Orlando, Florida
May 17-20, 2015
Keynote Address
JumpStarting the Regulatory Review Process: The Review PerspectiveLilliam Rosario, Ph.D.
Director, Office of Computational Science
CDER, U.S. FDA
Download Slides (PDF, 2.4MB)
Applications Development
AD01. The Implementation of Display Auto-Generation with Analysis Results Metadata Driven MethodChengxin Li, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
AD02. A Way to Manage Clinical Project Metadata in SAS® Enterprise Guide
David Wang, Sanofi, Bridgewater, NJ
AD03. Proc STREAM: The Perfect Tool For Creating Patient Narratives
Joseph Hinson, inVentiv Health, Princeton, NJ, USA
AD04. Accelerate define.xml generation using defineReady
Senthilkumar Karuppiah, Navitas LLP, Chennai, India
Georgina Wood, Navitas LLP, Princeton, New Jersey, USA
AD05. Have SAS Annotate your Blank CRF for you! Plus dynamically add color and style to your annotations
*** BEST PAPER ***
Steven Black, Agility-Clinical Inc., Carlsbad, CA
AD06. A SAS® Macro Tool to Automate Generation of Define.xml V2.0 from ADaM Specification for FDA Submission
Min Chen, Alkermes Inc., Waltham, MA
Xiangchen (Bob) Cui, Alkermes Inc., Waltham, MA
AD07. SDTM Annotations: Automation by implementing a standard process
Geo Joy, Novartis, Cambridge, MA
Andre Couturier, Novartis, East Hanover, NJ
AD08. SAS® Reports on Your Fingertips? – SAS BI is the Answer for Creating Immersive Mobile Reports
Swapnil Udasi, inVentiv Health, Hyderabad, India
AD09. The Dependency Mapper: How to save time on changes post database lock
Apoorva Joshi, Biogen, Cambridge, MA
Shailendra Phadke, Eliassen Group, Wakefield, MA
AD10. Qualification Process for Standard Scripts in the Open Source Repository with Cloud Services
Hanming Tu, Accenture, Berwyn, PA, USA
Dante Di Tommaso, Roche, Basel, Switzerland
Dirk Spruck, Accovion GmbH, Germany
Christopher Hurley, MMS Holdings, Inc., Canton, MI, USA
Nancy Brucken, inVentiv Health, Ann Arbor, MI, USA
AD11. When Reliable Programs Fail: Designing for Timely, Efficient, Push-Button Recovery
Troy Martin Hughes
AD12. Toward Adoption of Agile Software Development in Clinical Trials
Troy Martin Hughes
AD13-SAS. Patient Profiles and SAS Visual Analytics
Pritesh Desai, Solutions Architect, SAS
Robert Collins, Principal Solutions Architect, SAS
AD14-SAS. Making Shared Collaborative Research Environments Usable for Researchers and Sponsors
(No paper available)
Matt Gross, SAS
Kishore Papineni, Astellas
Beyond the Basics
BB01. The Knight’s Tour in Chess – Implementing a Heuristic SolutionJohn R Gerlach, DataCeutics, Inc., Cape Coral, FL
BB02. A New Era: Open access to clinical Trial Data - A case study
Aruna Kumari Panchumarthi, Novartis Pharmaceuticals Corporation, EH, NJ, USA
Jacques Lanoue, Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
BB03. A Toolkit to Create a Dynamic Excel Format Metadata to Assist SDTM Mapping Process
Huei-Ling Chen, HLC Analytics Inc., Edison, NJ
Helen Wang, Sanofi, Bridgewater, NJ
BB04. Process and Programming Challenges in Producing Define.xml
Mike Molter, d-Wise, Morrisville, NC
BB05. A Methodology of Laboratory Data Reporting of Potentially Clinical Significant Abnormality (PCSA) for Clinical Study Report
Xiangchen (Bob) Cui, Alkermes, Inc, Waltham, MA
Min Chen, Alkermes, Inc, Waltham, MA
BB06. Implementing Union-Find Algorithm with Base SAS DATA Steps and Macro Functions
Chaoxian Cai, AFS, Exton, PA
BB07. Is Your Failed Macro Due To Misjudged “Timing”?
Arthur Li, City of Hope Comprehensive Cancer Center, Duarte, CA
BB09. Things Are Not Always What They Look Like: PROC FORMAT in Action
Peter Eberhardt, Fernwood Consulting Group Inc, Toronto, Canada
Lucheng Shao, Ivantis Inc, Irvine, CA, USA
BB11. Generic Macros for Data Mapping
Qian Zhao, J&J Consumer Companies, Inc., Morris Plains, NJ
Jun (John) Wang, J&J Consumer China Ltd, Shanghai, China
Ruofei Hao, J&J Consumer Companies, Inc., Morris Plains, NJ
BB13. A Unique Way to Annotate Case Report Forms (CRFs) in PDF, Using Forms Data Format (FDF) Techniques
Boxun Zhang, Seattle Genetics Inc., Bothell, WA
Tyler Kelly, Seattle Genetics Inc., Bothell, WA
BB14. Perl Regular Expression in SAS® Applications
Yang Wang, Seattle Genetics, Inc., Bothell, WA
Abdul Ghouse, Seattle Genetics, Inc., Bothell, WA
BB15. Creating Data-Driven SAS® Code with CALL EXECUTE
Hui Wang, Biogen, Cambridge, MA
BB16. Unpacking an Excel Cell: Dealing with Multi-Line Excel Cells in SAS
Lucheng Shao, Ivantis Inc., Irvine, CA
BB17. Fresh Cup of Joe: Utilizing Java to automate Define.XML for SDTM Origin mapping from SAS® aCRF PDFs
Tony Cardozo, Theorem Clinical Research, King of Prussia, PA
BB18. Not Just Merge - Complex Derivation Made Easy by Hash Object
*** BEST PAPER ***
Lu Zhang, PPD, Beijing, China
BB19-SAS. Clinical Trials Analysis Driven by CDISC Data Standards
(No paper available)
Kelci Miclaus, JMP
BB20. Macro Programming Best Practices: Styles, Guidelines and Conventions Including the Rationale Behind Them
Don Henderson, Henderson Consulting Services, Olney, MD
Art Carpenter, California Occidental Consultants, Anchorage, AK
Career Planning
CP01. What's Hot, What's Not: Skills for SAS® ProfessionalsKirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Charles Edwin Shipp, Consider Consulting Corporation, San Pedro, CA
CP02. ELearnSAS- A Win-Win Situation
Vikas Gaddu, Anova Groups, Raleigh, NC
Darpreet Kaur, Anova Groups, Cary, NC
CP03. Managing the Evolution of SAS® Programming
Carey Smoak, Portola Pharmaceuticals, South San Francisco, CA
CP04. CRO, TLF, SOP? OMG!: A Beginner’s Guide to the Clinical Research Organization
*** BEST PAPER ***
Mandy Bowen, INC Research, Wilmington, NC
Otis Evans, INC Research, Wilmington, NC
Stephen Terry, INC Research, Wilmington, NC
CP05. Prospecting for Great Programmers – Advice from a Hiring Manger to Make You a Solid Gold Addition to the Team
Christopher C. Hurley, MMS Holdings Inc., Canton, MI, USA
James Zuazo, MMS Holdings Inc., Canton, MI, USA
CP06. Where are the SAS® Jobs?
Beth Ward, Chiltern, Cary, NC
Chelsea Jackson, Chiltern, Cary, NC
CP07. Clinical knowledge helps SAS programmers in the pharmaceutical industry get job done better
Yingqiu Yvette Liu, Merck & Co., Inc., North Wales, PA
CP08. Downloading, Configuring, and Using the Free SAS® University Edition Software
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Charles Edwin Shipp, Consider Consulting Corporation, San Pedro, CA
CP10. A Review of "Free" Massive Open Online Content (MOOC) for SAS® Learners
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Data Standards
DS01. Understanding SE, TA, TE DomainsJacques Lanoue, Novartis Pharmaceuticals Corporation, East Hanover, NJ
DS02. SDTM TE, TA, and SE Domains: Demystifying the Development of SE
Kristin Kelly, Accenture Accelerated R&D Services, Berwyn, PA
Fred Wood, Accenture Accelerated R&D Services, Berwyn, PA
Jerry Salyers, Accenture Accelerated R&D Services, Berwyn, PA
DS03. Considerations in Conforming Data from Multiple Implantable Medical Devices to CDISC Standards Using SAS®
Julia Yang, Medtronic plc., Mounds View, MN
DS04. ADDL Model for Device Analysis
Priya Gopal, Theorem Clinical, King of Prussia, PA
DS05. LOCF vs. LOV in Endpoint DTYPE Development with CDISC ADaM Standards
Maggie Ci Jiang, Teva Pharmaceuticals, West Chester, PA
DS06. Considerations in ADaM Occurrence Data: Handling Crossover Records for Non-Typical Analysis
Karl Miller, inVentiv Health, Lincoln, NE
Richann Watson, Experis, Batavia, OH
DS07. The Best Practices of CDISC ADaM Validation Checks: Past, Present, and Future
*** BEST PAPER ***
Shelley Dunn, d-Wise, Morrisville, NC
Ed Lombardi, Agility Clinical, Carlsbad, CA
DS08. Proper Parenting: A Guide in Using ADaM Flag/Criterion Variables and When to Create a Child Dataset
Richann Watson, Experis, Batavia, OH
Karl Miller, inVentiv Health, Lincoln, NE
Paul Slagle, inVentiv Health, Ann Arbor, MI
DS09. ADaM Example for a Complex Efficacy Analysis Dataset
Milan Mangeshkar, Exelixis, Inc., South San Francisco, CA
Sandra Minjoe, Accenture Life Sciences, San Francisco, CA
DS10. PhUSE De-Identification Working Group: Providing De-Identification Standards to CDISC Data Models
Jean-Marc Ferran, Qualiance & PhUSE, Copenhagen, Denmark
Jacques Lanoue, Novartis, East Hanover, NJ
DS11. It Depends On Your Analysis Need
Sandra Minjoe, Accenture Life Sciences, San Francisco, CA
DS12. DIABETES: Submission Data Standards and Therapeutic End Points
Naveed M Khaja, SW-T Consulting Group Inc., West Chester, PA
DS13. Implementing Various Baselines for ADaM BDS datasets
Songhui Zhu, A2Z Scientific Inc., East Brunswick, NJ
DS14. What to Expect in SDTMIG v3.3
Fred Wood, Accenture Accelerated R&D Services, Berwyn, PA
DS15. Considerations in Submitting Non-Standard Variables: Supplemental Qualifiers, Findings About, or a Custom Findings Domain
Jerry Salyers, Accenture Accelerated R&D Services, Berwyn, PA
Richard Lewis, Accenture Accelerated R&D Services, Berwyn, PA
Fred Wood, Accenture Accelerated R&D Services, Berwyn, PA
DS16. What is the “ADAM OTHER” Class of Datasets, and When Should it be Used?
John Troxell, Accenture Accelerated R&D Services, Berwyn, PA
DS18. ADaM Implementation Roundtable Discussion
(No paper available)
Nancy Brucken, inVentiv Health Clinical
Richann Watson, Experis
Steve Kirby, Theorem Clinical Research
DS19-SAS. Managing Custom Data Standards in SAS® Clinical Data Integration
Melissa R. Martinez, SAS
Data Visualization and Graphics
DV01. Variable-Width Plot - SAS® GTL ImplementationSongtao Jiang, Boston Scientific Corporation, Marlborough, MA
DV02. Creating Sophisticated Graphics using Graph Template Language
*** BEST PAPER ***
Kaitlyn McConville, Rho, Inc., Chapel Hill, NC
Kristen Much, Rho, Inc., Chapel Hill, NC
DV04. An Enhanced Forest Plot Macro Using SAS®
Janette Garner, Gilead Sciences, Inc., Foster City, CA
DV05. Techniques of Preparing Datasets for Visualizing Clinical Laboratory Data
Amos Shu, MedImmune, Gaithersburg, MD
Victor Sun, MedImmune, Gaithersburg, MD
DV06. Have a Complicated Graph? Annotate Can be Great!
Scott Burroughs, GlaxoSmithKline, Research Triangle Park, NC
DV07. Getting Sankey with Bar Charts
Shane Rosanbalm, Rho, Inc., Chapel Hill, NC
DV08. Looking at the Big Picture – Snapshots of Patient Health in SAS®
Ruth Kurtycz, Spectrum Health – Healthier Communities, Grand Rapids, MI
DV09-SAS. Clinical Graphs are Easy with SAS 9.4
(No paper available)
Sanjay Matange, SAS Institute Inc., Cary, NC
DV10. Graphical Presentation of Clinical Data in Oncology Trials
Murali Kanakenahalli, Seattle Genetics, Inc., Bothell, WA
Avani Kaja, Seattle Genetics, Inc., Bothell, WA
DV11. Leveraging Visualization Techniques to tell my data story: Survival Analysis Interpretation made easy through simple programming
Vijayata Sanghvi, Consultant, Princeton, NJ
DV12. R you ready to show me Shiny for PharmaSUG 2015
Amulya R Bista, Pharmacyclics Inc, Sunnyvale, CA
Jeff Cai, Pharmacyclics Inc, Sunnyvale, CA
DV13. Forest Plots: Old Growth versus GMO (Genetically Modified Organism)
Scott Horton, Experis Clinical, Kalamazoo, MI
Hands-On Training
HT01. Picture this: Hands-on SAS Graphics SessionKriss Harris, SAS Specialists Ltd, Hertfordshire, United Kingdom
HT02. Application Development Techniques Using PROC SQL
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
HT03. Are You a Control Freak? Control Your Programs – Don’t Let Them Control You!
Mary F. O. Rosenbloom, Edwards Lifesciences LLC, Irvine, CA
Art Carpenter, California Occidental Consultants, Anchorage, AK
HT04. Usage of OpenCDISC Community Toolset 2.0 for Clinical Programmers
Sergiy Sirichenko, Pinnacle 21, Plymouth Meeting, PA
Michael DiGiantomasso, Pinnacle 21, Plymouth Meeting, PA
Travis Collopy, Pinnacle21, Plymouth Meeting, PA
HT05. DS2 with Both Hands on the Wheel
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada
Xue Yao, Winnipeg Regional Health Authority, Winnipeg, MB, Canada
HT06. Introduction to Interactive Drill Down Reports on the Web
Michael G. Sadof, MGS Associates, Inc.,Bedford NH
Louis T. Semidey, The Semidey Group, San Francisco, CA
HT07. Using INFILE and INPUT Statements to Introduce External Data into SAS®
Andrew T. Kuligowski, HSN
HT08-SAS. Creating Multi-Sheet Microsoft Excel Workbooks with SAS®: The Basics and Beyond. Part 2
Vincent DelGobbo, SAS Institute Inc., Cary, NC
Healthcare Analytics
HA01. Statistical Analyses Across Overlapping Time Intervals Based on Person-YearsJohn Reilly, Dataceutics, Inc., Pottstown, PA
John R. Gerlach, Dataceutics, Inc., Pottstown, PA
HA02. Using SAS® to Analyze the Impact of the Affordable Care Act
John J. Cohen, Advanced Data Concepts, LLC, Newark, DE
Meenal (Mona) Sinha, Premier, Inc., Downingtown, PA
HA03. Now You See It, Now You Don’t -- Using SAS to De-Identify Data to Support Clinical Trial Data Transparency
Dave Handelsman, d-Wise, Research Triangle Park, NC
HA04. Medication Adherence in Cardiovascular Disease: Generalized Estimating Equations in SAS®
Erica Goodrich, Priority Health, Grand Rapids, MI
Daniel Sturgeon, Priority Health, Grand Rapids, MI
HA05. A General SAS® Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius
*** BEST PAPER ***
Kathy H. Fraeman, Evidera, Bethesda, MD
HA06. The Path To Treatment Pathways
Tracee Vinson-Sorrentino, IMS Health, Plymouth Meeting, PA
HA07. Distributed data networks: A paradigm shift in data sharing and healthcare analytics
Jennifer R. Popovic, Harvard Pilgrim Health Care Institute/Harvard Medical School, Boston, MA
HA09. Simple Tests of Hypotheses for the Non-statistician: What They Are and Why They Can Go Bad
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK
Industry Basics
IB01. The 5 Most Important Clinical SAS? Programming Validation StepsBrian C. Shilling, inVentiv Health Clinical, Cape Coral, FL
IB02. Use of SAS Reports for External Vendor Data Reconciliation
Soujanya Konda, inVentiv Health Clinical, Hyderabad, India
IB03. Tackling Clinical Lab Data in Medical Device Environment
Juan Wu, Medtronic plc., Santa Rosa, CA
Min Lai, Medtronic plc., Santa Rosa, CA
IB04. SAS Grid: Simplified
Rajinder Kumar, inVentiv International Pharma Services Pvt. Ltd., Pune, Maharashtra (India)
IB06. Two different use cases to obtain best responses using RECIST 1.1: SDTM and ADaM
Kevin Lee, Accenture Life Sciences, Berwyn, PA
Vikash Jain, Inventiv Health Clinical, Princeton, NJ
IB07. The Disposition Table: Make it Easy
Endri Endri, ProXpress Clinical Research GmbH, Berlin, Germany
Benedikt Trenggono, ProXpress Clinical Research GmbH, Berlin, Germany
IB08. Improving Data Quality - Missing Data Can Be Your Friend!
Julie Chen, United BioSource Corporation, Blue Bell, PA
IB09. Tips on Creating a Strategy for a CDISC Submission
Rajkumar Sharma, Nektar Therapeutics, San Francisco, CA
IB11. Proc Compare: Wonderful Procedure!
*** BEST PAPER ***
Anusuiya Ghanghas, inVentiv International Pharma Services Pvt Ltd, Pune, India
Rajinder Kumar, inVentiv International Pharma Services Pvt Ltd, Pune, India
Management and Support
MS01. It’s Not That You Know It, It’s How You Show ItJim Christine, DataCeutics, Inc., Pottstown, PA
MS02. Are You An Indispensable SAS Programmer?
R. Mouly Satyavarapu, inVentiv Health, Cary, NC
MS03. How to Harness Your Company's Wiki for World Domination (or — Even Better — to Write the Best Reusable Code Possible)
Kurtis Cowman, PRA Health Sciences, Lenexa, KS
MS04. No Regrets: Hiring for the Long Term in Statistical Programming
Chris Moriak, AstraZeneca, Gaithersburg, MD, USA
Graham Wilson, AstraZeneca, Alderly Park, UK
Elizabeth Meeson, AstraZeneca, Alderly Park, UK
MS05. Development of a Clinical SAS University Training Program in Eastern Europe
*** BEST PAPER ***
Erfan Pirbhai, Experis, Kalamazoo, MI
Sergey Glushakov, Intego Group, Maitland, FL
MS06. Advantages and Disadvantages of Two Commonly Used CRO Resourcing Models in the Pharmaceutical SAS Programming Environment
Ying (Evelyn) Guo, Amgen, Thousand Oaks, CA
Mark Matthews, GCE Solutions, Indianapolis, IN
MS07. How to Build and Manage a Successful and Effective FSP Service for US sponsors out of India
Debiprasad Roy, Medivation, Inc., San Francisco, CA
Ganesh Gopal, Ephicacy Consulting Group, Inc., Iselin, NJ
MS08. How to Build an “Offshore” Team with “Onshore” Quality
Lulu Swei, PRA Health Sciences, Blue Bell, PA
MS09. Managing a Remote Workforce: Making Sense Out of Today's Business Environment
Dave Polus, Experis ManpowerGroup, Portage, MI
MS10. An Analysis of Clinical Programmer and Other Technical Job Descriptions: Lessons Learned for Improved Employment Postings
(No paper available)
Troy Hughes
MS11. Schoveing Series 1: Motivating and Inspiring Statistical Programmers in the Biopharmaceutical Industry
Priscilla Gathoni, Morristown, NJ, USA
MS12. Firefighters - clinical trial analysis teams
Iryna Kotenko, Experis Clinical, Kharkiv, Ukraine
MS14. Steps Required to Achieve Operational Excellence (OpEx) Within Clinical Programming
Opeyemi Ajoje, Accenture Accelerated R & D Services, Berwyn, PA
MS16-SAS. View Into SAS® Modern Architectures - Adding 'Server' SAS to Your Artifacts
(No paper available)
Matt Becker, SAS
MS17. Panel Discussion: Managing in a Virtual Workplace
(No paper available)
Matt Becker, SAS
Kent Letourneau, PRA
MS18. PhUSE CSS Round Table Discussion - Statistical Computing Environment
(No paper available)
Mark Matthews, GCE Solutions
Wayne Woo, Novartis Vaccines
Andre Couturier, Novartis
Gina Wood, Novartis
Posters
PO01. TEAE: Did I flag it right?Arun Raj Vidhyadharan, inVentiv Health, Somerset, NJ
Sunil Mohan Jairath, inVentiv Health, Somerset, NJ
PO02. Creating a Break in the Axis
Amos Shu, MedImmune, Gaithersburg, MD
PO03. A Visual Reflection on SAS/GRAPH® History: Plot, Gplot, Greplay, and Sgrender
Haibin Shu, AccuClin Global Services LLC, Wayne, PA
John He, AccuClin Global Services LLC, Wayne, PA
PO04. Automation of paper dossier production for Independent Review Charter
Ting Ma, Pharmacyclics, Sunnyvale, CA
Jeff Cai, Pharmacyclics, Sunnyvale, CA
Michelle Zhang, Pharmacyclics, Sunnyvale, CA
Lina Gau, Pharmacyclics, Sunnyvale, CA
PO05. A Web-Based Approach to Fighting Analysis Chaos
Gordon Fancher, Seattle Genetics Inc., Bothell, WA
Rajeev Karanam, Seattle Genetics Inc., Bothell, WA
Shawn Hopkins, Seattle Genetics Inc., Bothell, WA
PO06. Programming Pharmacokinetic (PK) Timing and Dosing Variables in Oncology Studies: Demystified
Kiran Cherukuri, Seattle Genetics Inc., Bothell, WA
PO07. Create bookmarked PDFs using ODS
Aruna Kumari Panchumarthi, Novartis Pharmaceuticals Corporation, EH, NJ, USA
Jacques Lanoue, Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
PO08. Exchange of data over internet using web services (e.g., SOAP and REST) in SAS environment
Kevin Lee, Accenture Accelerated Research & Development Services, Berwyn, PA
PO09. Adding Subversion® Operations to the SAS® Enhanced Editor
Oscar F. Cheung, PPD, Morrisville, NC
Kenneth W. Borowiak, PPD, Morrisville, NC
PO10. Summing up - SDTM Trial Summary Domain
Yi Liu Celerion Inc., Lincoln, NE
Jenny Erskine Celerion Inc., Belfast, Northern Ireland
Stephen Read Celerion Inc., Belfast, Northern Ireland
PO11. Efficiently Produce Descriptive Statistic Summary Tables with SAS Macros
Chunmao Wang, Quintiles, Rockville, MD
PO12. Utilizing SAS® for Cross-Report Verification in a Clinical Trials Setting
Daniel Szydlo, Fred Hutchinson Cancer Research Center, Seattle, WA
Iraj Mohebalian, Fred Hutchinson Cancer Research Center, Seattle, WA
Marla Husnik, Fred Hutchinson Cancer Research Center, Seattle, WA
PO13. Leveraging ADaM Principles to Make Analysis Database and Table Programming More Efficient
Andrew L Hulme, PPD, Kansas City, MO
PO14. Subset without Upsets: Concepts and Techniques to Subset SDTM Data
Jhelum Naik, PPD, Wilmington, NC
Sajeet Pavate, PPD, Wilmington, NC
PO15. Update: Development of White Papers and Standard Scripts for Analysis and Programming
Nancy Brucken, inVentiv Health, Ann Arbor, MI
Michael Carniello, Astellas Pharma US, Northbrook, IL
Mary Nilsson, Eli Lilly and Company, Indianapolis, IN
Hanming Tu, Accenture, Wayne, PA
PO16. Automating biomarker research visualization process
Xiaohui Huang, Gilead Sciences Inc., Foster City, CA
Jigar Patel, Gilead Sciences Inc., Foster City, CA
PO17. Spelling Checker Utility in SAS® using VBA Macro and SAS® Functions
Ajay Gupta, PPD, Morrisville, NC
PO19. Macro to Read Mock Shells in Excel and Create a Copy in Rich Text Format
*** BEST PAPER ***
Rakesh Mucha, Sarah Cannon Research Institute, Nashville, TN
Jeffrey Johnson, Sarah Cannon Research Institute, Nashville, TN
PO21. Evaluating SDTM SUPP Domain For AdaM - Trash Can Or Buried Treasure
Xiaopeng Li, Celerion, Lincoln, NE
Yi Liu, Celerion, Lincoln, NE
Chun Feng, Celerion, Lincoln, NE
PO22. Challenges in Developing ADSL with Baseline Data
Hongyu Liu, Vertex Pharmaceuticals Incorporated, Boston, MA
Hang Pang, Vertex Pharmaceuticals Incorporated, Boston, MA
PO23. Analysis Methods for a Sequential Parallel Comparison Design (SPCD)
Harry Haber, MMS Holdings, Inc., Canton, MI
James Zuazo, MMS Holdings, Inc., Canton, MI
Linda LaMoreaux, MMS Holdings, Inc., Canton, MI
PO24. Introducing a Similarity Statistic to Compare Data Libraries for Improving Program Efficiency for Similar Clinical Trials
Taylor Markway, PRA Health Sciences, Raleigh, NC
Amanda Johnson, PRA Health Sciences, Raleigh, NC
PO25. Enhancing Infrastructure for Growth
Amber Randall, Axio Research, Seattle, WA
William Coar, Axio Research, Seattle, WA
Quick Tips
QT02. ISPLIT Macro: to split large SAS datasetsHany Aboutaleb, Biogen, Cambridge, MA
QT04. Let SAS Generate XML Code for ACCESS Audit Trail Data Macro
Sijian Zhang, VA Medical Center, Washington, DC
QT05. EXTENDED ATTRIBUTES: A New Metadata Creation Feature in SAS® 9.4 for Data Sets and Variables
Joseph Hinson, inVentiv Health, Princeton, NJ, USA
QT06. PROC SQL for SQL Die-hards
Jessica Bennett, Advance America, Spartanburg, SC
Barbara Ross, Flexshopper LLC, Boca Raton, FL
QT07. Creating the Perfect Table Using ODS to PDF in SAS 9.4®
Elizabeth Dennis, EMB Statistical Solutions, Overland Park, KS
Maddy Dennis, Independent Contractor, Lawrence, KS
QT08. Hands Free: Automating Variable Name Re-Naming Prior to Export
John Cohen, Advanced Data Concepts LLC, Newark, DE
QT09. Using Meta-data to Identify Unused Variables on Input Data Sets
Keith Hibbetts, Inventiv Health Clinical, Indianapolis, IN
QT10. A Simple Macro to Select Various Variables Lists
Ting Sa, Cincinnati Children’s Hospital, Cincinnati, OH
Yanhong Liu, Cincinnati Children’s Hospital, Cincinnati, OH
QT11. Don’t Get Blindsided by PROC COMPARE
Joshua Horstman, Nested Loop Consulting, Indianapolis, IN
Roger Muller, Data-to-Events.com, Carmel, IN
QT12. Let SAS “Modify” Your Excel File
Nelson Lee, Genentech, South San Francisco, CA
QT13. Is Your SAS® System Reliable?
Wayne Zhong, Accretion Softworks
QT14. Getting the Most Out of PROC SORT: A Review of Its Advanced Options
Max Cherny, GlaxoSmithKline, King of Prussia, PA
QT15. A Macro to Easily Generate a Calendar Report
Ting Sa, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
QT19. Using the power of new SGPLOT features in SAS 9.4 – Customized graphic programming made easier for clinical efficacy and exposure-response analyses
Peter Lu, Novartis Pharmaceuticals Corporation, East Hanover, NJ
Xingji Han, Novartis Pharmaceuticals Corporation, East Hanover, NJ
Hong Yan, Novartis Pharmaceuticals Corporation, East Hanover, NJ
QT20. Simplicity is the Soul of Efficiency: Simple Tips and Tricks for Efficient SAS® Programming
Shefalica Chand, Seattle Genetics, Inc., Bothell, WA
QT21. Sorting big datasets. Do we really need it?
Daniil Shliakhov, Experis Clinical, Kharkiv, Ukraine
QT22. Creating output datasets using SQL (Structured Query Language) only
Andrii Stakhniv, Experis Clinical, Ukraine
QT23. A Macro to Produce a SAS® Data Set Containing the List of File Names Found in the Requested Windows or UNIX Directory
Mike Goulding, Experis, Portage, MI
QT24. Reproducibly Random Values
William Garner, Gilead Sciences, Inc., Foster City, CA
Ting Bai, Gilead Sciences, Inc., Foster City, CA
QT25. Transitions in Depressive Symptoms After 10 Years of Follow-up Using PROC LTA
Seungyoung Hwang, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
QT26. Keyboard Macros - The most magical tool you may have never heard of - You will never program the same again (It's that amazing!)
*** BEST PAPER ***
Steven Black, Agility-Clinical Inc., Carlsbad, CA
QT27. The concept of “Dynamic” SAS programming
Sergey Sian, Quintiles, Cambridge, MA
QT28. Which TLFs Have Changed Since Your Previous Delivery? Get a Quick YES or NO for Each TLF
Tom Santopoli, Accenture, Berwyn, PA
QT29. Sensitivity Training for PRXers
Kenneth W. Borowiak, PPD, Morrisville, NC
QT30. FILLPATTERNS in SGPLOT Graphs
Pankhil Shah, PPD, Morrisville, NC
QT31. Copying Files Programmatically in SAS® Drug Development (SDD)
Wayne Woo, Novartis Vaccines, Cambridge, MA
QT32. Accelerating Production of Safety TFLs in Bioequivalence and Early Phase
Denis Martineau, Algorithme Pharma, Laval, Quebec, Canada
QT33. Simulate PRELOADFMT Option in PROC FREQ
Ajay Gupta, PPD, Morrisville, NC
QT34. Grooming of Coarse Data by Smart Listing Using SAS System
Soujanya Konda, inVentiv Health Clinical, Hyderabad, India
QT36. Mlibcompare – keep an eye on the changes of your data
Wen Shi, Accenture, Berwyn, PA
QT37. I/O, I/O, It’s Off To Work We Go: Digging Into the ATTRC Function
Karleen Beaver, PPD, Morrisville, NC
QT38. Statistical Review and Validation of Patient Narratives
Indrani Sarkar, inVentiv Health Clinical, Indianapolis, IN
Bradford J. Danner, Sarah Cannon Research Institute, Nashville, TN
QT40. Regaining Some Control Over ODS RTF Pagination When Using Proc Report
Gary E. Moore, Moore Computing Services, Inc., Little Rock, AR
QT41. Automated Checking Of Multiple Files
Kathyayini Tappeta, Percept Pharma Services, Bridgewater, NJ
QT42. Automating Clinical Trial Reports with ODS ExcelXP Tagset
*** BEST PAPER ***
John O’Leary, Department of Veterans Affairs, West Haven, CT
QT44. Get a Data Dictionary using PROC contents: Easily
Beatriz Garcia, Inventiv Health Clinical Mexico, Mexico City, Mexico
Alberto Hernandez, Inventiv Health Clinical Mexico, Mexico City, Mexico
QT45. A Practical Approach to Create Adverse Event Summary by Toxicity Grade Table
Zhengxin (Cindy) Yang, inVentiv Health, Princeton, NJ
QT47. Customizing the Graph Templates for a Kaplan-Meier Failure Plot
Hugh Geary, Novella Clinical, a Quintiles company, Columbus, OH
Statistics and Pharmacokinetics
SP01. Multilevel RandomizationMarina Komaroff, Noven Pharmaceuticals, New York, NY
Lois Lynn, Noven Pharmaceuticals, New York, NY
SP02. MMRM: Macro for Selecting Best Covariance Structure with the Method of Interest
Linga Reddy Baddam, inVentiv Health Clinical, Hyderabad, India
Sudarshan Reddy Shabadu, inVentiv Health Clinical, Hyderabad, India
Chunxue Shi, inVentiv Health Clinical, Maryland, USA
SP03. Missing Data For Repeated Measures: Single Imputation VS Multiple Imputation
Giulia Tonini, PhD Menarini Ricerche, Florence, Italy
Simona Scartoni, Menarini Ricerche, Florence, Italy
Camilla Paoli, Menarini Ricerche, Florence, Italy
Andrea Nizzardo, Menarini Ricerche, Florence, Italy
Angela Capriati, MD, PhD, Menarini Ricerche, Florence, Italy
SP04. Means Comparisons and No Hard Coding of Your Coefficient Vector – It Really Is Possible!
Frank Tedesco, United Biosource Corporation, Blue Bell, PA
SP05. Using ANCOVA to Assess Regression to the Mean
*** BEST PAPER ***
Kathryn Schurr, M.S., Spectrum Health – Healthier Communities, Grand Rapids, MI
SP06. Confidence Intervals Are a Programmer’s Friend
Xinxin Guo, Quintiles, Cambridge, MA
Zhaohui Su, Quintiles, Cambridge, MA
SP07. Growing Needs in Drug Industry for NONMEM Programmers Using SAS®
Sharmeen Reza, Cytel Inc., Cambridge, MA
SP08. How To Use Latent Analyses Within Survey Data Can Be Valuable Additions to Any Regression Model
Deanna Schreiber-Gregory, National University, La Jolla, CA
SP09-SAS. Current Methods in Survival Analysis Using SAS/STAT® Software
(No paper available)
Changbin Guo, SAS
SP10. %PIC_NPMLE: A SAS Macro For Nonparametric Estimation In Partly Interval-Censored Survival Data
Liang Zhu, St. Jude Children’s Research Hospital, Memphis, TN
Yimei Li, St. Jude Children’s Research Hospital, Memphis, TN
Qinlei Huang, St. Jude Children’s Research Hospital, Memphis, TN
Submission Standards
SS01. Getting Loopy with SAS® DICTIONARY Tables: Using Metadata from DICTIONARY Tables to Fulfill Submission RequirementsJeanina Worden, Santen Inc., Emeryville, CA
SS02. Japanese submission/approval processes from programming perspective
*** BEST PAPER ***
Ryan Hara, Novartis Pharma AG, Basel, Switzerland
SS04. Begin with the End in Mind – Using FDA Guidance Documents as Guideposts when Planning, Delivering and Archiving Clinical Trials
David C. Izard, Accenture, Berwyn, PA
SS05. OSI Packages: What you need to know for your next NDA or BLA Submission
Thaddea Dreyer, AstraZeneca, Gaithersburg, MD
Tatiana Scetinina, AstraZeneca, Gaithersburg, MD
SS06. The Most Common Issues in Submission Data
Sergiy Sirichenko, Pinnacle 21, Plymouth Meeting, PA
Max Kanevsky, Pinnacle 21, Plymouth Meeting, PA
SS08-SAS. Getting Rid of Bloated Data in FDA Submissions
Ben Bocchicchio, SAS Institute, Cary, NC
Frank Roediger, SAS Institute, Cary, NC
SS09-SAS. SAS® Tools for Working with Dataset-XML files
Lex Jansen, SAS Institute Inc., Cary, NC
SS10-SAS. Using SAS® Clinical Data Integration to Roundtrip a Complete Study Study Metadata (Define-XML) and Study Data (Dataset-XML)
Ken Ellis, SAS Institute Inc., Cary, NC
Techniques and Tutorials
TT01. Team-work and Forensic Programming: Essential Foundations of Indestructible Projects*** BEST PAPER ***
Brian Fairfield-Carter, inVentiv Health, Cary, NC
TT02. Phantom of the ODS – How to run cascading compute blocks off of common variables in the data set for complex tasks.
Robin M. Sandlin, Cook Systems International, Inc., Memphis, TN
TT03. PROC SQL: Make it a monster using these powerful functions and options
Arun Raj Vidhyadharan, inVentiv Health, Somerset, NJ
Sunil Mohan Jairath, inVentiv Health, Somerset, NJ
TT04. SAS® Programming Tips, Tricks and Techniques for Programmers
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
TT05. DATA Step Merging Techniques: From Basic to Innovative
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK
TT06. Using Arrays to Quickly Perform Fuzzy Merge Look-ups: Case Studies in Efficiency
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK
TT07. Inside the DATA Step: Pearls of Wisdom for the Novice SAS Programmer
Joshua M. Horstman, Nested Loop Consulting, Indianapolis, IN
Britney D. Gilbert, Juniper Tree Consulting, Porter, OK
TT08. A Collection of Items from a Programmers’ Notebook
David Franklin, Real-World & Late Phase Research, Quintiles, Cambridge, MA
Cecilia Mauldin, Independent Consultant, Chapel Hill, NC
TT09. Defensive Coding by Example: Kick the Tires, Pump the Breaks, Check Your Blind Spots, and Merge Ahead!
Nancy Brucken, inVentiv Health, Ann Arbor, MI
Donna E. Levy, inVentiv Health, Cary, NC
TT10. Essential Guide to Good Programming Practice
Shafi Chowdhury, Shafi Consultancy Limited, London, United Kingdom
Mark Foxwell, PRA Health Sciences, Reading, United Kingdom
Cindy Song, Sanofi-Aventis, Bridgewater, NJ
TT11. Lessons Learned from the QC Process in Outsourcing Model
Faye Yeh, Takeda Development Center Americas, Inc., Deerfield, IL
TT12. PROC TRANSPOSE® For Fun And Profit
John J. Cohen, Advanced Data Concepts, LLC, Newark, DE
TT13. Looking Beneath the Surface of Sorting
Andrew T. Kuligowski, HSN
TT14-SAS. Reusability, Macros, and Automation in SAS Clinical Data Integration
(No paper available)
Melissa R. Martinez, SAS
TT15-SAS. The REPORT Procedure: A Primer for the Compute Block
Jane Eslinger, SAS Institute Inc., Cary, NC
TT16. PROC PRINT the Granddaddy of all Procedures, Enhanced and Still Going Strong!
David Franklin, TheProgrammersCabin.com, Litchfield, NH