Denver, Colorado
May 8-11, 2016
Applications Development
AD02. Efficient Safety Assessment in Clinical Trials using the Computer-generated AE Narratives of JMP® ClinicalRichard C. Zink, JMP Life Sciences, SAS Institute, Cary, NC
Drew Foglia, JMP Life Sciences, SAS Institute, Cary, NC
AD03. The Benefits and Know-how of Building a Central CDISC-Terminology Dictionary with a Macro System
Solomon Lee, Ph D, South San Francisco, CA
Ronghai Bo, Medicine, WI
AD04. SAS® integration with NoSQL data
Kevin Lee, Clindata Insight, Moraga, CA
AD06. All Aboard! Next Stop is the Destination Excel
William E Benjamin Jr, Owl Computer Consultancy, LLC, Phoenix, AZ
AD07. Enhanced OpenCDISC Validator Report for Quick Quality Review
Ajay Gupta, PPD, Morrisville, NC
AD08. The Power of Perl Regular Expressions: Processing Dataset-XML documents back to SAS® Data Sets
Joseph Hinson, inVentiv Health, Princeton, NJ
AD09. The Devil is in the Details – Reporting from Pinnacle 21 (OpenCDISC) Validation Report
Amy Garrett, Novella Clinical, Columbus, OH
Chris Whalen, Clovis Oncology, Boulder, CO
AD11. Best Practice Programming Techniques for SAS® Users
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Mary Rosenbloom, Lake Forest, CA
AD12. Building a Better Dashboard Using Base SAS® Software
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Roger Muller, Data-To-Events, Indianapolis, IN
Joshua M. Horstman, Nested Loop Consulting, Indianapolis, IN
AD13. The Little-Known DOCUMENT Procedure, a Utility for Manipulating Output Delivery System (ODS) Content
Roger D. Muller, Ph.D., Data To Events Inc., Carmel, IN
AD15. Automating Patient Narratives – The Medical Writer Loves Me!
Scott Burroughs, PAREXEL International, Durham, NC
AD17. The New STREAM Procedure as a Virtual Medical Writer
*** BEST PAPER ***
Joseph Hinson, inVentiv Health, Princeton, NJ
AD18. Consider Define.xml Generation during Development of CDISC Dataset Mapping Specifications
Vara Prasad Sakampally, Vita Data Sciences, Waltham, MA
Bhavin Busa, Vita Data Sciences, Waltham, MA
AD19. SAS® Office Analytics: An Application In Practice - Monitoring and Ad-Hoc Reporting Using Stored Process
Mansi Singh, Roche Molecular Systems Inc., Pleasanton, CA
Smitha Krishnamurthy, Roche Molecular Systems Inc., Pleasanton, CA
Chaitanya Chowdagam, MaxisIT Inc., Metuchen, NJ
Kamal Chugh, Roche Molecular Systems Inc., Pleasanton, CA
Beyond the Basics
BB01. Color, Rank, Count, Name; Controlling it all in PROC REPORTArthur L. Carpenter, California Occidental Consultants, Anchorage, AK
BB02. Performing Pattern Matching by Using Perl Regular Expressions
Arthur Li, City of Hope National Medical Center, Duarte, CA
BB03. An Intersection of Pharma and Medical Devices - Development of Companion Diagnostics in Conjunction with Targeted Therapies
Carey G. Smoak, Portola Pharmaceuticals, South San Francisco, CA
BB04. Go Compare: Flagging up some underused options in PROC COMPARE
Michael Auld, Ampersoft Ltd, London, UK
BB05. DOSUBL and the Function Style Macro
John Henry King, Ouachita Clinical Data Services, Inc. Caddo Gap, AK
BB06. Superior Highlighting: Identifying New or Changed Data in Excel Output using SAS®
Kim Truett, KCT Data, Inc., Alpharetta, GA
BB07. Meaningful Presentation of Clinical Trial Data with Multiple Y-Axes Graph
*** BEST PAPER ***
Mina Chen, Roche Product Development in Asia Pacific, Shanghai, China
BB08. Novel Programming Methods for Change from Baseline Calculations
Mina Chen, Roche Product Development in Asia Pacific, Shanghai, China
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada
BB09. I Object: SAS® Does Objects with DS2
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada
Xue Yao, Winnipeg Regional Health Authority, Winnipeg, MB, Canada
BB10. Name that Function: Punny Function Names with Multiple MEANings and Why You Do Not Want to be MISSING Out
Ben Cochran, The Bedford Group, Raleigh, NC
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK
BB11. Surviving the SAS® Macro Jungle by Using Your Own Programming Toolkit
Kevin Russell, SAS Institute Inc., Cary, NC
BB12. Preparing the Data to Ensure Correct Calculation of Relative Risk
Ravi Kankipati, Seattle Genetics, Inc., Bothell, WA
Abhilash Chimbirithy, Merck & Co., Inc., Rahway, NJ
BB13. The Power of Data Access Functions: An example with Dataset-XML Creation
Joseph Hinson, inVentiv Health, Princeton, NJ
BB14. Macro to get data from specific cutoff period
Kiranmai Byrichetti, SCRI, Nashville, TN
Jeffrey Johnson, SCRI, Nashville, TN
BB15. The Impact of Change from wlatin1 to UTF-8 Encoding in SAS Environment
*** BEST PAPER ***
Hui Song, PRA Health Sciences, Blue Bell, PA
Anja Koster, PRA Health Sciences, Zuidlaren, The Netherlands
BB16. UTF What? A Guide to Using UTF-8 Encoded Data in a SDTM Submission
Michael Stackhouse, Chiltern, Cary, NC
Career Planning
CP01. What's Hot – Skills for SAS® ProfessionalsKirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
CP02. Don’t be a Diamond in the Rough: Tips to Employment
Janet Stuelpner, SAS Institute, New Canaan, CT
CP03. Are You Thinking About Becoming an Independent Contractor? Things to Consider as you Plan for Your New Entrepreneurship
*** BEST PAPER ***
Kathy Bradrick, MSM, Chiltern International, Inc.
Dawn Edgerton, MBA, RAC, Edgerton Data Consulting, LLC
CP04. Career Path for SAS Profession in Pharmaceutical Industry
James Meiliang Yue, Gauss Inc., Queens, NY
CP05. Journey from the student to the full-time programmer
Viktoriia Vasylenko, Experis Clinical, Kharkiv, Ukraine
CP06. My First Job Dos-and-Don’ts: A Survival Guide for Your First Statistical Programming Job in the Industry
Assir Abushouk, PAREXEL International, Boston, MA
Thu-Nguyen Nguyen, PAREXEL International, Durham, NC
CP08. There is No Time Like Present-Being from Elf to the True Self
Rajinder Kumar, Novartis Healthcare Private Limited, Hyderabad, India
Sudarshan Reddy Shabadu, Inventiv Health Clinical, Hyderabad, India
Houde Zhang, Novartis Pharmaceuticals, East Hanover, NJ
CP09. A Review of "Free" Massive Open Online Content (MOOC) for SAS® Learners
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Data Standards
DS02. Tips and tricks when developing Trial Design Model Specifications that provide Reductions in Creation timeRuth Marisol Rivera Barragan, Chiltern International, Inc., Manor, TX
DS03. Results-Level Metadata: What, How, and Why
Frank Dilorio, CodeCrafters, Inc., Philadelphia PA
Jeffrey Abolafia, Rho Inc., Chapel Hill, NC
DS04. Moving up! – SDTM v3.2 – What is new and how to use it
Alyssa Wittle, Chiltern International, King of Prussia, PA
Christine McNichol, Chiltern International, King of Prussia, PA
Antonio Cardozo, Chiltern International, King of Prussia, PA
DS05. Getting Started with Data Governance
Gregory S. Nelson, ThotWave Technologies, Chapel Hill, NC
DS06. “It is a standard, so it is simple, right?”: Misconceptions and Organizational Challenges of Implementing CDISC at a CRO
Susan H.M. Boquist, PAREXEL, Billerica, MA
Adam J. Sicard, PAREXEL, Durham, NC
DS07. Considerations and Conventions within the Therapeutic Area User Guides (TAUGs)
Jerry Salyers, Accenture Accelerated R&D Services, Berwyn, PA
Kristin Kelly, Accenture Accelerated R&D Services, Berwyn, PA
Fred Wood, Accenture Accelerated R&D Services, Berwyn, PA
DS08. Deconstructing ADRS: Tumor Response Analysis Data Set
*** BEST PAPER ***
Steve Almond, Bayer Inc., Toronto, ON, Canada
DS09. Prepare for Re-entry: Challenges and Solutions for Handling Re-screened Subjects in SDTM
Charity Quick, Rho, Inc, Chapel Hill, NC
Paul Nguyen, Rho, Inc, Chapel Hill, NC
DS10. Associated Persons Domains – Who? What? Where? When? Why? How?
Alyssa Wittle, Chiltern, King of Prussia, PA
Michael Stackhouse, Chiltern, Cary, NC
DS11. SDTM Trial Summary Domain: Putting Together the TS Puzzle
Kristin Kelly, Accenture Accelerated R&D Services, Berwyn, PA
Jerry Salyers, Accenture Accelerated R&D Services, Berwyn, PA
Fred Wood, Accenture Accelerated R&D Services, Berwyn, PA
DS12. Conformance, Compliance, and Validation: An ADaM Team Lead's Perspective
John Troxell, Accenture, Berwyn, PA
DS13. Implementation of ADaM Basic Data Structure for Cross-over Studies
Songhui Zhu, A2Z Scientific Inc., East Brunswick, NJ
DS14. TRTP and TRTA in BDS Application per CDISC ADaM Standards
Maggie Ci Jiang, Teva Pharmaceuticals, West Chester, PA
DS15. Transforming Biomarker Data into an SDTM based Dataset
Kiran Cherukuri, Seattle Genetics, Inc., Bothell, WA
DS16. Codelists Here, Versions There, Controlled Terminology Everywhere
Shelley Dunn, Regulus Therapeutics, San Diego, CA
Data Visualization and Graphics
DG01. Now You Can Annotate Your GTL Graphs!Dan Heath, SAS Institute Inc., Cary, NC
DG02. Clinical Graphs Using SAS®
Sanjay Matange, SAS Institute Inc., Cary, NC
DG03. Waterfall plot: two different approaches, one beautiful graph
Ting Ma, Pharmacyclics LLC, Sunnyvale, CA
DG04. Fifty Ways to Change your Colors (in ODS Graphics)
Shane Rosanbalm, Rho, Inc., Chapel Hill, NC
DG05. Stylish Kaplan-Meier Survival Plot using SAS(R) 9.4 Graph Template Language
Setsuko Chiba, Pharmacyclics, LLC., Sunnyvale, CA
DG07. Annotating Graphs from Analytical Procedures
Warren F. Kuhfeld, SAS Institute Inc., Cary NC
DG08. Swimmer Plot by Graphic Template Language (GTL)
Baiming Wang, Pharmaceutical Products Development (PPD), Wilmington, NC
DG09. What HIGHLOW Can Do for You
Kristen Much, Rho®, Inc., Chapel Hill, NC
Kaitlyn Steinmiller, Rho®, Inc., Chapel Hill, NC
DG10. Empowering Users By Creating Data Visualization Applications In R/Shiny
Sudhir Singh, Pharmacyclics LLC, CA
Brian Munneke, Pharmacyclics LLC, CA
Amulya R Bista, Pharmacyclics LLC, CA
Jeff Cai, Pharmacyclics LLC, CA
DG11. Displaying data from NetMHCIIPan using GMAP: the SAS System as a Bioinformatics Tool
Kevin R. Viel, Ph.D., Histonis, Incorporated, Atlanta, GA
DG13. Get a Quick Start with SAS® ODS Graphics By Teaching Yourself
Roger D. Muller, Ph.D., Data To Events, Inc., Carmel, IN
DG14. A Different Approach to Create Swimmer Plot Using Proc Template and SGRENDER
Jui-Fu Huang, Baxalta, Cambridge, MA
DG15. Elevate your Graphics Game: Violin Plots
*** BEST PAPER ***
Spencer Childress, Rho, Inc., Chapel Hill, NC
Hands-On Training
HT01. Hands-On SAS® Macro Programming Essentials for New UsersKirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
HT02. PROC REPORT: Compute Block Basics
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK
HT03. New for SAS® 9.4: A Technique for Including Text and Graphics in Your Microsoft Excel Workbooks, Part 1
Vincent DelGobbo, SAS Institute Inc., Cary, NC
HT04. Usage of Pinnacle 21 Community Toolset 2.1.1 for Clinical Programmers
Sergiy Sirichenko, Pinnacle 21, Plymouth Meeting, PA
Michael DiGiantomasso, Pinnacle 21, Plymouth Meeting, PA
Travis Collopy, Pinnacle21, Plymouth Meeting, PA
HT05. Building and Using User Defined Formats
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK
HT06. Combining TLFs into a Single File Deliverable
William Coar, Axio Research, Seattle, WA
Healthcare Analytics
HA01. PrecMod: An Automated Precision SAS® Macro for Random Effects Models*** BEST PAPER ***
Jesse A. Canchola, Roche Molecular Diagnostics, Pleasanton, CA
Pari Hemyari, Roche Molecular Diagnostics, Pleasanton, CA
HA03. Working with Composite Endpoints: Constructing Analysis Data
Pushpa Saranadasa, Merck & Co., Inc., Upper Gwynedd, PA
HA04. What's the Case? Applying Different Methods of Conducting Retrospective Case/Control Experiments in Pharmacy Analytics
Aran Canes, Cigna, Bloomfield, CT
HA05. Four "Oops" Moments While Using Electronic Health Records to Identify a Cohort of Medication Users
Steve Ezzy, Optum Epidemiology, Waltham, MA
Industry Basics
IB01. What makes a “Statistical Programmer” different from a “Programmer”Arun Raj Vidhyadharan, inVentiv Health, Somerset, NJ
Sunil Mohan Jairath, inVentiv Health, Somerset, NJ
IB02. Introduction of Semantic Technology for SAS® programmers
Kevin Lee, Clindata Insight, Moraga, CA
IB04. Programming checks: Reviewing the overall quality of the deliverables without parallel programming
Shailendra Phadke, Baxalta US Inc., Cambridge, MA
Veronika Csom, Baxalta US Inc., Cambridge, MA
IB05. Compilation of Errors, Warnings and Notes!
Anusuiya Ghanghas Novartis Healthcare Private Limited, Hyderabad, India
Houde Zhang, Novartis Pharmaceuticals, East Hanover, NJ
Rajinder Kumar, Novartis Healthcare Private Limited, Hyderabad, India
IB06. Practical Implications of Sharing Data: A Primer on Data Privacy, Anonymization, and De-Identification
Gregory S. Nelson, ThotWave Technologies, Chapel Hill, NC
IB08. Good versus Better SDTM: Data Listings
Mario Widel, Eli Lilly & Co, Indianapolis, IN
Henry Winsor, Relypsa Inc., Redwood City, CA
IB10. Moving from Data Collection to Data Visualization and Analytics: Leveraging CDISC SDTM Standards to Support Data Marts
Steve Kirby, JD, MS, Chiltern, King of Prussia, PA
Terek Peterson, MBA, Chiltern, King of Prussia, PA
IB11. AE: An Essential Part of Safety Summary Table Creation
Rucha Landge, Inventiv International Pharma Services Pvt. Ltd., Pune, India
IB12. Handling Interim and Incomplete Data in a Clinical Trials Setting
*** BEST PAPER ***
Paul Stutzman, Axio Research LLC, Seattle, WA
IB14. Access OpenFDA: A Cloud Based Big Data Portal Using SAS®
Jie Roger Zhou, University of Bridgeport, Bridgeport, CT
James Sun, Insmed Inc, Bridgewater, NJ
IB15. Importance of Niche Provider for Successful NDA Submission: Rescue Case Study
Aparna Poona, Vita Data Sciences, Waltham, MA
Bhavin Busa, Vita Data Sciences, Waltham, MA
Tim Southwick, Vita Data Sciences, Waltham, MA
Management and Support
MS01. Increase Your Bottom-line and Keep More Money in Your Pocket – A Practical Guide to the Self-EmployedMargaret Hung, MLW Consulting LLC, Chapel Hill, NC
MS02. Outcome of a Clinical SAS University training program in Eastern Europe - How are graduates performing in a real work environment?
Donnelle LaDouceur, Experis Clinical, Portage, MI
Sergey Glushakov, Intego-group, LLC, Maitland, FL
MS03. Getting Clouds Moving across the Pacific - a case study on working with a Chinese CRO on SAS ® Drug Development
Chen Shi, Santen Inc., Emeryville, CA
MS04. QA and Compliance Insights Using the SCAPROC Procedure
Ben Bocchicchio, SAS Institute, Cary NC
Sandeep Juneja, SAS Institute, Cary NC
MS05. Schoveing Series 2: Self-Management: The Science of Balancing Work and Life
Priscilla Gathoni, AstraZeneca Pharmaceutical, Gaithersburg, MD
MS06. Recruiting and Retention Strategies for 2016 in the SAS Programmer Staffing Organizations
*** BEST PAPER ***
Helen Chmiel, Experis, Inc, Kalamazoo, MI
Mindy Kiss, Experis, Inc, Kalamazoo, MI
Andrea Moralez, Experis, Inc, Kalamazoo, MI
MS07. Quality, Timely and Within Budget Analysis and Reporting – Yes, you can have all three! A process and tool to help achieve this goal
Wilminda Martin, Alcon, Fort Worth, TX
Sharon Niedecken, Alcon, Fort Worth, TX
Syamala Schoemperlen, Alcon, Fort Worth, TX
MS08. Change Management: The Secret to a Successful SAS® Implementation
Gregory S. Nelson, ThotWave Technologies, Chapel Hill, NC
MS10. The CDISC’s are coming!!
Annapurna Ravi, inVentiv Health Clinical, Cincinnati, OH
Caroline Gray, inVentiv Health Clinical, Cork, Ireland
Posters
PO01. Enough of Clinical…Let’s talk Pre-Clinical!Arun Raj Vidhyadharan, inVentiv Health, Somerset, NJ
Sunil Mohan Jairath, inVentiv Health, Somerset, NJ
PO04. Data Visualization for Quality Control in NONMEM Data set
Linghui Zhang, Merck Co., Upper Gwynedd, PA
PO05. A Data Preparation Primer: Getting Your Data Ready for Submission
Janet Stuelpner, SAS Institute, New Canaan, CT
PO06. Mixed Effects Models
Yan Wang, Bristol-Myers Squibb, Wallingford, CT
PO07. Creating Time to Event ADaM Dataset for a Complex Efficacy Endpoint in Multiple Sclerosis Therapeutic Area
Ittai Rambach, Teva Pharmaceuticals LTD., Netanya, Israel
PO08. Summarizing Adverse Events of Interest – Onset, Duration, and Resolution
John Shaik, Seattle Genetics, Inc., Bothell, WA
Avani Kaja, Seattle Genetics, Inc., Bothell, WA
PO09. Don't Agonize, Organize: Maximizing efficiency and effectiveness of SAS® Programming in Clinical Trials projects, by using Project Management Organizing Methodologies
*** BEST PAPER ***
Shefalica Chand, Seattle Genetics, Inc., Bothell, WA
PO10. Tips and Tricks for Bar Charts using SAS/Graph® Template Language
Randall Nordfors, Seattle Genetics, Inc., Bothell, WA
Boxun Zhang, Seattle Genetics, Inc., Bothell, WA
PO11. Delivering a quality CDISC compliant accelerated submission using an outsourced model
Mei Dey, AstraZeneca, Gaithersburg, MD
Diane Peers, AstraZeneca, Alderley Park, Macclesfield, Cheshire, UK
PO12. CDISC Standards End-to-End: Transitional Hurdles
Alyssa Wittle, Chiltern International, King of Prussia, PA
Christine McNichol, Chiltern International, King of Prussia, PA
Antonio Cardozo, Chiltern International, King of Prussia, PA
PO13. The Hadoop Initiative: Supporting Today’s Data Access and Preparing for the Emergence of Big Data
Michael Senderak, Merck & Co., Inc., North Wales, PA
David Tabacco, Merck & Co., Inc., Branchburg, NJ
Robert Lubwama, Merck & Co., Inc., North Wales, PA
David O’Connell, Merck & Co., Inc., North Wales, PA
Matt Majer, Merck & Co., Inc., Rahway, NJ
Bryan Mallitz, Merck & Co., Inc., North Wales, PA
PO14. Making Greedy into Optimal! A Poor Woman’s Attempt to Get Optimal Propensity Score Matching from a Greedy Matching Algorithm
Janet Grubber, VA HSR&D, Durham, NC
Carl Pieper, Duke University Medical Center, Durham, NC
Cathleen Colon-Emeric, Duke University Medical Center, Durham, NC
PO15. SDTM Metadata: The Output is only as Good as the Input
Sue Sullivan, d-Wise, Morrisville, NC
PO16. "The car is in the shop but where are the mechanics?" The future of Standard Scripts for Analysis and Reporting
Dirk Spruck, Clinipace Worldwide, Marburg, Germany
Nina Worden, Santen, Emeryville, CA
PO17. Standards Implementation & Governance: Carrot or Stick?
*** BEST PAPER ***
Julie Smiley, Akana, San Antonio, TX
Judith Goud, Akana, Bennekom, Netherlands
PO18. Importing Data Specifications from .RTF and .DOC files and producing Reports
Sandesh Jagadeesh, PPD, Austin, TX
PO19. SAS Macro for Summarizing Adverse Events
Julius Kirui, Sarah Cannon Research Institute, Nashville, TN
Rakesh Mucha, Sarah Cannon Research Institute, Nashville, TN
PO20. Using GTL Generating Customized Kaplan-Meier Survival Plots
Joanne Zhou, GlaxoSmithKline, Collegeville, PA
PO21. SDTM Automation with Standard CRF Pages
Taylor Markway, SCRI Development Innovations, Carrboro, NC
PO22. Automatic Consistency Checking of Controlled Terminology among SDTM Datasets, Define.xml, and NCI/CDISC Controlled Terminology for FDA Submission
Min Chen, Alkermes Inc., Waltham, MA
Xiangchen (Bob) Cui, Alkermes Inc., Waltham, MA
PO23. Validation Methods and Strategies for Presentation of Clinical Reports: The Programmers Road Map to Success
Vijayata Sanghvi, inVentiv Health Clinical, Princeton, NJ
PO24. Building Efficiency and Quality in SDTM Development Cycle
Kishore Pothuri, Vita Data Sciences, Waltham, MA
Bhavin Busa, Vita Data Sciences, Waltham, MA
Quick Tips
QT01. Log Checks Made EasyYogesh Pande, Merck Sharp & Dohme Corp., Rahway, NJ
QT02. Plan Your Work Using PROC CALENDAR
Suresh Kumar Kothakonda, inVentiv Health Clinical, Hyderabad, India
QT03. Quick and Efficient Way to Check the Transferred Data
Divyaja Padamati, Eliassen Group Inc., NC
QT04. Keeping Up with Updating Dictionaries
Divyaja Padamati, Eliassen Group Inc., NC
QT06. Scalable Vector Graphics (SVG) using SAS
Yang Wang, Seattle Genetics, Inc., Bothell, WA
Vinodita Bongarala, Seattle Genetics, Inc., Bothell, WA
QT07. Adding Statistics and Text to the Panel Graphs using INSET option in PROC SGPANEL
Ajay Gupta, PPD, Morrisville, NC
QT08. Trivial Date Tasks? PROC FCMP Can Help
*** BEST PAPER ***
Jueru Fan, PPD, Morrisville, NC
QT09. A SAS Macro Tool to Automate Generation of Customized Patient Profile in PDF Documents
Haining Li, Massachusetts General Hospital, Boston, MA
Merit E. Cudkowicz, Massachusetts General Hospital, Boston, MA
Hong Yu, Massachusetts General Hospital, Boston, MA
QT10. Selecting Analysis Dates - A Macro Approach Using Raw Data for Expediting Results
Abdul Ghouse, Seattle Genetics, Inc., Bothell, WA
QT11. Data Validation: Bolstering Quality and Efficiency
Anusuiya Ghanghas Novartis Healthcare Private Limited, Hyderabad, India
Houde Zhang, Novartis Pharmaceuticals, East Hanover, NJ
Rajinder Kumar, Novartis Healthcare Private Limited, Hyderabad, India
QT12. Breaking up (Axes) Isn’t Hard to Do: A Macro for Choosing Axis Breaks
Alex Buck, Rho®, Chapel Hill, NC
QT13. Lab CTCAE – the Perl Way
Marina Dolgin, Teva Pharmaceutical Industries Ltd., Netanya, Israel
QT14. SAS® and R - stop choosing, start combining and get benefits!
*** BEST PAPER ***
Diana Bulaienko, Experis Clinical, Kharkiv, Ukraine
QT15. Tips on Checking and Manipulating Filenames with SAS
Solomon Lee, K Solomon LLC
QT16. When ANY Function Will Just NOT Do
Richann Watson, Experis, Batavia, OH
Karl Miller, inVentiv Health, Lincoln, NE
QT17. Becoming a more efficient programmer with SAS® Studio
Max Cherny, GlaxoSmithKline, Collegeville, PA
QT18. PROC SQL: To Create Macro Variables with Multiple Values and the Uses in Clinical Programming
Anish Kuriachen, inVentiv Health Clinical, NJ
QT19. A Macro to Automatically Flag Baseline in SDTM
Taylor Markway, SCRI Development Innovations, Carrboro, NC
QT20. SAS® Abbreviations: a Shortcut for Remembering Complicated Syntax
Yaorui Liu, Department of Preventive Medicine, University of Southern California, Los Angeles, CA
QT21. Enhancing the SAS® Enhanced Editor with Toolbar Customizations
Lynn Mullins, PPD, Cincinnati, OH
QT22. Using PROC GENMOD with count data
Meera G Kumar, Sanofi, Bridgewater, NJ
QT23. SAS Techniques for Managing Large Datasets
Rucha Landge, Inventiv International Pharma Services Pvt. Ltd., Pune, India
QT24. Remember to always check your simple SAS function code!
Yingqiu Yvette Liu, Merck & Co. Inc., North Wales, PA
Statistics and Pharmacokinetics
SP01. Cox proportional hazards regression to model the risk of outcomes per double increase in a continuous explanatory variableSeungyoung Hwang, Johns Hopkins University, Baltimore, MD
SP03. Programming Support for Exposure-Response Analysis in Oncology Drug Development
Peter Lu, Novartis Pharmaceuticals Corporation, East Hanover, NJ
Hong Yan, Novartis Pharmaceuticals Corporation, East Hanover, NJ
SP04. Scrambled Data – A Population PK/PD Programming Solution
Sharmeen Reza, Cytel Inc., Cambridge, MA
SP05. Unequalslopes: Making life easier when the proportional odds assumption fails
Matthew Wiedel, inVentiv Health, Lincoln, NE
SP06. A Dose Escalation Method for Dual-Agent in Phase 1 Cancer Clinical Trial using the SAS MCMC Procedure
Gwénaël Le Teuff, Gustave Roussy, Paris, France
Mohamed Amine Bayar, Gustave Roussy, Paris, France
SP07. Latent Structure Analysis Procedures in SAS®
*** BEST PAPER ***
Deanna Schreiber-Gregory, National University, Moorhead, MN
SP08. Everything or Nothing - A Better Confidence Intervals for Binomial Proportion in Clinical Trial Data Analysis
Sakthivel Sivam, Quartesian LLC, Princeton, NJ
Subbiah Meenakshisundaram, L.N Government College, Ponneri, India
SP09. Simulation of Data using the SAS System, Tools for Learning and Experimentation
Kevin R. Viel, Ph.D., inVentiv Health Clinical, Atlanta, GA
SP10. “I Want the Mean, But not That One!”
David Franklin, Quintiles Real Late Phase Research, Cambridge, MA
SP11. ROC Curve: Making way for correct diagnosis
Manoj Pandey, Ephicacy Lifescience Analytics Pvt. Ltd., Bangalore, India
Abhinav Jain, Ephicacy Consulting Group Inc., Iselin, NJ
Submission Standards
SS01. Creating Define-XML version 2 including Analysis Results Metadata with the SAS® Clinical Standards ToolkitLex Jansen, SAS Institute Inc., Cary, NC
SS02. Preparing Legacy Format Data for Submission to the FDA - When & Why Must I Do It, What Guidance Should I Follow?
David C. Izard, Accenture, Berwyn, PA
SS03. Strategic Considerations for CDISC Implementation
Amber Randall, Axio Research, Seattle, WA
William Coar, Axio Research, Seattle, WA
SS04. To IDB or Not to IDB: That is the Question
*** BEST PAPER ***
Kjersten Offenbecker, Spaulding Clinical Research, West Bend, WI
Beth Seremula, Chiltern International, King of Prussia, PA
SS05. A Practical Approach to Re-sizing Character Variable Lengths for FDA Submission Datasets (both SDTM and ADaM)
Xiangchen (Bob) Cui, Alkermes, Inc, Waltham, MA
Min Chen, Alkermes, Inc, Waltham, MA
SS06. New Features in Define-XML V2.0 and Its Impact on SDTM/ADaM Specifications
Hang Pang, Vertex Pharmaceuticals Incorporated, Boston, MA
SS07. Up-Versioning Existing Define.xml from 1.0 to 2.0
Jeff Xia, Merck & Co., Rahway, NJ
Lugang Xie (Larry), Merck & Co., Rahway, NJ
SS08. A SAS® Macro Tool to Automate Generation of Define.xml V2.0 from SDTM Specification for FDA Submission
Min Chen, Alkermes Inc., Waltham, MA
Xiangchen (Bob) Cui, Alkermes Inc., Waltham, MA
SS09. Achieving Clarity through Proper Study Documentation: An Introduction to the Study Data Reviewer’s Guide (SDRG)
Michael Stackhouse, Chiltern, Cary, NC
Terek J. Peterson, MBA, Chiltern, King of Prussia, PA
SS11. What is high quality study metadata?
Sergiy Sirichenko, Pinnacle 21, Plymouth Meeting, PA
Max Kanevsky, Pinnacle 21, Plymouth Meeting, PA
SS12. Submission-Ready Define.xml Files Using SAS® Clinical Data Integration
Melissa R. Martinez, SAS Institute, Cary, NC
SS13. The Standard for the Exchange of Nonclinical Data (SEND): History, Basics, and Comparisons with Clinical Data
Fred Wood, Accenture Accelerated R&D Services, Berwyn, PA
Techniques and Tutorials
TT01. Removing Duplicates Using SAS®Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
TT02. The Dynamic Duo: ODS Layout and the ODS Destination for PowerPoint
Jane Eslinger, SAS Institute Inc., Cary, NC
TT03. Controlling Colors by Name; Selecting, Ordering, and Using Colors for Your Viewing Pleasure
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK
TT05. Generalized Problem-Solving Techniques for De-bugging and Diagnosing Logic Errors
Brian Fairfield-Carter, inVentiv Health, Cary, NC
Tracy Sherman, Chiltern International, Wilmington, NC
TT06. SAS® Functions You May Have Been MISSING
Mira Shapiro, Bethesda, MD
TT07. Array of Sunshine: Casting Light on Basic Array Processing
Nancy Brucken, inVentiv Health, Ann Arbor, MI
TT08. Best Practices: Subset Without Getting Upset
Mary F. O. Rosenbloom, Lake Forest, CA
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
TT09. Formats and Informats – Concepts and Quick Reference Table
Emmy Pahmer, inVentiv Health, Montreal, Canada
TT10. May the Function Be With You: Helpful SAS® Functions, Particularly When Handling CDISC Data
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Angela Lamb, Chiltern, King of Prussia, PA
TT11. Exploring HASH Tables vs. SORT/DATA Step vs. PROC SQL
Richann Watson, Experis, Batavia, OH
Lynn Mullins, PPD, Cincinnati, OH
TT12. Let’s Make Music: Using SAS® Functions for Music Composition
Kim Truett, KCT Data, Inc., Alpharetta, GA
Zak Truett, KCT Data, Inc., Alpharetta, GA
TT14. Setting the Percentage in PROC TABULATE
David Franklin, Quintiles Real Late Phase Research, Cambridge, MA
TT16. Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS® Code
Joshua M. Horstman, Nested Loop Consulting, Indianapolis, IN
TT17. Capturing Macro Code when Debugging in the Windows Environment: The Power of MFILE and the Simplicity of Pasting
Kevin R. Viel, Ph.D., inVentiv Health Clinical, Atlanta, GA
TT18. Duplicate records - it may be a good time to contact your data management team
Sergiy Sirichenko, Pinnacle 21, Plymouth Meeting, PA
Max Kanevsky, Pinnacle 21, Plymouth Meeting, PA