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MWSUG 2014 Conference Proceedings

Chicago, Illinois
October 5-7, 2014

Advanced Analytics

AA-02. Comparing regression, propensity matching and coarsened exact matching in healthcare observational studies using SAS®: An example from the Medical Expenditure Panel Survey (MEPS)
Doug Thompson, Blue Cross Blue Shield of IL, MT, NM, OK & TX, Chicago, IL

AA-03. Should more of your PROC REGS be QUANTREGs and ROBUSTREGs?
Peter L. Flom, Peter Flom Consulting, New York, NY

AA-04. Using The Delta Method With Proc Mixed To Generate Means And Confidence Intervals From A Linear Mixed Model On The Original Scale, When The Analysis Is Done On The Log Scale
Brandy R. Sinco, MS, University of Michigan, Ann Arbor, MI
Edith Kieffer, MPH, PhD, University of Michigan, Ann Arbor, MI
Michael S. Spencer, MSW, PhD, University of Michigan, Ann Arbor, MI

AA-05. Creating Code writing algorithms for producing n-lagged variables
Matt Bates, J.P. Morgan Chase, Columbus, OH

AA-07. Creating an Easy to Use, Dynamic, Flexible Summary Table Macro with P-Values in SAS® for Research Studies
Amy Gravely, Center for Chronic Disease Outcomes Research, A VA HSR&D Center of Innovation, Minneapolis, MN
Barbara Clothier, Center for Chronic Disease Outcomes Research, A VA HSR&D Center of Innovation, Minneapolis, MN
Sean Nugent, Center for Chronic Disease Outcomes Research, A VA HSR&D Center of Innovation, Minneapolis, MN

AA-08. Kaplan-Meier Survival Plotting Macro %NEWSURV
Jeffrey Meyers, Mayo Clinic, Rochester, MN

AA-09. Selection and Transformation of Continuous Predictors for Logistic Regression
Bruce Lund, Magnify Analytic Solutions, A Division of Marketing Associates, Detroit, MI

AA-10. Tell Me What You Want: Conjoint Analysis Made Simple Using SAS®
Delali Agbenyegah, Alliance Data Systems, Columbus, OH

AA-11. Common Method Variance Techniques
Bradford R. Eichhorn, Cleveland State University, Cleveland, OH

AA-13. A SAS® Macro Using Parallel Genetic Algorithm to Automate Variable Selection
Jinqiao Li, MS., Alliance Data, Columbus, OH
Yi Cao, Ph.D., Alliance Data, Columbus, OH
Yanping Shen, MS., Alliance Data, Columbus, OH

AA-14. Understanding Change through Different Methodological Lens
Jie Liao, M.S., Alliance Data, Columbus, OH



BI / Customer Intelligence

BI-01. An Object Oriented Framework for Simulations in base SAS®
Michael C. Frick, General Motors - Retired, Warren, MI

BI-02. Efficiencies using SAS® and Netezza®
Rachel Rabaey, OLSON 1to1, Minneapolis, MN

BI-03. Understanding Double Ampersand [&&] SAS® Macro Variables
Nina L. Werner, Madison, WI

BI-04. Preparing Interaction Variables for Logistic Regression
Bruce Lund, Data Mining Consultant, Novi, MI

BI-05. Managing the Organization of SAS® Format and Macro Code Libraries in Complex Environments Including PC SAS, SAS® Enterprise Guide®, and UNIX SAS
Roger D. Muller Ph.D., Data-To-Events, Inc., Carmel, IN

BI-06. Analyzing Collection Effectiveness using Incremental Response Modeling
Ryan Burton, CAPITAL Services, Sioux Falls, SD

BI-07. Reporting The Facts: The ODSmemo macro suite for making reproducible RTF memos within SAS®
A. Rocio Lopez, Cleveland Clinic, Cleveland, OH

BI-08. Investigating the Irregular: Using Perl Regular Expressions
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada

BI-09. %LET Me Help You Improve Your Reporting
Lori Sissing, CAPITAL Services, Sioux Falls, SD

BI-10. Secret Sauce for Reporting Health Care Data Using SAS and SQL Server Business Intelligence
Bradley Blackmore, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
Gowri Madhavan, Cincinnati Children's Hospital Medical Center, Cincinnati, OH

BI-11. Use of Social Media Data to Predict Retail Sales Performance
Li Zhang, Ph.D., Alliance Data Systems, Inc., Columbus, OH

BI-12. The Joinless Join; Expand the Power of SAS® Enterprise Guide® in a New Way
Kent & Ronda Phelps, The SASketeers, Des Moines, IA
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

BI-13. SAS® Commands PIPE and CALL EXECUTE; Dynamically Advancing from Strangers to Your Newest BFF (Best Friends Forever)
Kent & Ronda Phelps, The SASketeers, Des Moines, IA
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

BI-14. SAS® In-Memory Analytics: Beyond Foundation SAS
Tho Nguyen, Teradata Corporation, Raleigh, NC
John Cunningham, Teradata Corporation, Danville, CA

BI-15. Conquering Big Data Analytics with SAS, Teradata and Hadoop
John Cunningham, Teradata Corporation, Danville, CA
Tho Nguyen, Teradata Corporation, Raleigh, NC
Paul Segal, Teradata Corporation, San Diego, CA

BI-16. SAS® In-Database Analytics for Teradata: Stop the Data Movement
Paul Segal, Teradata Corporation, San Diego, CA
Tho Nguyen, Teradata Corporation, Raleigh, NC
John Cunningham, Teradata Corporation, Danville, CA

BI-17. Parallel Data Preparation with the DS2 Programming Language
John Cunningham, Teradata Corporation, Danville, CA
Paul Segal, Teradata Corporation, San Diego, CA
Tho Nguyen, Teradata Corporation, Raleigh, NC



Beyond the Basics

BB-01. ‘V’ for … Variable Information Functions to the Rescue
Richann Watson, Experis, Batavia, OH
Karl Miller, inVentiv Health Clinical, Lincoln, NE

BB-03. Five Little Known, But Highly Valuable and Widely Usable, PROC SQL Programming Techniques
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

BB-04. List Processing Macro Call-Macro
Ronald J. Fehd, Stakana Analytics, Atlanta, GA

BB-05. File Finder: Conditionally Processing Files from a Directory Using SAS®
Katrina Drager, OptumInsight, Eden Prairie, MN

BB-06. DATA Step Merging Techniques: From Basic to Innovative
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK

BB-07. Using Arrays to Quickly Perform Fuzzy Merge Look-ups: Case Studies in Efficiency
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK

BB-08. Five Ways to Flip-Flop Your Data
Joshua M. Horstman, Nested Loop Consulting, Indianapolis, IN

BB-09. All Your Scripts are Belong to Us: Extending SAS with Server-Side Scripts
Ben Elbert, Alliance Data, Columbus, OH

BB-10. DS2: The New and Improved DATA Step in SAS®
Rajesh Lal, Experis US Inc., Portage, MI

BB-11. Documentation Driven Programming: How Orienting Your Projects Around Their Data Leads to Quicker Development and Better Results
Marcus Maher, Ipsos Public Affairs, Chicago, IL
Joe Matise, NORC at the University of Chicago, Chicago, IL

BB-12. From Wide to Tall: A Proc SQL Approach to Transposing
Brandon Crosser, University of Kansas Medical Center, Kansas City, KS

BB-13. Some useful SQL Procedures in SAS® ? Applications in complex scenarios
Soma Ghosh, UnitedHealth Group, Minneapolis, MN

BB-14. 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

BB-15. So You Want to be SAS Certified
Ben Cochran, The Bedford Group, Raleigh, NC

BB-16. Introduction to SAS® Hash Objects
Chris Schacherer, Clinical Data Management Systems, LLC

BB-17. Why Aren’t Exception Handling Routines Routine? Toward Reliably Robust Code through Increased Quality Standards in Base SAS
Troy Hughes

BB-18. Will You Smell Smoke When Your Data Are on Fire? The SAS Smoke Detector: Installing a Scalable Quality Control Dashboard for Transactional and Persistent Data
Troy Hughes

BB-19. Get Control of Your Input: Refer to Multiple Data Files Efficiently
Zhongping Zhai, Bloomington, IL



Data Visualization

DV-01. Categorical AND Continuous – The Best of Both Worlds
Kathryn Schurr, M.S., Spectrum Health-Healthier Communities, Grand Rapids, MI
Ruth Kurtycz, Spectrum Health-Healthier Communities, Grand Rapids, MI

DV-03. SAS® Graphs with Multiple Y Axes — Some Useful Tips and Tricks
Soma Ghosh, UnitedHealth Group, Minneapolis, MN

DV-04. Labelling without the Hassle: How to Produce Labeled Stacked Bar Charts Using SGPLOT and GTL Without Annotate
Joe Matise, NORC at the University of Chicago, Chicago, IL

DV-05. Extreme SAS® reporting II: Data Compendium and 5 Star Ratings Revisited
Louise Hadden, Abt Associates Inc., Cambridge, MA

DV-06. How to Create a UNIX Space Management Report Using SAS®
Thomas Lehmann, MBA, Truven Health Analytics, Ann Arbor, MI
Matthew Shevrin, MM, Truven Health Analytics, Ann Arbor, MI

DV-07. Where in the World Are SAS/GRAPH® Maps? An Exploration of the Old and New SAS® Mapping Capacities
Louise Hadden, Abt Associates Inc., Cambridge, MA

DV-08. Visualization of Self-Reported Alcohol Use Data: A Timeline Followback Dashboard using Heatmaps, Time Series Plots, and HTML Hover-Text
Daniel Fuhrmann, University of Wisconsin-Milwaukee, Milwaukee, WI
Michael Fendrich, University of Connecticut, West Hartford, CT
Lisa Berger, University of Wisconsin-Milwaukee, Milwaukee, WI

DV-09. Interactive HTML Reporting Using D3
Naushad Pasha Puliyambalath Ph.D., Nationwide Insurance, Columbus, OH

DV-10. Pin the SAS® Tail on the Excel Donkey: Automatically Sizing & Positioning SAS Graphics for Excel
Ted Conway, Chicago, IL



Hands-On Workshops

HW-01. SAS® Enterprise Guide® 5.1: A Powerful Environment for Programmers, Too! (No paper available)
Marje Fecht, Prowerk Consulting

HW-02. Creating and Using Prompts in SAS Enterprise Guide
Ben Cochran, The Bedford Group, Raleigh, NC

HW-03. Point-and-Click Programming Using SAS® Enterprise Guide®
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Mira Shapiro, Analytic Designers LLC, Bethesda, MD

HW-05. SAS®, Excel®, and JMP® Connectivity
Charles Edwin Shipp, Consider Consulting Corporation, Los Angeles, CA
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

HW-06. Programming With CLASS: Keeping Your Options Open
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK

HW-07. ODS Graphics
Chuck Kincaid, Experis Business Analytics, Kalamazoo, MI

HW-08. Understanding How the DATA Step Works
Arthur X. Li, City of Hope National Medical Center, Duarte, CA

HW-09. Basic SAS® PROCedures for Quick Results
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

HW-10. A Hands-on Introduction to SAS® Dictionary Tables
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada

HW-11. The Armchair Quarterback: Writing SAS® Code for the Perfect Pivot (Table, That Is)
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada

HW-12. Dealing with Non-Traditional Data Formats – An Update on Writing a Data Parser Using SAS® (No paper available)
Andrew T. Kuligowski, HSN



JMP

JM-01. JMP® Visual Statistics
Charles Edwin Shipp, Consider Consulting Corporation, Los Angeles, CA

JM-02. Design of Experiments (DOE) Using JMP®
Charles Edwin Shipp, Consider Consulting Corporation, Los Angeles, CA

JM-03. JMP®, and JMP® Training: a panel discussion
Charles Edwin Shipp, Consider Consulting Corporation, Los Angeles, CA

JM-04. An effective JSL architecture to develop, test, and maintain applications in JMP software (No paper available)
Peter Wiebe, Abbott Laboratories
Kevin True, Abbott Laboratories
Mark Anawis, Abbott Laboratories

JM-05. Taming the Unruly Control Chart: Control Charting Unstable Processes (No paper available)
Erich Gundlach, SAS/JMP

JM-06. Design of Experiments for Digital Marketing Using JMP® (No paper available)
Anish Koppula, Starcom MediaVest Group
Erich Gundlach

JM-07. Avoiding Long Work Weeks by Maximizing Your Productivity in JMP (No paper available)
Erich Gundlach, SAS/JMP

JM-08. JMP® and SAS®: One Completes The Other!
Philip Brown, Predictum Inc, Potomac, MD!
Wayne Levin, Predictum Inc, Toronto, ON, Canada



Pharmaceutical Applications

PH-01. Jack of all Listings, A New Approach for Review of Clinical Data
Hardik Panchal, Celgene Corporation, NJ

PH-02. A Comprehensive Automated Data Management System For Clinical Trials
Heather F. Eng, University of Pittsburgh, Pittsburgh, PA
Jason A. Lyons, University of Pittsburgh, Pittsburgh, PA
Theresa M. Sax, University of Pittsburgh, Pittsburgh, PA

PH-03. Case Study: Generating Clinical Trial Summary Plots from an ORACLE database using the SAS® Macro Language
Shannon L. Morrison, M.S., Cleveland Clinic Foundation, Cleveland, OH

PH-04. Programming in a Distributed Data Network Environment: A Perspective from the Mini-Sentinel Pilot Project
Jennifer R. Popovic, Harvard Pilgrim Health Care Institute/Harvard Medical School, Boston, MA

PH-06. Reducing the Bias: Practical Application of Propensity Score Matching in Healthcare Program Evaluation
Amber Schmitz MS, Optum, Golden Valley, MN
Jessica Navratil-Strawn MS MBA, Optum, Golden Valley, MN
Stephen Hartley BS, Optum, Golden Valley, MN
Ronald Ozminkowski PhD, Optum, Golden Valley, MN

PH-07. Examining the Factor Structure of the Revised Illness Perception Questionnaires (IPQ-R) among Underserved Type II Diabetic Patients
Xueying (Carol) Li, MS, Health Integrity LLC, Baltimore, MD
Amy McQueen, PhD, Washington University School of Medicine, St. Louis, MO
Anjali D. Deshpande, PhD, Washington University School of Medicine, St. Louis, MO

PH-08. How To Use Latent Analyses of Survey Data in a Logistic Regression Model
Deanna Schreiber-Gregory, North Dakota State University, Fargo, ND



Posters

PO-01. Design of Experiments (DOE) Using JMP®
Charles Edwin Shipp, Consider Consulting Corporation, Los Angeles, CA

PO-02. ANCOVA (Analysis of Covariance): A Visual, Intuitive Understanding
Michael Senderak, Merck & Co., Inc., Upper Gwynedd, PA

PO-03. Using the time-closest non-missing value to replace the missing value
Yubo Gao, University of Iowa Hospitals and Clinics, Iowa City, IA

PO-04. Using SAS PROC SQL to create a Build Combinations Tool to Support Modularity (No paper available)
Stephen Sloan, Accenture

PO-05. Prediction Improvement from Geostatistical Methodology in Regression Modeling For Census Tract Data
Robert G. Downer, Grand Valley State University, Allendale, MI

PO-06. Universal SAS® Macro to Parse Any Schema of Ryan White Services (RSR) XML Data
David Izrael, Abt Associates
Michael Costa, Abt Associates
Fizza S. Gillani, Brown University and Lifespan/Tufts/Brown Center for AIDS Research

PO-07. PROC ARBOR: Coding Decision Trees in SAS (No paper available)
Kevin Putschko, Grand Valley State University

PO-08. Measuring Product-level Promotional Effectiveness using Multiple Linear Regression
Aaron Clark, Meijer, Inc.
Cory Weinmann
Donald Kirk

PO-09. Show it with Shewhart: Determining if a Process is in Statistical Control (No paper available)
Carleen Dykstra, Grand Valley State University
Emma Mead, Grand Valley State University

PO-10. Modifying a structural equation model of child dietary intake using the Lagrange Multiplier test in SAS® PROC CALIS for MWSUG 2014
Lauren Cook, University of Southern California, Los Angeles, CA
Chih-Ping Chou, University of Southern California, Los Angeles, CA
Nicole Gatto, MPH, PhD, Loma Linda University, Loma Linda, CA
Jaimie N Davis, RD, PhD, University of Texas, Austin, TX
Donna Spruijt-Metz, MFA, PhD, University of Southern California, Los Angeles, CA

PO-11. The Basics of PROC FCMP (No paper available)
Dachao Liu, Northwestern University



Rapid Fire

RF-02. SAS® and Relational Databases: What You Should Know Before You Code
Patricia Hettinger, Data Analyst-Consultant, Oakbrook Terrace, IL

RF-03. WHERE, Oh, WHERE Art Thou? A Cautionary Tale for Using WHERE Statements and WHERE= Options
Britney D. Gilbert, Juniper Tree Consulting, Porter, OK

RF-04. Using Cardinality Ratio for Fast Data Review
Ronald J. Fehd, Stakana Analytics, Atlanta, GA

RF-05. File Management and Backup Considerations When Using SAS® Enterprise Guide (EG) Software
Roger Muller, Data To Events, Inc., Carmel, IN

RF-06. Captain’s LOG: Taking Command of SAS® Logarithm Functions
Britney D. Gilbert, Juniper Tree Consulting, Porter, OK
Joshua M. Horstman, Nested Loop Consulting, Indianapolis, IN

RF-07. Color Me Impressed: Creating Colorful PROC REPORT Output
Erica Goodrich, Priority Health, Grand Rapids, MI
Daniel Sturgeon, Priority Health, Grand Rapids, MI

RF-08. Burst Reporting With the Help of PROC SQL
Daniel Sturgeon, Priority Health, Grand Rapids, MI
Erica Goodrich, Priority Health, Grand Rapids, MI

RF-09. Can you decipher the code? If you can, maybe you can break it
Jay Iyengar, Independent Consultant, Evanston, IL

RF-10. Learning SAS’s Perl Regular Expression Matching the Easy Way: By Doing
Paul Genovesi, Henry Jackson Foundation for the Advancement of Military Medicine, WPAFB, OH

RF-11. A Simplified Approach to Add Shading to your Graph using GTL
Jennifer L Yen, Abbott Laboratories, Abbott Park, IL

RF-12. Using SAS Hash Objects To Cut Down Processing Time
Girish Narayandas, Optum, Eden Prairie, MN

RF-13. The Use of PROC FCMP to Manage Your Functions
Jennifer Tsai, MPH, University of Southern California, Los Angeles, CA



SAS 101

SA-01. SAS® Debugging 101
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

SA-02. SAS® 101 For Newbies--Not Too Little, Not Too Much--Just Right
Ira Shapiro, UnitedHealth Group, Minnetonka, MN

SA-03. SAS® PASSTHRU to Microsoft SQL Server using ODBC
Nina L. Werner, Madison, WI

SA-04. Quick Hits - My Favorite SAS® Tricks
Marje Fecht, Prowerk Consulting

SA-05. Navigating the Data Universe Aboard the Starship Enterprise Guide
Jay Iyengar, Independent consultant, Evanston, IL

SA-06. Working with Character Data
Andrew T. Kuligowski, HSN
Swati Agarwal, Optum

SA-07. How Do I … ? Some Beginners FAQs
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada
Audrey Yeo, Athene USA, West Des Moines, IA

SA-08. PROC SQL for DATA Step Die-hards
Christianna S. Williams, Chapel Hill, NC

SA-09. Before You Get Started: A Macro Language Preview in Three Parts
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK

SA-10. FORMATs Top Ten
Christianna Williams, PhD, Chapel Hill, NC

SA-11. A Hands On Tutorial For Automating a Tedious Process
Eric Barnitt, Minnesota Department of Health, St. Paul, MN

SA-12. Report Customization Using PROC REPORT Procedure
Shruthi Amruthnath, EPITEC, INC., Southfield, MI

SA-13. SAS ODS HTML + PROC Report = Fantastic Output
Girish K. Narayandas, OptumInsight, Eden Prairie, MN

SA-14. An Introduction to the Mighty DATASETS Procedure
Ben Cochran, The Bedford Group, Raleigh, NC

SA-15. SAS®: Data Manipulation Tools
Audrey Yeo, Athene USA, West Des Moines, IA

SA-16. Ignorance is Not Bliss: Pearls of Wisdom for the Novice SAS Programmer
Joshua M. Horstman, Nested Loop Consulting, Indianapolis, IN
Britney D. Gilbert, Juniper Tree Consulting, Porter, OK

SA-17. Top Ten SAS® Performance Tuning Techniques
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA



SAS Presents

SS-01. Helpful Hints for Transitioning to SAS 9.4
Edith Jeffreys, SAS Institute, Cary, NC

SS-02. Putting on the Ritz: New Ways to Style Your ODS Graphics to the Max
Dan Heath, SAS Institute, Cary, NC

SS-03. PDF vs HTML: Can’t We All Just Get Along?
Scott Huntley, SAS Institute, Cary, NC
Cynthia Zender, SAS Institute, Cary, NC

SS-04. Some Techniques for Integrating SAS® Output with Microsoft Excel Using Base SAS®
Vince DelGobbo, SAS Institute, Cary, NC

SS-05. Introduction to the MCMC Procedure in SAS/STAT Software (No paper available)
Maura Stokes, SAS Institute, Cary, NC

SS-06. Data Manipulation and Reports using SAS Enterprise Guide (aka: SAS Enterprise Guide for Everyone) (No paper available)
Michelle Buchecker, SAS Institute, Cary, NC

SS-07. Up Your Game with Graph Template Language Layouts
Sanjay Matange, SAS Institute, Cary, NC

SS-08. Sailing Over the ACROSS Hurdle in PROC REPORT
Cynthia Zender, SAS Institute, Cary, NC

SS-09. Text Analytics in High Performance SAS and SAS Enterprise Miner
Edward Jones, Texas A&M University