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MWSUG 2012 Conference Proceedings

Minneapolis, MN
September 16-18, 2012

Download the entire 2012 conference proceedings as a ZIP file (63MB)

Individual Paper PDFs:

BI Applications/Architecture

BI01. Getting to Know an Undocumented SAS® Environment
Brian Varney, Experis Business Analytics Practice, Portage, Michigan

BI05. Developing an Analytics Center of Excellence (Or The Care and Feeding of Magical Creatures)
Charles D. Kincaid, Experis Business Analytics Practice, Portage, MI

BI07. Project Automation and Tracking Using SAS
Rajesh Lal, Experis, Portage, MI

BI08. SAS Enterprise Guide® - Why and How the Programmer Should Adapt It Now
Roger D. Muller, Ph.D., First Phase Consulting, Carmel, IN
Donald L Penix (DJ), Jr., Pinnacle Solutions, Indianapolis, IN

BI09. Using Enterprise Guide® Effectively
Tom Miron, Systems Seminar Consultants, Madison, WI

BI10. SAS Enterprise Guide® – Moving Beyond Your Initial Startup
Donald L Penix Jr. (D.J.) Pinnacle Solutions, Inc., Indianapolis, IN
Roger D. Muller, Ph.D., First Phase Consulting, Carmel, IN

BI11. Top Ten SAS® Performance Tuning Techniques
*** BEST PAPER ***
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

BI12. Preparing Your Computer for SAS® Grid
Margaret Crevar, SAS Institute Inc., Cary, NC, USA
Tony Brown, SAS Institute Inc, Dallas, TX USA

BI13. How Readable and Comprehensible Is a SAS® Program? A Programmatic Approach to Getting an Insight into a Program
Rajesh Lal, Experis, Portage, MI, USA
Raghavender Ranga, Vertex, Cambridge, MA, USA

BI14. Human side of BI implementation
Arlen Harmoning, Minnesota Management & Budget, St. Paul, MN

BI15. What's Hot, What's Not: Skills for SAS® Professionals
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Charles Edwin Shipp, Consider Consulting, Inc., San Pedro, CA

BI17. Using SAS® to Audit Programming Code Versioning
Rex Pruitt, PREMIER Bankcard, LLC, Sioux Falls, SD

BI18. Autocall Macro Libraries
Ken Schmidt, Truven Health Analytics, Ann Arbor, MI

BI19. So You Want to Install SAS®?
Rafi Sheikh, Analytiks International Inc., Minneapolis, MN

BI21. SAS Grid: Grid Enablement of SAS
Adam H. Diaz, IBM Platform Computing, Research Triangle Park, NC

BI22. SAS® Grid: Grid Scheduling Policy and Resource Allocation
Adam H. Diaz, IBM Platform Computing, Research Triangle Park, NC

BI23. Creating and Using Prompts in Enterprise Guide (No paper available)
Ben Cochran, The Bedford Group, Raleigh, NC



Coders Corner

CC01. Simple Rules to Remember When Working with Indexes
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

CC02. Building Macros and Tracking Their Use
*** BEST PAPER ***
Richard Koopmann, Jr., Capella University, Minneapolis, MN

CC03. Search Engine Using SAS
Pramod.R, Target Corporation, Minneapolis, MN

CC04. Converting a Binary String to a Decimal Numeric – An Exercise in Problem Solving (No paper available)
Andrew Kuligowski



Customer Intelligence

CI01. Top Ten SAS® Sites for Programmers: A Review
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Charles Edwin Shipp, JMP 2 Consulting, Inc., San Pedro, CA
Becky Harmon, Wells Fargo Community Banking, San Francisco, CA

CI02. Customer Sentiment Imputation Using Hash Tables
David J. Corliss, Magnify Analytics, Detroit, MI

CI03. Survival of the Milkman – How Modern Analytics is Helping an Old Fashioned Business Thrive
Bruce Bedford, Ph.D., Oberweis Dairy, North Aurora, IL

CI04. Direct Marketing Profit Model
*** BEST PAPER ***
Bruce Lund, Marketing Associates, Detroit, MI

CI05. Innovative Techniques: Doing More with Loops and Arrays
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK

CI06. Programming With CLASS: Keeping Your Options Open
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK

CI07. Applying Analytics to High Performance Customer Profitability Models in Financial Services
Tony Adkins, SAS Institute Inc., Cary, NC
Gary Cokins, SAS Institute Inc., Cary, NC

CI08. A Brief Survey of Clustering and Segmentation for Customer Intelligence
George J. Hurley, The Hershey Company, Hershey, PA

CI09. Using Base SAS® and SAS® Enterprise Miner™ to Develop Customer Retention Modeling
Rex Pruitt, PREMIER Bankcard, LLC, Sioux Falls, SD



Data Management / Data Mining

DM01. New Vs. Old – Under the Hood with Procs CONTENTS and COMPARE
Patricia Hettinger, SAS Professional, Oakbrook Terrace, IL

DM02. The SAS® Programmer's Guide to XML and Web Services
Christopher W. Schacherer, Clinical Data Management Systems, LLC, Madison, WI

DM06. SAS® Data Management Techniques: Cleaning and transforming data for delivery of analytic datasets
Christopher W. Schacherer, Clinical Data Management Systems, LLC, Madison, WI

DM07. Swimming with Sharks: Using Formats with Summary Data
Tom Bugg, Wells Fargo Home Mortgage, Des Moines, IA

DM09. A Basic Recipe for Building a Campaign Management System from Scratch: How Base SAS®, SQL Server and Access can Blend Together
Tera Olson, Aimia Proprietary Loyalty U.S. Inc., Minneapolis, MN

DM10. A CareerView Mirror: Another Perspective on Your Work and Career Planning
*** BEST PAPER ***
Bill Donovan, OckhamSource™, Cary, NC

DM11. Why and How To Use SAS® Macro Language: Easy Ways To Get More Value and Power from Your SAS® Software Tools
LeRoy Bessler PhD, Bessler Consulting and Research



Data Visualization and Graphics

DV01. FAQ: PROC TEMPLATE
Katherine Burgess, Sanford Research, Sioux Falls, SD
Ashley Miller, Sanford Research, Sioux Falls, SD

DV02. Performing response surface analysis using the SAS RSREG procedure
Zhiwu Li, National Database Nursing Quality Indicator and the Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS
Ying Liu, School of dentistry, University of Missouri at Kansas City, Kansas City, MO

DV03. Type, Point and Click – A Practical Guide to SAS Enterprise Guide® Prompts
Patricia Hettinger, SAS Professional, Oakbrook Terrace, IL

DV05. Flexible HTML Reporting Using Advanced Macro Programming and TableEditor Tagset
Smita Sumant, Wyndham Exchange & Rentals, Parsippany, NJ

DV06. Are You Dense? Using Kernel Density Estimation (KDE) to Connect the Dots Amidst Uncertainty
Stephen Crosbie, Magnify, a Division of Marketing Associates, LLC, Detroit, MI
David Corliss, Magnify, a Division of Marketing Associates, LLC, Detroit, MI

DV07. ODS ExcelXP: Customizing Cell Pattern, Borders and Cell Indenting
Deepak Asrani, Medtronic Inc., Mounds View, MN, USA

DV08. Van Gogh Your Data: Data Visualization Methods with SAS® Business Intelligence
Natalie Parsons, SAS Institute Inc., Cary, NC
Scott McQuiggan, SAS Institute Inc., Cary, NC
(Presented by Sanjay Matange, SAS Institute Inc., Cary, NC)

DV09. Introduction to the Graph Template Language
Sanjay Matange, SAS Institute, Cary, NC

DV10. Quick Results with SAS ODS Graphics Designer (No paper available)
Sanjay Matange, SAS Institute, Cary, NC

DV11. Using SAS® ODS Graphics
Chuck Kincaid, Experis, Portage, MI

DV12. Use of SAS® to determine broiler chick feed color preference in correlation with performance under different housing light colors.
Nichole Graham, Kansas State University, Manhattan, KS

DV13. Get the Best Out Of SAS® ODS Graphics and the SG (Statistical Graphics) Procedures: Communication-Effective Charts, Things That SAS/GRAPH® Cannot Do As Well, and Macro Tools To Save Time and Avoid Errors
*** BEST PAPER ***
LeRoy Bessler PhD, Bessler Consulting and Research



Hands-On Workshops

HW01. Doing More with the Display Manager: From Editor to ViewTable – Options and Tools You Should Know
*** BEST PAPER ***
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK

HW02. Taking Full Advantage of sasCommunity.org: Your SAS® Site
sasCommunity Advisory Board
(Presented by Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK)
(Presented by Don Henderson, Henderson Consulting Services LLC, Olney, MD)

HW03. The Armchair Quarterback: Writing SAS® Code for the Perfect Pivot (Table, That Is)
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada

HW04. Parsing Useful Data Out of Unusual Formats Using SAS®
Andrew T. Kuligowski

HW05. Using INFILE and INPUT Statements to Introduce External Data into SAS®
Andrew T. Kuligowski

HW06. Base-SAS® PROCedures for Quick Results
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

HW07. Essential SAS® Coding Techniques for Gaining Efficiency
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

HW08. Effectively Utilizing Loops and Arrays in the DATA Step
Arthur X. Li, City of Hope Comprehensive Cancer Center, Duarte, CA



Healthcare / Pharma

PH01. Using a Picture Format to Create Visit Windows
Richann Watson, Batavia, OH

PH02. Random assignment of proxy event dates to unexposed individuals in observational studies: An automated technique using SAS®
Raymond Harvey, UnitedHealthcare®
Dana Drzayich Jankus, UnitedHealthcare®
David Mosley, UnitedHealthcare®

PH03. A Simple Solution for Generating Two Sets of Similar Reports
Lion Li, Merck Serono, Beijing, China
Zheng Wang, Merck Serono, Beijing, China
Yanhong Li, Merck Serono, Beijing, China

PH04. Programmed Assisted Patient Narratives (PANs) with New SAS® ODS and Graphical Approaches
Faye Yeh, MS, Takeda Development Center America, Inc, Deerfield, IL
Melvin Munsaka, PhD, Takeda Development Center America, Inc, Deerfield, IL

PH05. SAS® Macro Tool to Find Source Data Sets Used in Programs
Prasanna Murugesan, Quintiles Inc., Overland Park, KS
Sushant Thakare, Quintiles Inc., Overland Park, KS

PH06. Get SAS®sy with PROC SQL
*** BEST PAPER ***
Amie Bissonett, Pharmanet/i3, Minneapolis, MN

PH08. The Health Care Information Factory: An Overview of the SAS® Enterprise Business Intelligence Environment at the Minnesota Department of Human Services
Mike Baldwin, Minnesota Information Technology Services

PH09. Reproducible Research Two Ways: SASweave vs. StatRep
Shannon M. Morrison, M.S., Quantitative Health Sciences – Cleveland Clinic Foundation, Cleveland, OH
Matthew T. Karafa, PhD., Quantitative Health Sciences – Cleveland Clinic Foundation, Cleveland, OH

PH10. An Analysis of Diabetes Risk Factors Using Data Mining Approach
Akkarapol Sa-ngasoongsong, Oklahoma State University, Stillwater, OK
Jongsawas Chongwatpol, Oklahoma State University, Stillwater, OK

PH11. A Multifaceted Approach to Generating Kaplan-Meier and Waterfall Plots in Oncology Studies
Stacey D. Phillips, PharmaNet/i3, Madison, WI

PH12. Error Reduction and Report Automation Approaches for Textually Dense Pharmaceutical Regulatory Conformance Incident Data
Barry deVille, Mark Wolff, SAS Institute Inc., Cary, NC

PH14. Using SAS® Software to Aid in the ICD-10 Code Set Implementation
*** BEST PAPER ***
Alexander Pakalniskis, M.S., Cedars-Sinai Medical Center, Los Angeles, CA
Nilesh Bharadwaj, B.S., Cedars-Sinai Medical Center, Los Angeles, CA
Caren Jones, MPH, Cedars-Sinai Medical Center, Los Angeles, CA
Alein T. Chun, Ph.D., Cedars-Sinai Medical Center, Los Angeles, CA



JMP

JM01. JMP® Spatial and Temporal Graphics: The Geocoded Moving Bubble Plot, Proc Geocode, and PowerPoint
George J. Hurley, The Hershey Company, Hershey, PA

JM02. Proficiency in JMP® Visualization
Charles Edwin Shipp, Consider Consulting, San Pedro, CA
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

JM03. JMP® Coders 101 Insights
Charles Edwin Shipp, Consider Consulting, San Pedro, CA
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

JM04. JMP® Pro Bootstrap Forest
*** BEST PAPER ***
George J. Hurley, The Hershey Company, Hershey, PA

JM05. Getting to the Good Part of Data Analysis: Data Access, Manipulation, and Customization Using JMP®
Audrey Ventura, SAS Institute Inc., Cary, NC

JM06. Google® Search Tips and Techniques for SAS® and JMP® Users
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Charles Edwin Shipp, Consider Consulting, San Pedro, CA



Posters

PO01. Use of SAS® to determine broiler chick feed color preference in correlation with performance under different housing light colors
*** BEST PAPER ***
Nichole Graham, Kansas State University, Manhattan, KS

PO02. Comparative Medication Adherence for Antihypertensive Therapy in Rural Ambulatory Clinics
Terrence J. Adam, University of Minnesota, Twin Cities, Minneapolis, MN



SAS 101

S101. Check and Summarize SAS Log Files
Qiling Shi, Pittsburgh, PA

S102. Why the Bell Tolls 108 times? Stepping Through Time with SAS
*** BEST PAPER ***
Peter Eberhardt, Fernwood Consulting Group Inc, Toronto, ON, Canada

S103. You Could Be a SAS® Nerd If . . .
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

S104. Exploring the PROC SQL _METHOD Option
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

S105. Things Dr Johnson Did Not Tell Me: An Introduction to SAS® Dictionary Tables
Peter Eberhardt, Fernwood Consulting Group Inc, Toronto, ON, Canada

S106. What to Do with a Regular Expression (No paper available)
Scott Davis, Experis, Portage, MI

S107. Sometimes One Needs an Option with Unusual Dates
Arthur S. Tabachneck, Ph.D., myQNA, Inc., Thornhill, ON, Canada
Matthew Kastin, I-Behavior, Inc., Louisville, CO
Xia Ke Shan, Chinese Financial Electrical Company, Beijing, China

S108. First Family Secret Santa (No paper available)
Ben First, US Bank, Madison, WI
Andrew First, Wells Fargo

S109. Add a Little Magic to Your Joins
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

S110. Executing a PROC from a DATA Step
Jason Secosky, SAS Institute Inc., Cary, NC

S111. Avoiding Pitfalls when Merging Data
James Lew, Compu-Stat Consulting, Scarborough, ON, Canada
Joshua Horstman, Nested Loop Consulting, Indianapolis, IN, USA

S112. The Good, The Bad, and The Ugly
Toby Dunn, AMEDDC&S (CASS), San Antonio, TX
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

S113. The SAS Data Step: Where Your Input Matters
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada

S114. Making it Add Up; Diagnosing Merge Issues Using PROC SQL and Simple Arithmetic
Eric Schreiber, PRA, Lenexa, KS

S115. Copy and Paste Almost Anything
Arthur S. Tabachneck, myQNA, Inc., Thornhill, ON, Canada
Randy Herbison, Westat, Rockville, MD
John King, Ouachita Clinical Data Services, Inc., Mount Ida, AR
Richard A. DeVenezia, Independent Consultant, Remsen, NY
Nate Derby, Stakana Analytics, Seattle, WA
Ben Powell, Genworth Financial, London, England

S116. The BEST. Message in the SASLOG®
Andrew T. Kuligowski

S117. A Cup of Coffee and Proc FCMP: I Cannot Function Without Them
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada

S118. Understanding SAS Index
Pramod.R, Target Corporation, Minneapolis, Minnesota

S120. Automagically Copying and Pasting Variable Names
Arthur S. Tabachneck, Ph.D., Insurance Bureau of Canada, Toronto, ON, Canada
Randy Herbison, Westat, Rockville, MD
Andrew Clapson, Ottawa, ON, Canada
John King, Ouachita Clinical Data Services, Inc., Mount Ida, AR
Roger DeAngelis, CompuCraft Inc., Newbury Park, CA
Tom Abernathy, Pfizer, Inc., New York, NY

S122. Using SAS® to Build Web Pages Linking Photographs with Google Maps
Arthur S. Tabachneck, Ph.D., myQNA, Inc. Thornhill, ON, Canada
William Klein, Ph.D., Toronto, ON, Canada

S124. The SAS® Hash Object: It’s Time To .find() Your Way Around
Peter Eberhardt, Fernwood Consulting Group Inc., Toronto, ON, Canada

S125. Setting the Table… of Contents: Using SAS ODS PDF Features to Organize, Link, and Navigate a Report
Betsy Enstrom, IDeaS-A SAS Company, Bloomington, MN

S126. A SAS® Solution to Create a Weekly Format
Susan Bakken, Aimia, Plymouth, MN

S127. Calling a Remote Macro Using %INCLUDE or a Stored Macro Facility
Tony Reeves, Research Analyst, Wisconsin Department of Health Services, Madison, WI

S128. Reading and Processing the Contents of a Directory
Ben Cochran, The Bedford Group, Raleigh, NC



Statistics and Analysis

SA01. Methods for Interaction Detection in Predictive Modeling Using SAS
Doug Thompson, PhD, Blue Cross Blue Shield of IL, NM, OK & TX, Chicago, IL

SA02. Using SAS to Determine the Sample Size on the Cohen’s Positive Kappa Coefficient Problem
Yubo Gao, University of Iowa, Iowa City, IA

SA03. Collapsing Levels of Predictor Variables for Logistic Regression and Weight of Evidence Coding
Bruce Lund, Marketing Associates, Detroit, MI
Steven Raimi, Marketing Associates, Detroit, MI

SA04. Getting the Code Right the First Time
Michael C. Frick, General Motors, Warren, MI

SA05. Estimating the discrete probability distribution of the age characteristic of Veteran populations using SAS®, SAS/OR® and SAS Simulation Studio® for use in population projection models
Michael C. Grierson, The Department of Veterans Affairs, Washington, DC

SA06. SAS® code to compute a set of new variables from Time Series variables
Anani K. Hoegnifioh, US Cellular, Chicago, IL

SA07. Modeling Complex Survey Data
Taylor Lewis, University of Maryland, College Park, MD

SA08. Are Sales Figures in Line With Expectations? Using PROC ARIMA in SAS® to Forecast Company Revenue
Saveth Ho, Deluxe Corporation, Shoreview, MN
Brian Van Dorn, Deluxe Corporation, Shoreview, MN

SA09. Models for Ordinal Response Data
Robin High, University of Nebraska Medical Center, Omaha, NE

SA10. Testing the Bayesian Suite of SAS® Procedures using Ecological Data and Comparing Simulations with WinBUGS
*** BEST PAPER ***
Matthew Russell, Dept. of Forest Resources, University of Minnesota, St. Paul, MN

SA11. ROC Analysis for the Evaluation of Ethyl Glucuronide (EtG) as a Long-term Alcohol Biomarker in Hair and Fingernails: Evidence from a Large College Drinking Study
Daniel Fuhrmann, Center for Applied Behavioral Health Research, University of Wisconsin-Milwaukee, Milwaukee, WI
Michael Fendrich, Center for Applied Behavioral Health Research, University of Wisconsin-Milwaukee, Milwaukee, WI
Lisa Berger, Center for Applied Behavioral Health Research, University of Wisconsin-Milwaukee, Milwaukee, WI
Charles Plate, United States Drug Testing Laboratories, Inc., Des Plaines, IL
Doug Lewis, United States Drug Testing Laboratories, Inc., Des Plaines, IL
Joseph Jones, United States Drug Testing Laboratories, Inc., Des Plaines, IL

SA12. Current Health Trends and Risk Behavior Analysis in American Youth: Using a Large Sample
Deanna Schreiber-Gregory, North Dakota State University, Fargo, ND

SA13. Combined Forecasts: What to Do When One Model Isn’t Good Enough
Ed Blair, SAS Institute Inc., Cary, NC
Michael Leonard, SAS Institute Inc., Cary, NC
Bruce Elsheimer, SAS Institute Inc., Cary, NC

SA15. Tips and Strategies for Mixed Modeling with SAS/STAT® Procedures
Kathleen Kiernan, SAS Institute Inc., Cary, NC
Jill Tao, SAS Institute Inc., Cary, NC
Phil Gibbs, SAS Institute Inc., Cary, NC

SA16. Model Selection Using Recursive Macro - Enhancements to R2 Selection in PROC REG
Anca M Tilea, University of Michigan, Ann Arbor MI
Philip L Francis III, Eastern Michigan University, Ypsilanti, MI
Brenda W Gillespie, PhD, University of Michigan, Ann Arbor, MI
Rajiv Saran, MD, University of Michigan, Ann Arbor, MI

SA17. Creating a SAS Frequency Data Set of Valid Categorical Variables for Export to Microsoft Excel or Access
Neil Kamdar, University of Michigan, Department of Biostatistics, Kidney Epidemiology and Cost Center (KECC), Ann Arbor, MI

SA19. The Avengers Assemble: Forecasting and Business Intelligence Team Up to Ease Daily Revenue Management
Patrick M. Kelly, BlueCross BlueShield of Tennessee, Chattanooga, TN
Betsy Enstrom, IDeaS-A SAS Company, Bloomington, MN