SAS® Analytics ● SAS® Visual Analytics® ● SAS® Enterprise Guide® ● SAS to Excel SQL vs DATA Step ● SAS Batch ● Pharma Data ● Data Visualization ● Machine Learning

For SAS Users from Illinois, Wisconsin, and Anywhere in the North Central USA

Wisconsin Illinois SAS Users Conference on June 28, 2017 At the Woman’s Club of Wisconsin in Milwaukee

~ Learn from SAS experts and SAS users just like you in parallel sessions on SAS and SAS Analytics ~ Network with fellow SAS users (including at the post-conference social) ~ See SAS demos and get in-depth questions answered

What to Expect at This International Event

Prof. David Dickey gives an extended presentation on Mixed Models. Tricia Aanderud creates dashboards with SAS Visual Analytics. Paul Segal talks about the modern analytical landscape and optimizing the analytical data life cycle. Mei Najim offers a case study in insurance claim analytics. Chevell Parker explains creating Excel worksheets and pivot tables with SAS. Charu Shankar gives advanced tutorials on SAS Enterprise Guide and SAS Data Step vs SQL. Alex Ge presents use cases for SAS and open source analytics. Nancy Brucken explains automating SAS program batch runs, and provides best practices for QS and ADQRS. Gene Ray analyzes fire department response times. Jay Iyengar provides a Base SAS tutorial. Andrea Frazier evaluates a simple intervention to reduce nursing home length of stay. David Oesper shows how to automate the updating of Excel workbooks. Dennis Low uses multiple methods to predict acute inpatient length of stay. LeRoy Bessler shares the keys to visual data insights and presents his method of “machine learning” with SAS software. In the SAS Demo Room, Melissa Perez explains the many SAS programs and resources available to users, Ken Pikulik covers self-service data management for analytics users across the enterprise, Nancy Brucken offers assistance with CDISC implementation issues and questions, and Paul Segal presents benefits of integrating SAS Analytics with big data. Proceedings-Only content is slides, papers, and code on conference-related topics from Vince DelGobbo, Jay Iyengar, and LeRoy Bessler.

Proceedings/Tools - Conference slides/papers, a selection of other conference-topic-related slides/papers, code tools, and guided links to resources at SAS and around the world are provided for attendees.

SAS Books - There be examination copies of Prof. Dickey’s book and Tricia Aanderud’s books. They will be raffled off at end of conference, as well as four coupons to order any SAS book of your choices. After the conference you will be able to order any SAS book at discount and with free shipping.

Conference Admission - Online registration or mail-in pre-payment must be received no later than June 23. We accept credit cards, personal check, company check, or money order, but no purchase orders. The fee includes admission, Conference Proceedings & Tools, continental breakfast, lunch, and beverages. REGISTER EARLY. Space is limited. Registration information is on the last page of this brochure.

Conference Site, etc. - There is free parking. See the second to last page for map and overnight accommodations.

Sponsors - We thank SAS Institute, Teradata, and Zencos Consulting for sponsorship and support! For how they can help you with your SAS-related needs, please see their web sites www.sas.com , www.teradata.com , and www.zencos.com .

We look forward to seeing you at the conference.

Craig Wildeman, Andrea Frazier, David Bruckner, Laura MacBride, and LeRoy Bessler Your WIILSU Conference Team ~ www.wiilsu.org

For Questions about Registration: David at registrar@wiilsu.org or 920-457-4442 x-77577

For Questions about the Conference: Craig at craig.wildeman@kohler.com or 920-457-4442 x-72118 All SAS products are trademarks of SAS Institute Inc. (Cary, NC) in the USA and other countries. ® denotes USA registration.

Visiting Speaker / SAS Press Author

David A. Dickey is William Neal Reynolds Distinguished Professor in Statistics at North Carolina State University, where he teaches graduate courses in statistical methods and time series. An accomplished SAS user since 1976 and prolific author, Dr. Dickey is a co-inventor of the Dickey-Fuller test used in SAS/ETS® software. He received his Ph.D. in statistics from Iowa State University in 1976, is a fellow of the American Statistical Association, and a member of the Institute of Mathematical Statistics. Prof. Dickey lives in Raleigh with wife Barbara. They have married children, Dr. Michael Dickey, an NCSU Chemical Engineering Professor, and Susan Dickey McShane, a graphic artist. They have three granddaughters, Aliyah and Emerson Dickey and Gillian McShane, and a grandson Declan McShane.

Dr. Dickey is a contract instructor for SAS and has spoken at many SESUG, SUGI, and SAS Global Forum conferences and in Milwaukee at MWSUG 2010 and at the 2011, 2013, and 2015 Wisconsin Illinois SAS Users Conference. He began working with SAS in 1981 and teaches or has taught time series, mixed models, regression and ANOVA, experimental design, IML, multivariate analysis, mixed models, generalized mixed models, and nonlinear mixed models at SAS locations and companies in the US, Canada, and New Zealand. Professor Dickey has coauthored several text books including Principles and Procedures of Statistics, third edition (Steel, Torrie, and Dickey), Applied Regression Analysis (Rawlings, Pantula, and Dickey), and Linear Statistical Models (Bowerman, O'Connel, and Dickey). For SAS Press, he coauthored SAS System for Forecasting Time Series, first and second editions (Brocklebank and Dickey) with an upcoming third edition in the works . In 1993, Dr. Dickey was initiated into NC State's Academy of Outstanding Teachers. In 2000, he was made a Fellow of the American Statistical Association in recognition of his teaching and time series research. In 2008, he was inducted into NC State's Academy of Outstanding Faculty Engaged in Extension and, in recognition for his work with the College of Agriculture and Life Sciences, was awarded the William Neal Reynolds Chair. Professor Dickey was a founding faculty member in the NCSU Institute for Advanced Analytics, is a member of the Financial Math faculty and an Associate faculty member of the Department of Agricultural and Resource Economics as well as the Integrated Manufacturing Systems Institute at NCSU. SAS for Forecasting Time Series, Second Edition In this book, Brocklebank and Dickey show you how SAS performs univariate and multivariate time series analysis. Taking a tutorial approach, the authors focus on the procedures that most effectively bring results: the advanced procedures ARIMA, SPECTRA, STATESPACE, and VARMAX. They demonstrate the interrelationship of SAS/ETS procedures with a discussion of how the choice of a procedure depends on the data to be analyzed and the results desired. With this book, you will learn to model and forecast simple autoregressive (AR) processes using PROC ARIMA, and you will learn how to fit autoregressive and vector ARMA processes using the STATESPACE and VARMAX procedures. Other topics covered include detecting sinusoidal components in time series models, performing bivariate cross-spectral analysis, and comparing these frequency-based results with the time domain transfer function methodology. New and updated examples in the second edition include retail sales with seasonality, ARCH models for stock prices with changing volatility, vector autoregression and cointegration models, intervention analysis for product recall data, expanded discussion of unit root tests and nonstationarity, and expanded discussion of frequency domain analysis and cycles in data.

Visiting Speaker / SAS Press Author

Tricia Aanderud, Director, Data Visualization Practice at Zencos Consulting, provides SAS Consulting services to organizations that need assistance in transforming their data into meaningful reports and dashboards. She has co-authored three books with her most recent title "Introduction to SAS Visual Analytics". You can read her data visualization tips and other SAS knowledge on the BI Notes blog (http://www.bi-notes.com). Tricia has a background in technical writing, process engineering, and customer service. Since 2002, she has been an enthusiastic SAS user and has presented papers at the SAS Global Forum and other industry conferences. Born in Kentucky, she now lives in Raleigh, NC, with her husband and four bratty Javanese cats.

An Introduction to SAS® Visual Analytics: How to Explore Numbers, Design Reports, and Gain Insight into Your Data Tricia Aanderud, Rob Collum, Ryan Kumpfmiller When it comes to business intelligence and analytical capabilities, SAS® Visual Analytics is the premier solution for data discovery, visualization, and reporting. An Introduction to SAS® Visual Analytics will show you how to make sense of your complex data with the goal of leading you to smarter, data-driven decisions without having to write a single line of code – unless you want to! You will be able to use SAS® Visual Analytics to access, prepare, and present your data to anyone anywhere in the world. SAS® Visual Analytics automatically highlights key relationships, outliers, clusters, trends and more. These abilities will guide you to critical insights that inspire action from your data. With this book, you will become proficient using SAS® Visual Analytics to present data and results in customizable, robust visualizations, as well as guided analyses through auto-charting. With interactive dashboards, charts, and reports, you will create visualizations which convey clear and actionable insights for any size and type of data. This book largely focuses on the version of SAS® Visual Analytics on SAS® 9.4, although it is available on both 9.4 and SAS® Viya platforms. Each version is considered the latest release, with subsequent releases planned to continue on each platform; hence, the Viya version works similarly to the 9.4 version and will look familiar. This book covers new features of each and important differences between the two. With this book, you will learn how to: - Build your first report using the SAS® Visual Analytics Designer - Prepare a dashboard and determine the best layout - Effectively use geo-spatial objects to add location analytics to reports - Understand and use the elements of data visualizations - Prepare and load your data with the SAS® Visual Analytics Data Builder - Analyze data with a variety of options, including forecasting, word clouds, heat maps, correlation matrix, and more - Understand administration activities to keep SAS® Visual Analytics humming along - Optimize your environment for considerations such as scalability, availability, and efficiency between components of your SAS® software deployment and data providers Building Business Intelligence Using SAS®: Content Development Examples Tricia Aanderud, Angela Hall Business intelligence (BI) software provides an interface for multiple audiences to dissect, discover, and decide what the data means. These reporting tools make dynamic information available to all users, giving everyone the ability to manipulate results and further understand the business. There is significant power in reducing the data gatekeeper role in your organization so that each person can quickly interact with data and uncover additional value. SAS offers a BI solution that provides mechanisms to reach every level of the organization. Each tool in this solution provides a different amount of complexity and functionality to aid a broad deployment. This book clarifies how you can fully leverage each SAS BI solution component to ensure a successful implementation. Focusing on the SAS BI Clients, the authors provide a quick-start guide loaded with examples and tips that will help users move quickly from using only one of the SAS BI Clients to using a significant portion of the system. So if you are a SAS BI or SAS Enterprise BI user, but you aren't yet using all the components of the solution, this book is the resource that you need. In addition, the tips and techniques provided in this book will prove invaluable for advanced SAS BI and SAS Enterprise BI users who are studying for SAS Certified BI Content Developer certification.

Speakers, Sponsors, Volunteers

Melissa Perez Tricia Aanderud Nancy Brucken David Bruckner David Dickey SAS User Group Support Speaker Speaker Volunteer Speaker SAS Institute Zencos Consulting inVentive Health Kohler Co North Carolina State University Cary, NC Cary, NC Ann Arbor, Michigan Kohler, WI Raliegh, NC

Andrea Frazier Alex Ge Jay Iyengar Dennis Low Laura MacBride Speaker & Volunteer Speaker Speaker Speaker Volunteer Presence Health SAS Institute Data Systems Consultants Presence Health Marquettte University Chicago, IL Cary, NC Northbook, IL Chicago, IL Milwaukee, WI

Mei Najim David Oesper Chevell Parker---- Ken Pikulik Gene Ray Speaker Speaker Speaker Speaker Speaker Gallagher Bassett Lands’ End SAS Institute Teradata Kennesaw State University Chicago, IL Dodgeville, WI Cary, NC Cary, NC Kennesaw, GA

Paul Segal Charu Shankar Craig Wildeman LeRoy Bessler Speaker Speaker Volunteer Speaker & Volunteer Teradata SAS Institute Kohler Co. Bessler Consulting & Research San Diege, CA Toronto, ON, Canada Kohler, WI Mequon, Milwaukee, WI

Presentation Abstracts and Information about Speakers & Volunteers SAS Section 1 (MAIN ROOM, SECOND FLOOR)

Stacking Up - Horizontal or Vertical with PROC SQL or DATA Step Charu Shankar, SAS Institute Come to this advanced tutorial to learn the huge power of SQL joins, the flexibility, and short code writing. See the four different joins, both ways (SQL vs Data Step merge): Inner Join Outer Join – left, right, full Learn how the data step operates when you merge data, and the prerequisites for the DATA Step, as well as when to use it. Learn how PROC SQL stacks data vertically with classic set operations. Learn how the DATA Step stacks data vertically and its flexibility. With visuals included to apply knowledge transfer, come to this powerful session to take away the best way to stack data vertically or horizontally, and when to use which Base SAS tool. Charu Shankar helps train individuals and organizations make winning decisions using Analytics. She is a Technical Training Specialist with SAS since 2007. Before SAS, Charu worked at UNESCO, Rotman School of Management, etc. She teaches SAS, SQL, DS2, SAS Enterprise Guide, and BI. She enjoys teaching by engaging her students with logic, visuals, and analogies to spark critical reasoning. Charu interviews clients to recommend the right SAS training to help meet their needs. She is a frequent blogger for the SAS Training Post, at www.Blogs.sas.com. When she’s not teaching technology, she is passionate about helping people come alive by teaching yoga and is an ardent foodie—chef, caterer, & blogger. Check out www.charuyoga.com for yoga tips n tricks. Also check out her YouTube yoga channel charuyoga . The Modern Analytical Landscape Paul Segal, Data Science Practice, Teradata Data and analytics have evolved and have become more complex. The volumes of the data and the variety of the data sources have grown exponentially. These challenges present many opportunities for organizations to grow and expand their businesses if you have the right analytical approach and strategy. In this presentation, we will examine the rapidly changing analytical landscape and how SAS fits into it. Paul Segal has been working in the analytics field for 30 years and has worked across a wide variety of industries and utilized a wide variety of tools, including close to 30 years of SAS. He is currently a senior Analytics consultant at Teradata, and a foundation member of the Teradata and SAS Centre of Expertise. He has a Bachelor of Economics majoring in Econometrics and a post graduate degree in Statistical Theory. More than a Pretty Face: Worksheets and Pivot Tables That Look Great and Answer Difficult Questions Chevell Parker, SAS Institute Microsoft Excel worksheets enable you to explore data that answers the difficult questions that you face daily in your work. When you combine the SAS® Output Deliver System (ODS) with the capabilities of Excel, you have a powerful toolset you can use to manipulate data in various ways, including highlighting data, using formulas to answer questions, and adding a pivot table or graph. In addition, ODS and Excel give you many methods for enhancing the appearance of your tables and graphs. This paper, written for the beginning analyst to the most advanced programmer, illustrates first how to manipulate styles and presentation elements in your worksheets by controlling text wrapping, highlighting and exploring data, and specifying Excel templates for data. Then, the paper explains how to use the TableEditor tagset and other tools to build and manipulate both basic and complex pivot tables that can help you answer all of the questions about your data. You will also learn techniques for sorting, filtering, and summarizing pivot-table data. Chevell is a member of the Foundation SAS group within SAS Technical Support. His support areas include the Output Delivery System, XML technologies, and the SAS Python Client Interface for Viya.

Secrets I learned from Reviewing 500 Dashboards Tricia Aanderud, Zencos Consulting A fierce dashboard is not an accident – it is the result of careful planning, design knowledge, and the right data. During this talk, we will review some dashboards that went right and explore those where the target was missed. You will learn the techniques professionals use for creating dashboards that are engaging, beautiful, and functional. SAS Visual Analytics is used for the examples, but the tasks shown could be accomplished with other SAS tools. See bio provided above. Building the Easy Button: Automating SAS® Program Batch Runs Nancy Brucken, inVentiv Health It is often the case that multiple SAS programs need to be run in a specific order to update databases, or generate a particular set of reports, because of dependencies between those programs. However, requiring a single person, or even multiple people, to manually execute the programs in the required order can be an expensive proposition, and subject to human error. This presentation will look at several methods for automating this process, from a simple SAS driver program containing multiple %INCLUDE statements to a Windows batch file that submits code to one or more SAS Grid nodes. It will also demonstrate one approach to programmatically building such a batch file. Nancy Brucken has been a SAS programmer for more than 30 years, over 25 of which have been spent in the pharmaceutical industry. She is currently a member of the inVentiv Health Data Standards group, and co-leads the ADaM ADQRS sub-team. Nancy is a proud graduate of Marietta College, and a devout Ohio State Buckeyes fan. Fully Automated Updating of Arbitrarily Complex Excel Workbooks David Oesper, Lands’ End You can generate some very sophisticated Excel workbooks using ODS EXCEL and ODS TAGSETS.EXCELXP, but sometimes you’ll want to create your Excel workbook in Microsoft Excel—or someone else will provide it to you. I’ll show you how you can use SAS to dynamically update (and distribute) any existing Excel workbook with no manual intervention required. You’ll need only Base SAS 9.4 TS1M2 or later and SAS/ACCESS to PC Files to use this approach. Examples in both the Linux and Windows operating environments will be presented. David Oesper is a Quantitative Analyst II and the senior SAS programmer at Lands’ End. His previous employers were the Iowa Department of Transportation, Sul Ross State University, and the University of Texas Health Science Center in Houston. He has been writing SAS code since 1985. An avid observational astronomer, he is always on the lookout for ways that SAS can be utilized in the service of astronomy. If you need these OBS and these VARS, then drop IF, and keep WHERE Jay Iyengar, Data Systems Consultants LLC Reading data effectively in the DATA step requires knowing the implications of various methods, and DATA step mechanics; The Observation Loop, and the PDV. The impact is especially pronounced when working with large data sets. Individual techniques for subsetting data have varying levels of efficiency and implications for input/output time. Use of the WHERE statement/option to subset observations consumes less resources than the subsetting IF statement. Also, use of DROP and KEEP to select variables to include/exclude can be efficient depending on how they’re used. Jay Iyengar is Principal of Data Systems Consultants LLC. He is a SAS Certified Professional, and independent consultant. He is the co-leader of the Chicago SAS Users Group, WCSUG. He’s presented papers at SAS Global Forum (SGF), Midwest SAS Users Group (MWSUG), Northeast SAS Users Group (NESUG), and Southeast SAS Users Group (SESUG). His industry experience includes Healthcare Services, Clinical\Pharmaceutical, Public Health, and Educational testing. He has been using SAS since 1997. Ten SAS® Enterprise Guide® Tips for Experienced SAS Programmers Charu Shankar, SAS Institute Come check out 10 of the many reasons to seriously consider using SAS Enterprise Guide if you are a programmer. Discover how to enhance your productivity by using SAS Enterprise Guide (EG) to perform standard tasks.

The Ten Tips: 1. Learn about the powerful data explorer 2. Leverage the AUTOEXEC process flow 3. Visually split the screen to see more than one activity 4. Learn to submit code at session start up automatically- big use for library assignment 5. Utilize tools like format code to get messy code nicely indented 6. Hide wrapper code in the log 7. Learn to easily spot errors in the log 8. Slice and dice your data with the summary tables task 9. Check out the program analyzer tool to get a cool GUI out of your EG points and clicks 10. Use the built in AI in EG to help you write SAS code. See bio provided above. SAS Section 2 (LOWER LEVEL BREAKOUT ROOM)

Machine Learning? The Machine Already Knows. Software-Intelligent Application Development Provides Reliability, Reusability, Extendability, and Maintainability for Strong Smart Systems in an Ever-Changing World LeRoy Bessler, Bessler Consulting and Research Applications designed and built with Software Intelligence (SI) are robust, made of reusable parts, and easy and quick to extend or maintain. With dynamically auto-customizing code, such "living" applications go beyond change tolerance to change amenability, and further—to change implementation. They cope with ever-changing user or management preferences, run-dates, data dates, and data content. Common types of changes in report/graph content, format, and function are handled without reprogramming. If business rules do not change, an SI application can have eternal life. Whether you are a new or experienced SAS programmer, or an analytically oriented user who does not think of herself or himself as a programmer at all, this paper—which assumes no advanced SAS knowledge—shows you how to apply principles of Software-Intelligent Application Development, which are really programming-language-dependent, to make your use of SAS software safe, simple, and speedy. One of the tools for SI implementation with SAS software is SAS macro language. For users with no SAS macro language experience, the stand-up presentation includes a brief, but sufficient, introduction. Besides explaining the few, but powerful, principles of Software-Intelligent application development, the paper provides you with some widely applicable practical examples that you can put to use back at work. Dr. LeRoy Bessler has presented at software users conferences in the US, Canada, and Europe, on effective visual communication (using graphs, tables, web pages, maps, or color), highly formatted Excel reporting from SAS, his tools to assist SAS server administrators, users, and managers, and Software-Intelligent Application Development to maximize Reliability, Reusability, Maintainability, and Extendability. His 39 years of SAS experience includes application development and supporting users, servers, software, and data. He led the Wisconsin Illinois SAS Users Conference volunteer team for 22 years during the period 1989 to 2016. QS and ADQRS: Best Practices for Building Questionnaire, Rating, and Scale Analysis Data Sets Nancy Brucken, inVentive Health

Questionnaires, ratings and scales (QRS) are frequently used as primary and secondary analysis endpoints in clinical trials. The Submission Data Standards (SDS) QRS sub-team has compiled a considerable library of SDTM supplements defining standards for the collection and storage of QRS data. The ADaM ADQRS sub-team has been formed to develop addenda to these supplements, which will define standards for corresponding analysis data sets. This paper describes the current process for modeling questionnaire data in the SDTM QS domain and developing SDTM QS supplements, and represents the current thinking of the ADQRS sub-team regarding basic principles for building QRS analysis data sets.

See bio provided above.

Keys to Visual Data Insights: What I Discovered in 37 Years of Using Data Visualization Tools LeRoy Bessler, Bessler Consulting and Research This tutorial assumes no prior knowledge, and the principles of visual communication effectiveness are actually software-independent. Come to learn what you don't know that you don't know, but should know. Examples featured were developed with SAS/GRAPH® or SAS ODS Graphics. They are suitable for presentation graphics or management reporting.

See bio provided above.

SAS Analytics Section (FIRST FLOOR BREAKOUT ROOM) “Are We There Yet?” An Analysis of Cobb County Fire Department’s Response Times Gene Ray, Kennesaw State University The Cobb County Fire Department’s (CCFD) 8-minute emergency response time doubles the National Fire Protection Association’s (NFPA) 4-minute standard, measured at the 90th percentile of all emergencies. The county is home to more than 700,000 people, according the 2015 estimates provided by the US Census. The project aims to reduce CCFD’s response time by focusing on the travel times of their various emergency vehicles. Currently, there are 29 fire stations and 272 fire zones within Cobb County, with each fire station being responsible for a pre-defined set of fire zones. Two teams of graduate students considered two different aspects of the problem. The first team investigate whether fire zones and stations can be realigned to reduce travel times by analyzing historical response time data from September 2015 to August 2016. CCFD historical data reveal which fire station actually responded to each incident, as well as the related travel time. Google Maps is then used to check the response times from neighboring fire stations to determine if a different fire station could have responded more quickly to the same incident. The comparison between historical and Google travel times reveals the location and frequency of disagreement between the historical and Google recommended fire stations. Fire zones can then be reassigned to different fire stations to reduce future traveling times. The second team examined characteristics that are related to the slower than expected response times. The weather, time of day, day of week, the population density of the fire zone, as well as other characteristics were considered. A random forest was utilized to determine the most predictive variables followed by a logistic regression model to determine the impact. Results vary for each fire station, but they show that there is room for improvement in the way that CCFD currently responds to emergencies. A combination of software packages where used including SAS Studio on a high performance computing grid, Python, Google Maps Distance API, and Enterprise Guide. Herman “Gene” Ray is an Associate Professor of Statistics in the Department of Statistics and Analytics Sciences at Kennesaw State University. Before joining KSU, Herman was employed by Thomson Reuters where he served in multiple positions but the most recent appointment was to Research Scientist within the thought leadership organization. His academic credentials include a BS and MS in Mathematics from Middle Tennessee State University and a Ph.D. in Biostatistics from the University of Louisville. Be More Open: Use Cases for SAS® and Open Source Analytics Alex Ge, SAS Institute As a data scientist, you need analytical tools and algorithms, whether commercial or open source, and you have some favorites. But how do you decide when to use what? And how can you integrate their use to your maximum advantage? This presentation provides several best practices for deploying both SAS and open source analytical tools to increase productivity and efficiency in your enterprise ecosystem. See an example of a marketing analysis using SAS and R algorithms through SAS® Enterprise Miner™ to develop a predictive model and then operationalize that model for performance monitoring and in-database scoring. Also learn about using Python and SAS integration for developing predictive models from a Jupyter notebook environment. Seeing these cases will help you decide how to improve your analytics with similar integration of SAS and open source. Alex Ge is an associate systems engineer with the Enterprise Architecture team, under the Global Technology Practice at SAS. Alex helps customers leverage the optimal present and future-state architectures to drive the most value from analytics. In addition, he helps to support, drive, and evaluate emerging platforms and technologies. Some of Alex’s focus areas include cloud architecture, Internet of Things, and open source.

Prior to SAS, Alex received his B.S. in Applied Mathematics from Columbia University. He spent his summers doing biostatistics research on gene expression data for breast cancer patients. Alex is an avid coder and loves to work with languages such as Python and R. In his free time, Alex likes to read, travel, hike, and play ping pong. Optimizing the Analytical Data Life Cycle Tho Nguyen & Paul Segal, Teradata The analytical data lifecycle consists of 4 stages - data exploration, preparation, model development, and model deployment. Traditionally, these stages can consume 80% of the time and resources within your organization. With innovative techniques such as in-database and in-memory processing, managing data and analytics can be streamlined and increase performance, economics, and governance. This session explores how you can optimize the analytical data life cycle with best practices and tips using SAS and Teradata. See bio provided above. Insurance Claim Analytics—A Case Study on a Worker’s Compensation (WC) Litigation Propensity Predictive Model Mei Najim, Hallagher Bassett Claim Analytics has been evolving in insurance industry for the past two decades. My paper is organized to be in four parts as follows:

1. This presentation will first provide an overview of Claim Analytics. 2. Then, a common high-level claim analytics technical process with large data sets will be introduced. The steps of

this process include data acquisition, data preparation, variable creation, variable selection, model building (a.k.a.: model fitting), model validation, and model testing, etc.

3. A Case Study: Over the past couple of decades, in the propensity & casualty insurance industry, around 20% closed claims have settled with litigation propensity representing 70-80% of total dollars paid. Apparently, the litigation is one of the main claim severity drivers. In this case study, we are going to introducing Worker’s Compensation (WC) Litigation Propensity Predictive Model at Gallagher Bassett which is designed to score the open WC claims to predict the open claims litigation propensity in the future. The data including WC Book of Business over a few thousands of clients’ data with millions of claims and thousands of variables has been used and explored to build the model. Multiple cutting-edge statistical and machine learning techniques (GLM Logistic Regression, Decision Tree, Neural Network, Gradient Boosting, etc.) along with WC business knowledge are utilized to discover and derive complex trends and patterns across the WC book of business data to build the model.

4. Conclusion

Mrs. Najim has over 10 years of analytics experience in both personal and commercial line in the areas of predictive analytics, claim analytics, catastrophe risk management analytics, and actuarial reserving and pricing analytics in the Property & Casualty insurance industry. She has presented in industry conferences to share her papers and expertise in predictive analytics. She holds a Bachelor of Science in Actuarial Science from Hunan University and two separate Master of Science degrees, in Applied Mathematics and in Statistics from Washington State University. Mei is a member of the American Statistical Association. Predicting Acute Impatient Length of Stay: Approaches for Retrospective Adjustment and Prospective Targeting Dennis Low, Presence Health In the current era of health care reform and cost-of-care reduction, effectively managing length of stay (LOS) and reducing unnecessary inpatient days for patients is of increasing importance. Predictive models can assist health care delivery systems answer several key questions: Are there operational inefficiencies extending LOS beyond that which is clinically necessary? How can patients be prioritized for LOS intervention and management as close to admission as

possible? This session will illustrate how analysts at a large hospital system used SAS and multivariate regression techniques to begin answering these questions. Emphasis will be placed on technical model development and validation, as well as a method used for integrating SAS and R for additional data mining. Dennis Low is a Lead Population Health Analytics with Presence Health, a large non-profit health system based in Chicago, Illinois. Dennis is a certified Base and Advanced SAS programmer with significant analytic and programming experience in both the marketing and health care fields. A Variety of Mixed Models: Linear, Generalized Linear, and Nonlinear David Dickey, North Carolina State University The MIXED procedure in SAS correctly handles linear models that have multiple sources of random effects such as random town to town, store to store, and aisle to aisle variation in sales. Associated fixed effects might be product price, color of packaging and amount spent on advertising. The talk begins with a checklist for deciding when to treat effects as random versus fixed and follows with a series of examples. When the response variable is not normal, for example with a binary response, additional complexities arise. Models with binary, Poisson and other non normal responses are often analyzed by assuming that some transformation of the expected value of Y follows a linear model, the logistic transformation being a prime example. When this transformation, generally referred to as a "link function," results in a linear model with fixed and random effects, we are in the generalized linear mixed model setting. It may be that a model cannot be linearized by a transformation, thus making it a nonnlinear model. If random effects are involved the model is referred to as a nonlinear mixed model. With a minimal amount of theory and an emphasis on examples, these types of models will be illustrated and compared. See bio provided above. Nothing to SNF At: Evaluating an intervention to reduce skilled nursing home (SNF) length of stay Andrea Frazier, Presence Health Length of stay (LOS) in skilled nursing facilities (SNF, pronounced “sniff”) is a driver of high health care costs, particularly for Medicare patients. This study used survival analysis techniques to examine the effect of a simple intervention (educating providers and case managers on expected LOS for each patient) on SNF LOS. We’ll also discuss techniques used to abate particular data collection challenges in this study. Andrea Frazier is the System Manager for Population Health Analytics at Presence Health in Chicago. A SAS user since 2011, she's proud to let her PROC FREQ flag fly at WIILSU and MWSUG.

Proceedings-Only Content

Navigating the Data Universe Aboard the Starship SAS® Enterprise Guide® Jay Iyengar, Data Systems Consultants LLC

If you DO have access to the new ODS Excel destination (these two papers also include use of ODS ExcelXP):

New for SAS® 9.4: A Technique for Including Text and Graphics in Your Microsoft Excel Workbooks, Part 1 Vincent DelGobbo, SAS Institute

New for SAS® 9.4: Including Text and Graphics in Your Microsoft Excel Workbooks, Part 2 Vincent DelGobbo, SAS Institute In case you don’t have access to the new ODS EXCEL destination:

SAS to Excel - OLD ODS Methods & DDE Comparison - Slides, Code, Reference Doc - Zip File - 7.14 MB LeRoy Bessler, Bessler Consulting and Research

SAS with Excel - DDE Method Only - Paper, Slides, Code, Output, ToolKit - Zip File - 4.10 MB LeRoy Bessler, Bessler Consulting and Research

Not included in the Proceedings and Tools Package

As a supplement to Vince DelGobbo’s papers, see also: “The New SAS ODS Excel Destination: A User Review and Demonstration” By LeRoy Bessler The New SAS ODS Excel Destination: A User Review and Demonstration

If you want to create an Excel Pivot Table from SAS, a very good reference, besides Chevell Parker’s paper, is: “The Armchair Quarterback: Writing SAS Code for the Perfect Pivot (Table, That Is)” By Peter Fernwood and Louanna Kong http://support.sas.com/resources/papers/proceedings12/146-2012.pdf

Conference Team

Craig Wildeman is in charge of conference operations. He is a Senior Systems Project Leader in the Quality Department of the Cast Iron Division at Kohler Co. Andrea Frazier (see bio above) is SAS Analytics Program Chair and SAS Analytics Session Coordinator. David Bruckner, Systems Project Leader at Kohler Co., assists with operations, registration and sponsor recruitment. Laura MacBride handles all conference communications and publications, and serves as SAS Session Coordinator. She is an Assistant Director of the Office of Institutional Research and Analysis at Marquette University. LeRoy Bessler (see bio above) served as SAS Program Chair. 

Wisconsin Illinois SAS Users Conference Agenda – June 28, 2017

Woman’s Club of Wisconsin, 813 E. Kilbourn Ave., Milwaukee, WI 53202, 414-276-5170

8:30 Registration Check-In

9:00 Welcome (MAIN ROOM - SECOND FLOOR) Craig Wildeman, Conference Coordinator

SAS SECTION (MAIN ROOM - SECOND FLOOR) 9:10 The Modern Analytical Landscape

Paul Segal, Teradata

10:05 More than a Pretty Face: Worksheets and Pivot Tables That Look Great and Answer Difficult Questions (continued) Chevell Parker, SAS Institute

10:25 Break

10:45 More than a Pretty Face (continuation) Chevell Parker, SAS Institute

11:10 Stacking Up - Horizontal or Vertical with PROC SQL or DATA Step Charu Shankar, SAS Institute

12:00 Lunch

1:00 Secrets I Learned from Reviewing 500 Dashboards Tricia Aanderud, Zencos Consulting

1:55 Building the Easy Button: Automating SAS® Program Batch Runs Nancy Brucken, inVentiv Health

2:50 Break

3:00 Ten SAS® Enterprise Guide® Tips for Experienced SAS Programmers Charu Shankar, SAS Institute

3:55 Fully Automated Updating of Arbitrarily Complex Excel Workbooks David Oesper, Lands’ End

4:20 If you need these OBS and these VARS, then drop IF, and keep WHERE Jay Iyengar, Data Systems Consultants LLC

SAS ANALYTICS SECTION ( FIRST-FLOOR BREAKOUT ROOM) 9:10 “Are We There Yet?” An Analysis of Cobb County Fire Department’s Response Times

Gene Ray, Kennesaw State University

10:05 Be More Open: Use Cases for SAS® and Open Source Analytics(continued) Alex Ge, SAS Institute

10:25 Break

10:45 Be More Open: Use Cases for SAS® and Open Source Analytics (continuation) Alex Ge, SAS Institute

11:10 Optimizing the Analytical Data Life Cycle Tho Nguyen, Teradata Paul Segal, Teradata

12:00 Lunch

1:00 Insurance Claim Analytics--A Case Study on a Worker’s Compensation (WC) Litigation Propensity Predictive Model Mei Najim, Gallagher Bassett

1:55 Predicting Acute Inpatient Length of Stay: Approaches for Retrospective Adjustment and Prospective Targeting Dennis Low, Presence Health

2:50 Break

3:00 A Variety of Mixed Models: Linear, Generalized Linear, and Nonlinear David A. Dickey, North Carolina State University

3:55 Nothing to SNF At: Evaluating an intervention to reduce skilled nursing home (SNF) length of stay Andrea Frazier, Presence Health

4:45 Return to Main Room

More Presentations on Next Page

SAS BASIC TUTORIALS ( LOWER-LEVEL BREAKOUT ROOM – CAPACITY 32 – NO STANDING ROOM – EACH TOPIC TWICE)

10:05 Machine Learning? The Machine Already Knows. Software-Intelligent Application Development Provides Reliability, Reusability, Extendability, and Maintainability for Strong Smart Systems in an Ever-Changing World (continued) LeRoy Bessler, Bessler Consulting and Research

10:25 Break

10:45 Machine Learning? The Machine Already Knows. (continuation) LeRoy Bessler, Bessler Consulting and Research

11:10 QS and ADQRS: Best Practices for Building Questionnaire, Rating, and Scale Analysis Data Sets Nancy Brucken, inVentiv Health

12:00 Lunch

1:00 Keys to Visual Data Insights: What I Discovered in 37 Years of Using Data Visualization Tools LeRoy Bessler, Bessler Consulting and Research

1:55 Machine Learning? The Machine Already Knows. Software-Intelligent Application Development Provides Reliability, Reusability, Extendability, and Maintainability for Strong Smart Systems in an Ever-Changing World LeRoy Bessler, Bessler Consulting and Research

2:50 Break

3:00 QS and ADQRS: Best Practices for Building Questionnaire, Rating, and Scale Analysis Data Sets Nancy Brucken, inVentiv Health

3:55 Keys to Visual Data Insights: What I Discovered in 37 Years of Using Data Visualization Tools LeRoy Bessler, Bessler Consulting and Research

4:45 Return to Main Room SAS SECTION (MAIN ROOM)

4:45 Conference Closing

5:00 Post-Conference Networking Social All attendees, volunteers, and speakers who can stay SAS DEMO ROOM (ACROSS FROM THE MAIN ROOM) 1:00-1:50 Melissa Perez, SAS Institute, Marketing Manager, SAS Users Group Programs

SAS resources to help you thrive as a SAS user Learn about the many SAS programs and resources available to users, including Ask The Expert webinars, user communities and more.

1:55-2:45 Paul Segal, Teradata

The Benefits of Integrating SAS Analytics with Big Data Integrating and optimizing data and analytics can be a daunting process. Come see a demonstration and hear best practices using SAS/ACCESS, SAS formats, data quality, DS2, model development, model scoring, Hadoop, and Visual Analytics all integrated with the data warehouse to improve performance, economics and governance.

2:50 Break 3:00-3:50 Ken Pikulik, Teradata

Self-Service Data Management for Analytics Users across the Enterprise With the proliferation of analytics expanding across every function of the enterprise, the need for broader access to data by experienced data scientists and non-technical users to produce reports and do discovery is growing exponentially. The unintended consequence of this trend is a bottleneck within IT to deliver the necessary data while still maintaining the necessary governance and data security standards required to safeguard this critical corporate asset. This presentation illustrates how organizations are solving this challenge and enabling users to both access larger quantities of existing data and add new data to their own models without negatively impacting the quality, security, or cost to store that data. It also highlights some of the cost and performance benefits achieved by enabling self-service data management and SAS analytics.

3:55-4:45 Nancy Brucken, inVentiv Health

CDISC Help Desk Provide assistance with CDISC implementation issues and questions.

4:45 Return to Main Room

Map

Woman’s Club of Wisconsin 813 East Kilbourn Avenue, Milwaukee, WI 53202 Telephone: 414-276-5170

The Woman's Club of Wisconsin is located at 813 East Kilbourn Avenue, at the intersection of East Kilbourn Avenue and North Cass Street. The Club entrance has a discreet marquee. (Handicapped entry is also available.) Parking Valet Parking is available at no cost by pulling up to the main entrance to the Club. Attendees can self-park in the Club lot which is one block South, at the intersection of East Wells Street and North Cass Street, but be sure to check in with the Valet on duty.

NOTE: There is notable road construction in the area, including along 794. Please consult http://www.511wi.gov/Web/ for information about work zones and detours.

Some Overnight Accommodation Alternatives Near the Conference Site:

County Clare Irish Inn & Pub 414-272-5273 http://countyclare-inn.com/

Park East Hotel 800-328-7275 http://www.parkeasthotel.com/

The Pfister Hotel 800-558-8222 http://www.thepfisterhotel.com/

Hyatt Regency 414-276-1234 or 888-591-1234 http://milwaukee.hyatt.com/hyatt/hotels/

Intercontinental Milwaukee 414-935-5943 or 800-954-4667 http://www.intercontinentalmilwaukee.com/

The Astor Hotel 800-558-0200 http://theastorhotel.com/

Registration & Payment Information and Optional Mailing List Form

SAS Users Conference – June 28, 2017 – Registration in Advance Only

Our online registration system allows us to accept credit cards or checks. If you pay by credit card, you will get a confirmation email and receipt when you complete your registration and successful payment has been received. If you prefer to pay by check, please fill in the online system, but select check payment and send a check to the address stated in the online system. You will receive a confirmation email when your check has been received. Registration Fee – $55

Full-Time Students – You can register for $25 if you are enrolled full-time at a degree-granting institution. You just need to email proof (student id) of your enrollment to registrar@wiilsu.org, and we will email you instructions on how to register at the $25 rate.

If payment is not received by June 23, you will not be allowed to attend the conference.

Cancellations will not be accepted unless received before June 16. To cancel a registration, please email registrar@wiilsu.org stating that you wish to cancel, and we will issue the credit. If you pay by credit card and need to cancel, your credit card will be refunded less a $10.00 processing fee (i.e., $55.00 charge less $10.00 fee results in a $45.00 refund to your card). To avoid the processing fee, you can substitute someone else for your registration by going back into the registration system and editing your registration by changing your name to the new registrant.

***** Online Registration site: www.regonline.com/wiilsu2017 *****

For questions about registration, contact our Registrar David Bruckner at 920-457-4441 or registrar@wiilsu.org.

NOTE: Order of speakers on the agenda might need to change without notice. ______________________________________________________________________________________________________________________________________

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