the EPOCH variable, and the Treatment Emergent Flag: How to Leverage these to Improve Review
Thomas Guinter, Independent
DS05. Implementation of STDM Pharmacogenomics/Genetics Domains on Genetic Variation Data
Linghui Zhang, Merck
DS06. Harmonizing CDISC Data Standards across Companies: A Practical Overview with Examples
Keith Shusterman, Chiltern
Prathima Surabhi, AstraZeneca
Binoy Varghese, MedImmune
DS07. Where Is the Link Broken - Another Look at SDTM Oncology Tumor Packages
*** BEST PAPER ***
Hong Wang, Boehringer Ingelheim
Ke Xiao, Boehringer Ingelheim
DS08. The CDISC SDTM Exposure Domains (EX & EC) Demystified. How EC Helps You Produce A Better (more compliant) EX.
Thomas Guinter, Independent
DS09. RELREC - SDTM Programmer's Bermuda Triangle
Charumathy Sreeraman, Ephicacy Lifescience Analytics
DS10. Ahead of the Curve: Leading with Industry Data Requirements
Maria Dalton, GlaxoSmithKline
Nancy Haeusser, GlaxoSmithKline
DS11. Leveraging metadata when mapping to CDISC Standards with SAS® machine learning in a Results as a Service plus Model (RAAS+)
Ben Bocchicchio, SAS Institute
Sandeep Juneja, SAS Institute
Preetesh Parikh, SAS Institute
DS12. Considerations and Conventions in the Submission of the SDTMIG Tumor and Response Domains
Jerry Salyers, Accenture Life Sciences
Fred Wood, Accenture Life Siences
DS13. Planning to Pool SDTM by Creating and Maintaining a Sponsor-Specific Controlled Terminology Database
Cori Kramer, Chiltern International
Ragini Hari, Chiltern International
Keith Shusterman, Chiltern
DS14. Considerations in Submitting Standardized Electronic Data Under the Animal Rule: The Use of Domains in the SDTMIG and the SENDIG
Fred Wood, Accenture Life Siences
DS15. Common Programming Errors in CDISC data
Sergiy Sirichenko, Pinnacle 21
DS16. ADaM Compliance - Validating your Specifications
Trevor Mankus, PRA Health Sciences
Kent Letourneau, PRA Inernational
DS17. ADaM Grouping: Groups, Categories, and Criteria. Which Way Should I Go?
*** BEST PAPER ***
Jack Shostak, DCRI
DS18. Clarifications About ADaM Implementation Provided in ADaMIG Version 1.1
John Troxell, Accenture
DS19. Standardized, Customized or Both? Defining and Implementing (MedDRA) Queries in ADaM Data Sets
Richann Watson, Experis
Karl Miller, inVentiv Health
DS20. LBTEST/LBSTRESU and ADaM lab parameters: The dilemma of mapping one-to-many or many-to-one
Michelle Barrick, Eli Lilly and Company
DS21. Programming Efficiency in the Creation of ADaM BDS Datasets
Ellen Lin, Amgen Inc
DS22. Deriving Rows in CDISC ADaM BDS Datasets
Sandra Minjoe, Accenture
DS23. The Benefits of Traceability Beyond Just From SDTM to ADaM in CDISC Standards
Maggie Ci Jiang, Teva Pharmaceuticals
DS24. ADQRS: Basic Principles for Building Questionnaire, Rating and Scale Analysis Datasets
Nancy Brucken, inVentiv Health Clinical
Karin Lapann, Shire
DS25. A Critique of Implementing the Submission Data Tabulation Model (SDTM) for Drugs and Medical Devices
Carey Smoak, DataCeutics, Inc.
Data Visualizations & Graphics
DV01. Effective Ways to Perfect the Visualization of Clinical Trial ResultsAmos Shu, AstraZeneca
DV02. A Combined AE + CM Graph using SAS
Sanjay Matange, SAS
DV03. Multipage Adverse Event Reports Using PROC SGPLOT
Warren Kuhfeld, SAS
Mary Beth Herring, Rho, Inc.
DV04. Mean and Individual Subject Graphs of Concentration vs. Time Data Using PROC SGPLOT
Pooja Trivedi, Cadila Healthcare Limited
DV05. SAS vs Tableau: Creating Adverse Event/ Concomitant Medication Time line plot
Liling Wei, Pharmacyclics
Kathy Chen, Pharmacyclics, Inc. A Abbvie Company
DV06. Visualizing Enrollment Over Time
Laura Gruetzner, BioStat Solutions, Inc.
DV07. Using Animated Graphics to Show PKPD Relationships in SAS 9.4
Andrew Mccarthy, Eli Lilly
DV08. Bird's eye view of the data, a graphical exploration!
Hrideep Antony, Inventiv Health USA
DV09. Mapping Participants to the Closest Medical Center
David Franklin, Quintiles Real World Late Phase Research
DV10. Automated DSMB Presentation in SAS: Yeah, I'd Submit That! How to auto populate PowerPoint presentations so you don't have to
*** BEST PAPER ***
Brett Jepson, Rho Inc.
Kaitie Lawson, Rho, Inc.
DV11. Translating Statistics into Knowledge by Examples Using SAS Graphic Procedures
Tao Shu, Eli Lilly and Company
Jianfei Jiang, Eli Lilly
DV12. Make it personal: Upgrade your figures by adding individual patient data to the common figure types
Yuliia Bahatska, inVentiv Health Clinical
Christiane Ahlers, Bayer AG
DV13. The basics of Graphics Template Language and output by PROC DOCUMENT
Fangping Chen, UBC
DV14. When one is not enough or multi-celled plots: comparison of different approaches
Vladlen Ivanushkin, inVentiv Health Germany GmbH
DV15. The %NEWSURV Family of Macros: An Update on the Survival Plotting Macro %NEWSURV and an Introduction to Expansion Macros
Jeffrey Meyers, Mayo Clinic
FDA Wednesday
FDA01. Statistical Review and Data Standards: It’s Gettin’ Better All The Time (slides)Steve Wilson, Dr.P.H., CAPT USPHS, Director, Division of Biometrics III, FDA/OMPT
FDA02. Update on Data Standards (slides)
Ron Fitzmartin, PhD, MBA, Sr. Advisor, Office of Strategic Programs, CDER / FDA
FDA03. Innovation and Regulatory Review: What is JumpStart? (slides)
Alan Shapiro, MD, PhD, FAAP, Medical Officer, Office of Computational Sciences, CDER / FDA
Hands-on Training
HT01. Five Ways to Create Macro Variables: A Short Introduction to the Macro LanguageArt Carpenter, CA Occidental Consultants
HT02. Point-and-Click Programming Using SAS® Enterprise Guide®
Kirk Paul Lafler, Software Intelligence Corporation
Mira Shapiro, Senior SAS® Consultant, Capacity Planner and SAS Programmer
Ryan Paul Lafler, High School Student and Software Enthusiast
HT03. Survival 101 - Just Learning to Survive
Leanne Goldstein, City of Hope
Rebecca Ottesen, City of Hope
HT04. New for SAS® 9.4: Including Text and Graphics in Your Microsoft Excel Workbooks, Part 2
Vince Delgobbo, SAS
HT05. SAS Studio - the next evolution of SAS programming environments
(No paper available)
Jim Box, SAS Institute
HT06. Usage of Pinnacle 21 Community Toolset 2.x.x for Clinical Programmers
Sergiy Sirichenko, Pinnacle 21
Michael Digiantomasso, Pinnacle 21
HT07. Single File Deliverables: Next Steps
Bill Coar, Axio Research
HT08. SAS® Life Science Visual Analytics
Pritesh Desai, sas
Healthcare Analytics
HA01. Developing Your Data StrategyGreg Nelson, ThotWave
HA02. Multinomial Logistic Regression Models With Sas® Proc Surveylogistic
*** BEST PAPER ***
Marina Komaroff, Noven Pharmaceuticals
HA03. Topology-based Clinical Data Mining for Discovery of Hidden Patterns in Multidimensional Data
Sergey Glushakov, Intego Group
Iryna Kotenko, Intego Group / Experis Clinical, Site Lead
Andrey Rekalo, Intego Group, Senior Data Scientist
HA04. Two Roads Diverged in a Narrow Dataset...When Coarsened Exact Matching is More Appropriate than Propensity Score Matching
Aran Canes, Cigna
HA07. Analyzing data from multiple clinical trials using SAS® Real World Evidence
Jay Paulson, SAS Institute
David Olaleye, SAS Institute
HA08. Put the "K" in Your KAB: Know How to Efficiently Program with Knowledge, Attitudes, and Behavior Survey Data
Cara Lacson, United BioSource Corporation
Jasmeen Hirachan, United BioSource Corporation
HA10. Removing the Mask of Average Treatment Effects in Chronic Lyme Disease Research Using Big Data and Sub-Group Analysis
Mira Shapiro, Analytic Designers LLC
Lorraine Johnson, LymeDisease.org
HA12. A Business Solution for Improved Drug Supply Chain Visibility
David Butler, Teradata Corporation
Joy King, Teradata Corporation
Ronald Chomiuk, Teradata Corporation
Richard Neafus, Teradata Corporation
Industry Basics
IB01. A Practical Guide to Healthcare Data: Tips, traps and techniquesGreg Nelson, ThotWave
IB02. Good Programming Practices at Every Level
*** BEST PAPER ***
Maria Dalton, GlaxoSmithKline
IB03. How to define Treatment Emergent Adverse Event (TEAE) in crossover clinical trials
Mengya Yin, Ultragenyx Pharmaceutical
Wen Tan, Ultragenyx Pharmaceutical
IB04. How to find the best MDR solution for your organization
Kevin Lee, Clindata Insight
IB05. SDTM Cartography - Learn To Create SDTM Mapping Specifications
Donna Sattler, Eli Lilly
Mike Lozano, Eli Lilly and Company
IB06. Data Monitoring Committee Report Programming: Considering a Risk-Based Approach to Quality Control
Amber Randall, Axio Research
Bill Coar, Axio Research
IB07. Building a Fast Track for CDISC: Practical Ways to Support Consistent, Fast and Efficient SDTM Delivery
Steve Kirby, Chiltern International Ltd
Mario Widel, Eli Lilly
Richard Addy, Chiltern International Ltd
Management & Support
MS01. A Review of "Free" Massive Open Online Content (MOOC) for SAS® LearnersKirk Paul Lafler, Software Intelligence Corporation
MS02. The Cross Border Program - Strengthening the Sponsor-Partner Offshore Experience
Kenneth Bauer, Merck
Girish Havildar, Merck
MS03. Stakeholder Management: How to be an effective lead SAS® programmer
Aakar Shah, Pfizer
MS04. Managing conflicts across Cross Functional and Global Virtual Teams
Arun Raj Vidhyadharan, inVentiv Health
Sunil Jairath, Inventiv Health
MS06. Insight into Offsites: creating a productive workflow with remote employees.
Maddy Wilks, Agility Clinical, Inc
MS07. Woops, I Didn't Know! An Elegant Solution to Let Your Entire Department Benefit from Individual Lessons Learned
Michael Hagendoorn, Amgen, Inc.
Annia Way, Amgen, Inc.
Tim Yerington, Amgen, Inc.
Rachel Bowman, Amgen, Inc.
MS08. Mentoring and Oversight of Programmers across Cultures and Time Zones
Chad Melson, Experis
MS09. Beyond "Just fix it!" Application of Root Cause Analysis Methodology in SAS® Programming.
Nagadip Rao, Eliassen Group
MS11. Lead Programmer Needs Help: Dedicated Programming Project Manager to the Rescue!
*** BEST PAPER ***
Gloria Boye, Vita Data Sciences (a division of Softworld, Inc)
Aparna Poona, Softworld, Inc. (Life Sciences)
Bhavin Busa, Vita Data Sciences (a division of Softworld, Inc.)
MS12. Sponsor Oversight of CROs Data Management and Biostatistical Abilities
Lois Lynn, Noven Pharmaceuticals, Inc.
Posters
PO01. CDISC Compliant NCA PK Parameter Analysis When Using Phoenix® WinNonlin®Renfang Hwang, Celgene Coporation
PO02. Takeaways from Integrating Studies Conducted by Bristol-Myers Squibb (BMS) and ONO
Yan Wang, Bristol-Myers Squibb
PO04. Implementing Patient Report Outcome data in clinical trials analysis
Qi Wang, Amgen
PO05. Data Transparency, de-identification strategies, and platforms available for sharing data
Arun Raj Vidhyadharan, inVentiv Health
Sunil Jairath, Inventiv Health
PO06. Statistician's secret weapon: 20 ways of detecting raw data issues
Lixiang Liu, Eli Lilly
PO07. ClinicalTrials.gov Results: an End of Study Deliverable That Should Be Considered at Study Startup
Maya Barton, Rho
Elizabeth Paynter, Rho
PO08. In-Licensing from a Programming Perspective
Asif Karbhari, AstraZeneca
PO09. AIR Binder: An Automatic Reporting and Data Analysis SAS Application for Cytochrome P450 Inhibition Assay to Investigate DDI
Hao Sun, Covance, Inc.
Kristen Cardinal, Covance, Inc.
Carole Kirby, Covance, Inc.
Richard Voorman, Covance, Inc.
PO10. Charting Your Path to Using the "New" SAS® ODS and SG Graphics Successfully - Interactively Generate the Code
Roger Muller, Data To Events, Inc
PO11. Facebook Data Analysis with SAS® Visual Analytics
*** BEST PAPER ***
Prasoon Sangwan, TATA Consultancy Services Ltd
Vikrant Bisht, TATA Consultancy Services Ltd
Piyush Singh, TCS
Ghiyasuddin Mohammed Faraz Khan, Sapphire Software Solutions Inc.
PO13. Developing ADaM Dataset for Cardiovascular Outcome Studies
Joanne Zhou, GSK
Rakesh Kumar, Mr.
David Wade, Mr.
David Chen, Mr.
PO14. Mindfulness at Work: Handling Stress and Changes Gracefully and Be the Leader You Were Born To Be
*** BEST PAPER ***
Helena Ho, Astrazeneca
PO15. Pilot Meta-Analysis of HPA Axis Suppression Studies on Topical Corticosteroids using ADaM Datasets derived from Legacy Data
Lillian Qiu, FDA
Hon-Sum Ko, FDA
PO16. A Precision-strike Approach to Submission Readiness: How to Prepare Your Filing Teams for Consistent Excellence
Michael Hagendoorn, Amgen, Inc.
Tony Chang, Amgen, Inc.
PO17. Power of Interleaving and RETAIN Combination in Data Manipulation
Zongming Pan, ConnectiveRx
PO18. A custom ADaM domain for time to event analysis in adverse events
John Saida Shaik, Seattle Genetics Inc.
PO19. Analyzing Singly Imputed Utility Based Weighted Scores Using EQ-5D for Determining Patients' Quality of Life in Oncology Studies
Vamsi Krishna Medarametla, Seattle Genetics
Liz Thomas, Seattle Genetics
Gokul Vasist, Seattle Genetics
PO20. Dealing with alignment of statistics: Beyond the decimal point.
Eric Pedraza, INC Research
PO21. Metadata of Titles, Footnotes, and Proc Report Details
Julius Kirui, SCRI
PO22. SAS and R Playing Nice Together
David Edwards, Amgen, Inc.
Bella Feng, Amgen, Inc
Brian Schultheiss, Amgen, Inc.
PO24. A Guide To Programming Patient Narratives
Renuka Tammisetti, PRA Health Sciences
Karthika Bhavadas, PRA Health Sciences
PO26. REDCAP Ins and Outs
Leanne Goldstein, City of Hope
PO27. Roadmap for Managing Multiple CRO Vendors
Veena Nataraj, Shire
Karin Lapann, Shire
Quick Tips
QT01. Remove the Error: Variable length is too long for actual dataEric Larson, inVentiv Health
QT02. Importing CSV Data to All Character Variables
Art Carpenter, CA Occidental Consultants
QT03. What Are Occurrence Flags Good For Anyway?
Nancy Brucken, inVentiv Health Clinical
QT04. Is There a Date In That Comment? Use SAS To Find Out.
*** BEST PAPER ***
Keith Hibbetts, Eli Lilly and Company
QT05. My bag of SAS lifehacks
Dmytro Hasan, Experis
QT06. Generating Colors from the Viridis Color Scale with a SAS® Macro
Shane Rosanbalm, Rho, Inc
QT07. PROC DOC III: Self-generating Codebooks Using SAS®
Louise Hadden, Abt Associates Inc.
QT09. Basic SDTM House-Keeping
Emmy Pahmer, inVentiv Health
QT10. Everyone can use a little Currency - when dependent data set updates silently make your analysis data set out of date.
Scott Worrell, PAREXEL
QT11. Multi-Dimensional Arrays: Add Derived Parameters
Siddharth Kumar Mogra, GCE Solutions Inc
QT12. Same statistical method different results? Don't panic the reason might be obvious.
Iryna Kotenko, Experis Clinical A Manpower Group Company
QT14. Visual Basic for Applications: A Solution for Handling Non-ASCII Removal and Replacement
Eric Crockett, Chiltern International
QT15. How to Create a Journal-Quality Forest Plot with SAS® 9.4
John O'Leary, Department of Veterans Affairs
Warren Kuhfeld, SAS
QT16. Common mistakes by programmers & Remedies
Sairam Veeramalla, GCE SOLUTIONS
Statistics & Pharmacokinetics
SP01. Multiple Imputation: A Statistical Programming Story*** BEST PAPER ***
Chris Smith, Cytel Inc.
Scott Kosten, DataCeutics Inc.
SP02. Multiplicity Controlled Analyses Using SAS/IML
Xingxing Wu, Eli Lilly and Company
Hangtao Xu, Eli Lilly and Company
SP04. Population PK/PD Analysis - SAS® with R and NONMEM® Make Customization Easy
Sharmeen Reza, Cytel Inc.
SP05. Combining Survival Analysis Results after Multiple Imputation of Censored Event Times
Jonathan L Moscovici, QuintilesIMS
Bohdana Ratitch, QuintilesIMS
SP06. Adverse Event Data over Time
Kriss Harris, SAS Specialists Ltd
Submission Standards
SS-Panel. Submission Musings(No paper available)
SS01. How will FDA Reject non-CDISC submission?
Kevin Lee, Clindata Insight
SS02. Preparing Analysis Data Model (ADaM) Data Sets and Related Files for FDA Submission
Sandra Minjoe, Accenture
SS03. Use of Traceability Chains in Study Data and Metadata for Regulatory Electronic Submission
Tianshu Li, Celldex
SS04. Good versus better SDTM: Including Screen Failure Data in the Study SDTM?
Henry Winsor, Relypsa Inc.
Mario Widel, Eli Lilly
SS05. Good Data Validation Practice
Sergiy Sirichenko, Pinnacle 21
Max Kanevsky, Pinnacle 21
SS06. Awareness from Electronic Data Submission to PMDA and FDA -- Lesson & Learnt from hands-on experiences --
*** BEST PAPER ***
Yuichi Nakajima, Novartis
Takashi Kitahara, Novartis
Ryan Hara, Novartis AG
SS07. Overview and Application of the HCV Vertical Resistance Analysis Template
Yan Xie, Abbvie
SS08. Creating Define-XML version 2 including Analysis Results Metadata with the SAS® Clinical Standards Toolkit
Lex Jansen, SAS Institute Inc.
SS09. Leveraging Study Data Reviewer's Guide (SDRG) in Building FDA's Confidence in Sponsor's Submitted Datasets
Xiangchen (Bob) Cui, Alkermes, Inc
Min Chen, Alkermes
Letan (Cleo) Lin, Alkermes Inc.
SS10. The Do's/Don'ts, An SDTM Validation Perspective. The should/shouldn't when explaining issues in the Study Data Reviewers Guide
Thomas Guinter, Independent
SS11. Documenting Traceability for the FDA: Clarifying the Legacy Data Conversion Plan & Introducing the Study Data Traceability Guide
David Izard, Chiltern
Kristin Kelly, Merck
Jane Lozano, Eli Lilly
SS12. Quality Check your CDISC Data Submission Folder Before It Is Too Late!
Bhavin Busa, Vita Data Sciences (a division of Softworld, Inc.)
Techniques & Tutorials
TT01. SAS® and ISO8601: A practical approachDerek Morgan, PAREXEL
TT02. Building Intelligent Macros: Using Metadata Functions with the SAS® Macro Language
Art Carpenter, CA Occidental Consultants
TT03. SAS® Debugging 101
Kirk Paul Lafler, Software Intelligence Corporation
TT04. SAS® Studio: We Program
Jim Box, SAS Institute
TT05. Special Symbols in Graphs: Multiple Solutions
Abhinav Srivastva, Gilead Sciences
TT06. Check Please: An Automated Approach to Log Checking
Richann Watson, Experis
TT07. Converting Non-Imputed Dates for SDTM Data Sets With PROC FCMP
Noory Kim, CROS NT
TT08. Clinical Trials Data: It's a Scary World Out There! or Code that Helps You Sleep at Night
Scott Horton, United BioSource
TT09. Hashtag #Efficiency! An Exploration of Hash Tables and Other Techniques
Lakshmi Nirmala Bavirisetty, Independent SAS User
Kaushal Chaudhary, Independent SAS User
Deanna Schreiber-Gregory, Henry M Jackson Foundation for the Advancement of Military Medicine
TT10. Data Quality Control: Using High Performance Binning to Prevent Information Loss
Deanna Schreiber-Gregory, Henry M Jackson Foundation for the Advancement of Military Medicine
Lakshmi Nirmala Bavirisetty, Independent SAS User
Kaushal Chaudhary, Independent SAS User
TT11. Tips and Best Practices using SAS® Analytics
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TT13. The Proc Transpose Cookbook
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David Franklin, Quintiles Real World Late Phase Research
TT15. Merge With Caution: How to Avoid Common Problems when Combining SAS Datasets
Josh Horstman, Nested Loop Consulting
TT16. Dear Dave, Please See the .LST File for Our Validation Differences. Thanks, Bad Validation Programmer
*** BEST PAPER ***
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David Carr, Ephicacy
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