MWSUG 2016 Conference Proceedings

Cincinnati, Ohio
October 9-11, 2016

BI / Customer Intelligence Beyond the Basics Career Development Data Visualization and Graphics Hands-On Workshops Pharmaceutical Applications

Rapid Fire SAS 101 Statistics / Advanced Analytics System Architecture and Administration Tools of the Trade e-Posters

BI / Customer Intelligence

BI01. Building a Better Dashboard Using Base SAS® Software
*** BEST PAPER ***
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Joshua M. Horstman, Nested Loop Consulting, Indianapolis, IN
Roger D. Muller, Data-To-Events, Carmel, IN

BI02. Macro method to use Google Maps™ and SAS® to find the shortest driving and straight line distances between 2 addresses in the United States
Laurie Bishop, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH

BI03. SAS AUTOMATION & SAS CODE IMPROVEMENT (MAKING CODES DYNAMIC)
Arjun Kumar Shrestha, Centene Corporation, St Louis, MO

BI04. Reducing Customer Attrition with Predictive Analytics for Financial Institutions
Nate Derby, Stakana Analytics, Seattle, WA
Mark Keintz, Wharton Research Data Services, Philadelphia, PA

Beyond the Basics

BB01. How to Create Sub-sub Headings in PROC REPORT and Why You Might Want to: Thinking about Non-traditional Uses of PROC REPORT
Amy Gravely, Center for Chronic Disease Outcomes Research, A VA HSR&D Center of Innovation
Barbara Clothier, Center for Chronic Disease Outcomes Research, A VA HSR&D Center of Innovation

BB02. 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

BB03. I’ve Got to Hand It to You; Portable Programming Techniques
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK
Mary F. O. Rosenbloom, Alcon, a Novartis Company, Lake Forest, CA

BB04. Simplifying Your %DO Loop with CALL EXECUTE
Arthur Li, City of Hope National Medical Center, Duarte, CA

BB06. Be Prompt! Do it Now! Creating and Using Prompts in SAS Enterprise Guide
Ben Cochran, The Bedford Group, Raleigh, NC

BB07. Be Prompt – Part II! Advanced Prompting Techniques in SAS? Enterprise Guide
Ben Cochran, The Bedford Group, Raleigh, NC

BB08. Don’t Let Your Annual Report Be Such a Manual Report: Neat New Ways from SAS to Combine Text, Graphs and Tabular Reports in a Single Document
Ben Cochran, The Bedford Group, Raleigh, NC

BB11. Read html files and create standard tables for BISR at KUMC
Chuanwu Zhang, Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS
John Keighley, Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS
Byron J. Gajewski, Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS

BB12. Time is of Essence: The power of “MISS” is “NOT being missed”!
Gowri Madhavan, Cincinnati Children’s Hospital, Cincinnati, OH
Brittney Delev, Cincinnati Children’s Hospital, Cincinnati, OH

BB14. WAPTWAP, but remember TMTOWTDI
Jack N Shoemaker, Greensboro, NC

BB15. Anatomy of a Merge Gone Wrong
*** BEST PAPER ***
Joshua Horstman, Nested Loop Consulting, Indianapolis, IN

BB16. Solving Common PROC SQL Performance Killers when using ODBC
John Schmitz, Luminare Data LLC, Omaha, NE

BB17. Five Little Known, But Highly Valuable and Widely Usable, PROC SQL Programming Techniques
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

BB18. SAS Advanced Programming with Efficiency in Mind: A Real Case Study
Lingqun Liu, University of Michigan, Ann Arbor, MI

BB19. How to Speed Up Your Validation Process Without Really Trying?
Alice Cheng, Portola Pharmaceuticals, South San Francisco, CA
Mike Wise, Experis

BB20. Leads and Lags: Static and Dynamic Queues in the SAS® DATA STEP
Mark Keintz, Wharton Research Data Services, Philadelphia, PA

BB21. Finding National Best Bid and Best Offer – Quote by Quote
Mark Keintz, Wharton Research Data Services, Philadelphia, PA

BB22. From Stocks to Flows: Using SAS® HASH objects for FIFO, LIFO, and other FO’s
Mark Keintz, Wharton Research Data Services, Philadelphia, PA

BB23. Automating the Process of Generating Publication Quality Regression Tables through SAS® Base Programming
Ji Qi, University of Michigan Health System, Ann Arbor, MI

BB26. Implementing a Medicaid Claims Processing System using SAS® software A Novel Implementation of BASE SAS® Using Limited Resources in an Effective Manner
Stephen C. DeVoyd, Ohio Department of Developmental Disabilities, Columbus, OH

BB27. A Macro that can Search and Replace String in your SAS Programs
Ting Sa, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH

BB28. A Macro That Can Fix Data Length Inconsistency and Detect Data Type Inconsistency
Ting Sa, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH

BB29. A DDE Macro to Put Data Anywhere in Excel
Ting Sa, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
Shiran Chen, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH

BB30. A Failure To EXIST: Why Testing for Data Set Existence with the EXIST Function Alone Is Inadequate for Serious Software Development in Asynchronous, Multiuser, and Parallel Processing Environments
Troy Martin Hughes

BB31. Stress Testing and Supplanting the SAS® LOCK Statement: Implementing Mutex Semaphores To Provide Reliable File Locking in Multiuser Environments To Enable and Synchronize Parallel Processing
Troy Martin Hughes

BB32. All Aboard! Next Stop is the Destination Excel
William E Benjamin Jr, Owl Computer Consultancy, LLC, Phoenix, AZ

BB34-. Data Analysis with User-Written DS2 Packages
Robert Ray, SAS Institute Inc., Cary, NC
William Eason, SAS Institute Inc., Cary, NC

Career Development

CD01. What's Hot – Skills for SAS® Professionals
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

CD04. Statistical Volunteering With SAS - Experiences and Opportunities
David Corliss, Peace-Work, Plymouth, MI

CD05. Recruiting and Retention Strategies for 2016 in the SAS Programmer Staffing Organizations
Helen Chmiel, Experis, Inc, Kalamazoo, MI
Mindy Kiss, Experis, Inc, Kalamazoo, MI
Andrea Moralez, Experis, Inc, Kalamazoo, MI

CD06. Mentoring and Oversight of Programmers across Cultures and Time Zones
*** BEST PAPER ***
Chad Melson, Experis Clinical, Cincinnati, OH

CD07. How to Use LinkedIn to Effectively Boost Your Career Development
Nate Derby, Stakana Analytics, Seattle, WA

Data Visualization and Graphics

DV01. Using Animation to Make Statistical Graphics Come to Life
*** BEST PAPER ***
Jesse Pratt, Cincinnati Children’s Hospital Medical Center

DV04. SAS/GRAPH® and GfK Maps: a Subject Matter Expert Winning Combination
Louise S. Hadden, Abt Associates Inc., Cambridge, MA

DV05. Red Rover, Red Rover, Send Data Right Over: Exploring External Geographic Data Sources with SAS®
Louise S. Hadden, Abt Associates Inc., Cambridge, MA

DV08. Four Thousand Reports Three Ways
Stephanie R. Thompson, Rochester Institute of Technology, Rochester, NY

DV09. Using Big Data to Visualize People Movement Using SAS® Basics
Stephanie R. Thompson, Rochester Institute of Technology, Rochester, NY

DV10-. Annotating the ODS Graphics Way!
Dan Heath, SAS Institute Inc., Cary, NC

Hands-On Workshops

HW01. A Hands-On Introduction to SAS® Visual Analytics Reporting
David Foster, Pinnacle Solutions Inc, Indianapolis, IN

HW02. A Hands-on Introduction to SAS® DATA Step Hash Programming Techniques
*** BEST PAPER ***
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

HW03. The Joinless Join ~ The Impossible Dream Come True; Expand the Power of Base SAS® and SAS® Enterprise Guide® in a New Way
Kent Phelps, The SASketeers, Des Moines, IA
Ronda Phelps, The SASketeers, Des Moines, IA

HW04. Working with the SAS® ODS EXCEL Destination to Send Graphs, and Use Cascading Style Sheets When Writing to EXCEL Workbooks
William E Benjamin Jr, Owl Computer Consultancy, LLC, Phoenix, AZ

HW05. Intermediate SAS® Macro Programming
Chuck Kincaid, Experis Business Analytics

Pharmaceutical Applications

PH01. Pre-Data Checks for SDTM Development
Abhinav Srivastva, PaxVax Inc., Redwood City, CA

PH03. Surviving Septic Shock: How SAS© Helped a Critical Care Nursing Staff Fulfill Its Septic Shock Reporting Requirements
*** BEST PAPER ***
Joe Palmer, OhioHealth, Columbus OH

PH04. Establishing Similarity of Modeled and Experimental PK/PD Hysteretic Loops using Pseudo Time Series Analysis and Dynamic Time Warping
Ron Smith, Florida Southwestern State College, Fort Myers, FL

PH05. Fitting Complex Statistical Models with PROCs NLMIXED and MCMC
Robin High, University of Nebraska Medical Center, Omaha, NE

PH06. Frequentist and Bayesian Interim Analysis in Clinical Trials: Group Sequential Testing and Posterior Predictive Probability Monitoring Using SAS
Kechen Zhao, University of Southern California Keck School of Medicine, Division of Biostatistics, Los Angeles, CA

Rapid Fire

RF01. MWSUG 2016 - Paper RF01 Two Shades of Gray: Implementing Gray’s Test for Equivalence of CIF in SAS 9.4
*** BEST PAPER ***
Tyler Ward, Grand Valley State University, Allendale, MI
Zachary Weber, Grand Valley State University, Allendale, MI

RF02. Utilizing PROC CONTENTS with Macro Programming to Summarize and Tabulate Copious Amounts of Data
Kathryn Schurr, M.S., Quest Diagnostics, Hudsonville, MI
Erica Goodrich, M.S., Brigham and Women’s Hospital, Boston, MA

RF03. A Quick Macro to Replace Missing Values with NULL for Numeric Fields within a CSV File
John Schmitz, Luminare Data LLC, Omaha, NE

RF04. Importing CSV Data to All Character Variables
Arthur L. Carpenter, California Occidental Consultants, Anchorage, AK

RF05. Fitting a Cumulative Logistic Regression Model
Shana Kelly, Spectrum Health: Healthier Communities, Grand Rapids, MI

RF06. Your Place or Mine: Data-Driven Summary Statistic Precision
Nancy Brucken, inVentiv Health, Ann Arbor, MI

RF07. An Application of CALL VNEXT
John Henry King, Ouachita Clinical Data Services Inc., Caddo Gap, AR

RF08. The Power of Cumulative Distribution Function (CDF) Plot in Assessing Clinical Outcomes
Min Chen, Cook Research Inc., West Lafayette, IN
Patricia Kultgen, Cook Research Inc., West Lafayette, IN

RF09. Quantifying the relative importance of crime rate on Housing prices
Aigul Mukanova, University of Cincinnati, Cincinnati, OH

RF10. I Have a Secret: How Can I Hide Small Numbers from Public View?
Fred Edora, South Carolina Department of Education, Columbia, SC

RF11. Tips and Tricks for Producing Time-Series Cohort Data
Nate Derby, Stakana Analytics, Seattle, WA

RF12. Generating Custom Shape Files for Data Visualization
Stephanie R. Thompson, Rochester Institute of Technology, Rochester, NY

RF13. An Animated Guide: Insights into the Logic of the ROC Curve
Russ Lavery, Bryn Mawr, PA

SAS 101

SA01. Top Ten SAS® Performance Tuning Techniques
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

SA02. SAS® Debugging 101
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

SA05. Simplifying Effective Data Transformation Via PROC TRANSPOSE
Arthur X. Li, City of Hope Comprehensive Cancer Center, Duarte, CA

SA06. Painless Extraction: Options and Macros with PROC PRESENV
*** BEST PAPER ***
Keith Fredlund, Grand Valley State University, Allendale, MI
Thinzar Wai, Grand Valley State University, Allendale, MI

SA08. Hashtag #Efficiency! An Introduction to Hash Tables
Lakshmi Nirmala Bavirisetty, South Dakota State University, Sioux Falls, SD
Deanna Naomi Schreiber-Gregory, National University, Moorhead, MN
Kaushal Chaudhary, Eli Lilly, Indianapolis, IN

SA09. Array of Sunshine: Casting Light on Basic Array Processing
Nancy Brucken, inVentiv Health, Ann Arbor, MI

SA10. Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS® Code
Joshua M. Horstman, Nested Loop Consulting, Indianapolis, IN

SA11. Accessing Teradata through SAS, common pitfalls, solutions and tips
Kiran Venna, Experis Business Analytics Practice

Statistics / Advanced Analytics

AA01. The Practice of Credit Risk Modeling for Alternative Lending
Keith Shields, Chief Analytics Officer, Magnify Analytic Solutions, Detroit, Wilmington, Charlotte
Bruce Lund, Consultant, Magnify Analytic Solutions, Detroit, Wilmington, Charlotte

AA02. Identifying the factors responsible for loan defaults and classification of customers using SAS® Enterprise Miner
Juhi Bhargava, Oklahoma State University, Stillwater, OK
Prashanth Reddy Musuku, Oklahoma State University, Stillwater, OK

AA03. Property and Casualty Insurance Predictive Analytics in SAS®
Mei Najim, Gallagher Bassett Services, Itasca, IL

AA04. Discover the golden paths, unique sequences and marvelous associations out of your big data using Link Analysis in SAS® Enterprise Miner TM
Delali Agbenyegah, Alliance Data Systems, Columbus, OH
Candice Zhang, Alliance Data Systems, Columbus, OH

AA05. Hybrid recommendation system to provide suggestions based on user reviews
Ravi Shankar Subramanian, Oklahoma State University
Shanmugavel Gnanasekar, Oklahoma State University

AA06. Analyzing sentiments in tweets for Tesla Model 3 using SAS Enterprise Miner and SAS Sentiment Analysis Studio
Tejaswi Jha, Oklahoma State University, Stillwater, OK
Praneeth Guggila , Oklahoma State University, Stillwater, OK

AA11. Assessing the Impact of Communication Channel on Behavior Changes in Energy Efficiency
Angela Wells, Senior Analyst, Direct Options
Ashlie Ossege, VP Analytic Services, Direct Options

AA12. An Innovative Method of Customer Clustering
Brian Borchers, Ph.D., Direct Options Inc. West Chester, OH

AA14. An Analysis of the Repetitiveness of Lyrics in Predicting a Song’s Popularity
Drew Doyle, University of Central Florida, Orlando, FL

AA15. Weight of Evidence Coding and Binning of Predictors in Logistic Regression
Bruce Lund, Independent Consultant, Novi, MI

AA17. Using SAS® to Generate p-Values with Monte Carlo Simulation
Brandy R. Sinco, MS, University of Michigan, Ann Arbor, MI
Edith Kieffer, PhD, University of Michigan, Ann Arbor, MI
Michael S. Spencer, PhD, University of Michigan, Ann Arbor, MI
Michael Woodford, PhD, Wilfrid Laurier University, Ontario, Canada
Gloria Palmisano, BS, MA, CHASS Center, Detroit, MI
Gretchen Piatt, PhD, University of Michigan, Ann Arbor, MI
Michele Heisler, M.D., University of Michigan, Ann Arbor, MI

AA18. To be two or not be two, that is a LOGISTIC question
Robert G. Downer, Grand Valley State University, Allendale, MI

AA19. Is My Model the Best? Methods for Exploring Model Fit
Deanna N Schreiber-Gregory, National University, Moorhead, MN

AA20. An Introduction to the HPFOREST Procedure and its Options
Carl Nord, Grand Valley State University, Grand Rapids, MI
Jacob Keeley, Grand Valley State University, Grand Rapids, MI

AA21. Use Multi-Stage Model to Target the Most Valuable Customers
Chao Xu, Alliance Data Systems, Columbus, OH
Jing Ren, Alliance Data Systems, Columbus, OH
Hongying Yang, Alliance Data Systems, Columbus, OH

AA22. Analyzing the effect of Weather on Uber Ridership
Snigdha Gutha, Oklahoma State University
Anusha Mamillapalli, Oklahoma State University

AA23. A Demonstration of Various Models Used in a Key Driver Analysis
Steven LaLonde, Rochester Institute of Technology, Rochester, NY

AA24. An Animated Guide: Penalized variable selection techniques in SAS® and Quantile Regression
*** BEST PAPER ***
Russ Lavery, Bryn Mawr, PA
Peter Flom, New York City, NY

AA25. An Animated Guide: Deep Neural Networks in SAS® Enterprise Miner
Russ Lavery, Bryn Mawr, PA

AA26. The State of Human Trafficking in the Cincinnati Metropolitan Area - a Statistical Case Study
David J Corliss, Peace-Work, Plymouth, MI
Heather M. Hill, Peace-Work, Plymouth, MI

AA27. Fixed Item Parameter Calibration with MMLE-EM Using a Fixed Prior in SAS/IML®
Sung-Hyuck Lee, ACT, Iowa City, IA
Hongwook Suh, ACT, Iowa City, IA

AA29-. Fitting Your Favorite Mixed Models with PROC MCMC
Fang Chen, SAS Institute Inc., Cary, NC
Gordon Brown, SAS Institute Inc., Cary, NC
Maura Stokes, SAS Institute Inc., Cary, NC

AA30-. Modeling Longitudinal Categorical Response Data (No paper available)
Maura Stokes, SAS Institute Inc., Cary, NC

System Architecture and Administration

SY01. Spawning SAS® Sleeper Cells and Calling Them into Action: Implementing Distributed Parallel Processing in the SAS University Edition Using Commodity Computing To Maximize Performance
Troy Martin Hughes

SY02. Key Tips for SAS® Grid Users
Venkateswarlu Toluchuri, United Health Group, Hyderabad, India

SY03. Enterprise Architecture for Analytics Using TOGAF
David Corliss, Ford Motor Company, Dearborn, MI

SY04. Avoiding Code Chaos - Architectural Considerations for Sustainable Code Growth
*** BEST PAPER ***
David L. Ward, Nashville, TN

SY05-. SAS® Grid Administration Made Simple
Scott Parrish, SAS Institute Inc.
Linda Zeng, SAS Institute Inc.
Paula Kavanagh, SAS Institute Inc.

Tools of the Trade

TT01. Downloading, Configuring, and Using the Free SAS® University Edition Software
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA
Ryan Paul Lafler, High School Student, Spring Valley, CA
Charles Edwin Shipp, Consider Consulting Corporation, San Pedro, CA

TT02. Removing Duplicates Using SAS®
Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, CA

TT03. Calculating Cardinality Ratio in Two Steps
Ronald J. Fehd, Stakana Analytics

TT04. A Sysparm Companion, Passing Values to a Program from the Command Line
Ronald J. Fehd, Stakana Analytics

TT06. List Processing Macro Function CallText: Read a Control Data Set and Unquote each Item
Ronald J. Fehd, Stakana Analytics

TT07. A SAS® Macro to Create a Data Dictionary with Ease
Amy Gravely, Center for Chronic Disease Outcomes Research, A VA HSR&D Center of Innovation
Barbara Clothier, Center for Chronic Disease Outcomes Research, A VA HSR&D Center of Innovation

TT08. Take a SPA Day with the SAS® Performance Assessment (SPA): Baselining Software Performance Across Diverse Environments To Elucidate Performance Placement and Performance Drivers
Troy Martin Hughes

TT09. Your Local Fire Engine Has an Apparatus Inventory Sheet and So Should Your Software: Automatically Generating Software Use and Reuse Libraries and Catalogs from Standardized SAS® Code
Troy Martin Hughes

TT10. Performing Pattern Matching by Using Perl Regular Expressions
Arthur Li, City of Hope National Medical Center, Duarte, CA

TT11. Base SAS® and SAS® Enterprise Guide® ~ Automate Your SAS World with Dynamic Code; Your Newest BFF (Best Friend Forever) in SAS
Kent Phelps, The SASketeers, Des Moines, IA
Ronda Phelps, The SASketeers, Des Moines, IA

TT12. Defensive Coding by Example: Kick the Tires, Pump the Brakes, Check Your Blind Spots, and Merge Ahead!
*** BEST PAPER ***
Nancy Brucken, inVentiv Health, Ann Arbor, MI
Donna E. Levy, inVentiv Health, Cary, NC

TT13. An Animated Guide: The Internals of PROC REPORT
Russ Lavery, Bryn Mawr, PA

TT14. Ron Fehd, SAS-L’s Macro Maven, Answers Your Macro Questions
Ronald J. Fehd, Stakana Analytics

TT16-. Recapping Two Winning ODS Layout Talks for SAS® 9.4: ODS Destination for PowerPoint and the ODS PDF Destination
Bari Lawhorn, SAS Institute Inc., Cary, NC

e-Posters

PO02. Sorting a Bajillion Records: Conquering Scalability in a Big Data World
Troy Martin Hughes

PO03. A Predictive Logistic Regression Model for Chronic Kidney Disease
Jingye Wang, Brown School at Washington University

PO04. When ANY Function Will Just NOT Do
Richann Watson, Experis
Karl Miller, inVentiv Health

PO05. Multicollinearity: What Is It and What Can We Do About It?
Deanna N Schreiber-Gregory, National University, Moorhead, MN

PO06. Protein NMR Reference Correction - A Statistical Approach for an Old Problem
Xi Chen, University of Kentucky
Hunter N.B. Moseley, University of Kentucky

PO07. StatTag: A New Tool for Conduction Reproducible Research with SAS
*** BEST PAPER ***
Abigail Baldridge, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine
Luke Rasmussen, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine
Leah Welty, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine

PO08. Document and Enhance Your SAS® Code, Data Sets, and Catalogs with SAS Functions, Macros, and SAS Metadata
Roberta Glass, Abt Associates Inc., Cambridge, MA
Louise S. Hadden, Abt Associates Inc., Cambridge, MA

PO09. What to Expect When You Need to Make a Data Delivery. . . Helpful Tips and Techniques
Tom McCall, Abt Associates Inc., Bethesda, MD
Louise S. Hadden, Abt Associates Inc., Cambridge, MA

PO13. Regression Analysis of the Levels of Chlorine in the Public Water Supply in Orange County, FL
Drew Doyle, University of Central Florida, Orlando, FL