CONFERENCE OVERVIEW
 

 

Pre/Post Conference Classes
Art Carpenter
Carpenter
Art Carpenter’s publications list includes four books, and numerous papers and posters presented at SAS Global Forum, SUGI, and other user group conferences. Art has been using SAS® since 1977 and has served in various leadership positions in local, regional, national, and international user groups. He is a SAS Certified Advanced ProgrammerTM and through California Occidental Consultants he teaches SAS courses and provides contract SAS programming support nationwide.
Advanced Reporting and Analysis Techniques
for the SAS® Power User: It's Not Just About The PROCs!
There are literally hundreds of techniques used on a daily basis by the users of SAS® software as they perform analyses and generate reports. Although often obscure, most of these techniques are relatively easy to learn and generally do not require specialized training before they can be implemented. Unfortunately a majority of these techniques are used by only a very small minority of the analysts and programmers. They are not used more frequently, because a majority of SAS users have simply not been exposed to them. Left to ourselves it is often very difficult to ‘discover’ the intricacies of these techniques and then to sift through them for the nuggets that have immediate value. This one day course presents a series of those nuggets. It covers a broad range of SAS topics that have proven to be useful to the intermediate and advanced SAS programmer who is involved with the analysis and reporting of data. The intended audience is expected to have a firm grounding in Base SAS. For most of the covered topics, the course will introduce useful techniques and options, but will not ‘teach the procedure’. No matter how experienced we are, no matter how well we know a procedure or a technique, there is still more that we do not yet know. The course includes options and techniques associated with:
New, powerful, and little used options in MEANS/SUMMARY
Reporting procedures including TABLULATE and REPORT
Understanding more about the REPORT compute block
In the DATA step (functions, options, statements)
Working with data
Taking full advantage of formats
Interfacing with the Macro Language
Output Delivery System, ODS, extras
Operating System Interfaces and how you can take advantage of them
Advanced Table look-up techniques
Importing and exporting data
. . . . much, much more
Advanced Techniques in the SAS® Macro Language
This one day course is designed for students with a good understanding of the DATA and PROC steps and who already understand the basic structure and syntax of the SAS Macro Language. The course will start with a short review of the macro basics and quickly move onto topics selected to improve your macro language expertise. Several key macro functions will be introduced, explained and demonstrated. Course topics include:
Macro Language Review
Macro Functions, Using and Creating
Writing Dynamic Code
Controlling Your Environment
Working With SAS Data Sets
Using SAS Macro Libraries
Miscellaneous Macro Topics
Learn how the macro language thinks as you use it to write your programs.
Building Dynamic Programs and Applications Using the SAS® Macro Language
This seminar shows you how to take advantage of SAS Macro Language capabilities that enable you to write dynamic programs and applications. By mastering the concepts and techniques presented in this class your programs will become free of hard-coded data dependencies, thus eliminating the need to re-write the code every time a data set name, variable name, or other data attribute, changes. Let “them” change the project's specifications as often as “they” want…your code is ready! The dynamic programming techniques that you will learn about during this seminar:

Are flexible and are easily adaptable to changing data structures, data table names, and variable (field) attributes
Reduce maintenance requirements by removing data dependencies from within the programs
Provide significant resource savings during program/application development cycles
Gives the end-user extensive control over program execution by using tables such as SAS data dictionaries, SAS data sets, and Excel tables
Reduce program validation efforts by providing reusable and generalized code that can be applied to many different applications
Establish controlled data environments, thus insuring data integrity throughout your organization

This course makes extensive use of example macros that have been gathered from real world applications, and it concentrates on the techniques necessary to make effective use of these tools.
Wei Cheng
Cheng

Wei Cheng is presently an executive director of Biometrics Department at Isis Pharmaceuticals, Inc. in Carlsbad, California. He is serving on San Diego’s SAS Users Group (SANDS) as president and has served on WUSS Executive Committee as user group liaison. He has contributed and been invited to SUGI/SGF, WUSS, PharmaSUG, SANDS, and NVSUG as a paper presenter, section chair, code doctor, judge, or session coordinator.
ODS Graphics for Clinical Research
This half-day course presents the techniques to create commonly requested clinical graphs using ODS Statistical Graphics. Some of the most commonly used statistical methods, such as ANOVA, Analysis of Covariance, Linear Regression, The Chi-Square Test, Logistic Regression, and The Log-Rank Test, will be discussed. The course provides recommendations and examples on which procedure / language / application is suitable for different kinds of clinical graphs. The plot names we plan to cover are:
• Forest /Odds Ratio Plot
• Product-Limit Survival Plot
• Most Frequent Adverse Events by Relative Risk
• Adverse Events Timeline
• Distribution of Maximum LFT.
• Time to First Adverse Event
• Hazard Function of Adverse events
• Box Plot of QTc Change from Baseline
• Panel of LFT Shift from Baseline
• LFT panel by Test and Visit
• Median/Mean of Lipid Profile over Time
• ASAT by Time and Treatment
• Matrix Display of Maximum LFT
• Patient Labs by Test over Time
• Regression Fit Plot / Fit Diagnostics
• Distribution Box Plot for ANOVA/ANCOVA
• Analysis of Covariance Plot
• Bar Chart of Mean Change
Ron Cody
Cody

National instructor for SAS and author of 8 books on SAS.

Data Cleaning Techniques
An introduction to data cleaning for both character and numeric values. In addition to demonstrating ways of identifying data errors, this class also suggests ways of correcting errors. Finally, there is a brief introduction to SAS Integrity Constraints, a relatively new facility that enables you to store rules (such as Gender must be 'M' or 'F' or Pulse_Rate must be less than 110) in the data descriptor portion of the data set.
AnnMaria De Mars
deMars

Dr. De Mars has been programming with SAS for 29 years, an amazing feat considering that she claims to only be 32 years old. Her experience with SAS categorical data analysis actually began with PROC CATMOD. Although that procedure is now obsolete, she is not, having progressed to PROC LOGISTIC, PROC SURVEYLOGISTIC and more. In a long and strange career, she has been senior statistical consultant at the University of Southern California, statistician on national surveys, industrial engineer monitoring factor loading and production targets and professor of statistics and research methods.
Analysis of Categorical Data: For When Your Data Really Do Fit in Neat Little Boxes
What do birth, death, high school dropout, failing a course, losing a sale, engineering majors and being fired all have in common? If you answered, “Things my mother warned me about”, you need therapy. The correct answer is all of these are categorical variables, where data pretty much DO fit in neat little boxes. You were either admitted to college or you weren't. The customer either bought insurance from you or opened the door and let his pit bull chase you down the street. The voter checked the box for the Democratic, Republican or Independent candidate. You get the idea. This course begins with a one-hour discussion of simple statistics that are part of the UNIVARIATE and FREQ procedures. The next two hours focus on a straightforward approach to PROC LOGISTIC. By the end of this course, you will be able to produce SAS analyses of categorical data and provide clear interpretations of the size of relationships, identify which variables matter in predicting outcomes, choose from competing models and whip out impressive graphic displays of your data and relationships. If you think McNemar and Akaike were on the same team in the last World Cup, this course is for you!
Sunil Gupta
Gupta

Sunil is a best selling SAS author and global corporate trainer. Currently, he is the principal consultant at Gupta Programming. Most recently, he released an e-Guide on Quick Results with Proc SQL. He has been using SAS® software for over 19 years and is a SAS Base Certified Professional. He is also the author of Quick Results with the Output Delivery System, Data Management and Reporting Made Easy with SAS Learning Edition 2.0, and Sharpening Your SAS Skills. Most recently, he is teaching his latest popular course, Best Practices in SAS Statistical Programming in Regulatory Submission.
Automating Tasks using SAS Macro Programming
Ready to get the most out of SAS macros with a simple and logical approach to SAS macro programming? This unique course shows how the macro language serves as a dynamic character editor for SAS programs. Course examples show how macros can be used to standardize, automate, communicate, customize and create SAS code. From simple to complex real-word examples, we will break down the macro language into easily digestible chunks. Using macros saves time with data-driven processing and dynamic code generation. Attend with your challenging macro questions. References are made to my top 25 ‘must read’ SAS papers. For an additional fee, the companion e-guide is a great reference for searching, cutting and pasting model SAS examples.
Maximizing Productivity and Efficiency using Proc SQL
Do you need to learn effective techniques for querying terabytes of data? Whether you are new or an experienced Proc SQL programmer, you can expect to quickly take advantage of Proc SQL to organize, group, sort, summarize and distill massive amount of data into reliable information. This unique course explores syntax options for each common program task including selecting, creating, joining and summarizing data. By applying the step-by-step examples provided throughout the course, you too can master Proc SQL in your daily programming environment. For an additional fee, the companion e-guide is a great reference for searching, cutting and pasting model SAS examples.
Gerry Hobbs
Hobbs
Gerry Hobbs is on the faculty in the Department of Statistics at West Virginia University as well as an Adjunct Associate Professor of Community Medicine within the WVU School of Medicine. He consults widely and frequently with students, faculty and, staff at the School of Medicine and has well over 100 publications relating to medical investigations in many different areas as well as publications in other disciplines. He has extensive experience with both observational data and with experimental investigations. He is a co-principal investigator, co-investigator and, consultant on several funded scientific and clinical research projects. Dr. Hobbs received both his MS and Ph.D. degrees at Kansas State University and teaches both Statistics and Biostatistics courses. Dr. Hobbs has served as a consultant to the SAS Institute for over twenty-five years. In that capacity he has taught numerous different SAS and JMP training courses that include courses that emphasize data management and those which are purely statistical in content. He has also authored and co-authored training materials that have been used by the statistics group, housed in the SAS Education Division. For over twenty years he has served on the Executive Board of the organization (SGUG) which puts on the annual SAS User's Group Conference and he served as Conference Co-Chair in 1988 when the conference was held in Orlando, Florida.
Methods of Survival Analysis in SAS
Survival analysis and its functional equivalent, reliability analysis, are widely used in many fields of work. Most notably, the methods have been widely used in medical research and quality assurance for manufacturing for a long time. More recently they have been applied to software engineering and warranty coverage. The fundamental idea stems from an interest in knowing how long an entity will continue to perform. Of course parts and systems eventually fail, but so do people. In a medical sense we failure may mean death but it may also mean progression to the next stage of disease or a relapse. There are a couple things that make these methods different than standard analyses. The first is that the data usually has an elongated tail – usually the right tail. The second is that some observations are censored or incomplete, e.g., the part has lasted for seven months and we don’t have time to wait for it to fail. The simple descriptive “statistic” in this area relates time (to event) to probabilities of the event in the form of a function that is most often estimated by a nonparametric device known as the Kaplan-Meier curve. Going beyond that, inferential methods are used to compare groups. Wilcoxon and Log-Rank tests are popular in that area. Several methods are available that allow us to study the effect of a factor, either categorical or continuous, while we control for one or more confounders, also categorical or continuous. In this presentation we will explore the ways in which SAS Software® empowers the user to perform these analyses.
Kirk Paul Lafler
Lafler

Kirk Paul Lafler is consultant and founder of Software Intelligence Corporation and has been programming in SAS since 1979. He is a SAS Certified Professional, SAS Institute Alliance Member (1996 – 2002), and provider of IT consulting services and training to SAS users around the world. As an author of four books including PROC SQL: Beyond the Basics Using SAS (SAS Institute. 2004), he has written nearly five hundred peer-reviewed papers, been an Invited speaker at more than three hundred SAS International, regional, local, and special-interest user group conferences/meetings, and is the recipient of 17 “Best” contributed paper awards.
Advanced SAS® Programming Techniques
SAS users who have acquired basic skills presented in a SAS Software Basics course and want to expand their knowledge in the DATA step as a programming language will want to attend this Advanced SAS Programming Techniques course. Attendees learn complex programming topics and techniques in the areas of data access, data manipulation, data management, data presentation, and more. Topics include DATA step programming techniques including reading and writing data and output from and to MS-Excel; creating and using user-defined formats; coding and using arrays, loops, and ranges; performing lookup operations with DATA step hash objects; using operators and modifiers to search data; reshaping columns and rows of data with the TRANSPOSE procedure and DATA step approaches; techniques on controlling and improving I/O, CPU and memory operations; specialized ODS techniques for improved output, and testing and debugging techniques.
PROC SQL Programming: The Basics and Beyond
This course teaches SAS users core concepts and features about accessing data stored in relational database tables. Attendees learn how to use PROC SQL to access data stored in relational tables; accomplish tasks including retrieving, subsetting, ordering, and grouping data; construct tables and “virtual” tables known as views; define, access and manipulate data; produce “quality” looking output with PROC SQL options and Output Delivery System (ODS); use summary (statistical) functions to aggregate data; understand the difference between DATA step merges and joins; create complex queries using inner and outer joins; construct logic scenarios with case expressions; interface PROC SQL with the macro facility; understand index rules and strategies; and apply query performance techniques.
SAS® Software Essentials Using Enterprise Guide®
Hands-On Class
You will have access to a PC for  hands-on experience with SAS Enterprise Guide. This will give you a highly engaging learning experience to increase your retention of the material. 

This course is designed to teach the basics of the SAS® System emphasizing the power of the graphical user interface (GUI) features found in Enterprise Guide. It serves as an excellent foundation of knowledge without the need to learn complicated programming techniques. Acquire a working knowledge of the GUI front-end interface along with the built-in wizards to perform basic reporting and analytical tasks; access a variety of input data types; subset, order, group, summarize, and transpose data; join two or more tables together; export results to Excel, Word, HTML, and PDF; and organize, view and manage projects visually. Attendees learn how to use Enterprise Guide’s GUI front-end to construct and execute programs using the SAS System; read and process text, ASCII, comma- and tab-delimited, and Excel data; format existing variables and create new calculated variables; create temporary and permanent SAS data sets; subset, order and group data; create detail, summary, tabular and statistical reports; perform conditional programming logic; manipulate numeric and character data; combine data using concatenation, match-merging, and joining techniques; and manage SAS data sets and projects for added power and flexibility.
R. Scott Leslie
Leslie

R. Scott Leslie is a Health Outcomes Researcher for MedImpact Healthcare Systems, with 12 years of SAS experience in the pharmacy benefits and medical management field. Scott holds a Master of Public Health degree in Epidemiology and Biostatistics from Loma Linda University. He is an author of a SAS book chapter and has presented at local, regional and international SAS conferences.
Propensity Scoring Methods and Uses
Observational research provides the ability to assess effects of treatments in large populations. Although observational studies can answer many relevant questions in "real world" conditions, studies that lack randomization of subjects into treatment groups must address confounding and treatment selection bias to properly estimate the effect of treatment as non randomized groups usually differ on observed and unobserved characteristics. That is, the observed treatment effect may be due to the treatment itself or due to the differential selection into treatment groups from non-randomization. Propensity score methods are often used to reduce confounding and treatment selection bias by mimicking randomization. Conventional regression adjustment, matching, and stratification using propensity scores are widely used techniques to adjust for treatment selection bias by balancing groups, usually a treatment group and non treatment group, on observed characteristics. Included in this half-day training class is a description of these propensity score methods, an explanation of the advantages and disadvantages of each method, and applications of methods by showing examples. This class will also review published SAS(r) papers on this topic. This class is intended for intermediate level statisticians and SAS(r) programmers.
Sanjay Matange
Matange

Sanjay Matange is a Senior Development Manager in the Data Visualization Division at SAS.  Sanjay is responsible for the development and support of the ODS Graphics system, including the Graph Template Language (GTL), Statistical Graphics (SG) procedures ODS Graphics Designer and other related graphics applications.  Sanjay’s team is also responsible for development of interactive visualization components used in many SAS Products and Solutions such as Enterprise Miner, Forecast Studio, Risk Analysis, Warranty Analysis and many more.  Sanjay has been involved in the development of graphics software at SAS for over 20 years.  Sanjay has graduate degrees in Computer Science from NC State University.
ODS Graphics for Clinical Research
This half-day course presents the techniques to create commonly requested clinical graphs using ODS Statistical Graphics. Some of the most commonly used statistical methods, such as ANOVA, Analysis of Covariance, Linear Regression, The Chi-Square Test, Logistic Regression, and The Log-Rank Test, will be discussed. The course provides recommendations and examples on which procedure / language / application is suitable for different kinds of clinical graphs. The plot names we plan to cover are:
• Forest /Odds Ratio Plot
• Product-Limit Survival Plot
• Most Frequent Adverse Events by Relative Risk
• Adverse Events Timeline
• Distribution of Maximum LFT.
• Time to First Adverse Event
• Hazard Function of Adverse events
• Box Plot of QTc Change from Baseline
• Panel of LFT Shift from Baseline
• LFT panel by Test and Visit
• Median/Mean of Lipid Profile over Time
• ASAT by Time and Treatment
• Matrix Display of Maximum LFT
• Patient Labs by Test over Time
• Regression Fit Plot / Fit Diagnostics
• Distribution Box Plot for ANOVA/ANCOVA
• Analysis of Covariance Plot
• Bar Chart of Mean Change
David Pasta
Pasta

Currently VP of Statistical and Strategic Analysis at ICON Late Phase and Outcomes research, David Pasta has been using SAS since 1977 for a wide array of statistical analysis and data management tasks. He has frequently presented contributed and invited papers at SGF and WUSS.
Practicalities of Building and Testing Models with Continuous and/or Categorical Predictors
Analysts trying to understand the contribution of predictor variables are faced with many choices. Treat them as continuous or categorical? Fit piecewise models? Which interactions to include? What parameterization to use? Also, testing the desired hypotheses often involves complicated coding of ESTIMATE and CONTRAST statements. This course will provide practical guidance on all these points.
Chris Riddiough
Riddiough

Christine Riddiough, Technical Training Specialist 5, Rockville Regional Office, has a BA in Astronomy from Carleton College, and an MS in Astrophysics from Northwestern University. She started teaching SAS for the Education Division in 1991. Before that she had taught science, math and computer programming at colleges in the Washington, DC and Chicago areas. She teaches courses in SAS programming, statistics, grid computing and BI content development and maintains several SAS applications for the SAS Education Division.
Making the Most of Your Statistical Analysis
with SAS® Programming and the Output Delivery System
This seminar teaches students how to use the SAS Output Delivery System to customize the output from their statistical analyses, and how to generate reports based on that output for consumption by business users and other information consumers in their organization. It starts with a review of ODS basics, focusing on the data destination and using the ODS Output statement. The ODS Trace as a mechanism for determining what output objects are created will be explored. We will then examine some examples of how we can easily capture statistical information and report on it in a concise way. Questions addressed will include:

• Which correlations are statistically significant? – use ODS to output correlation statistics to a data set and then use a data step to eliminate those that are non-significant.
• How can I get predicted values based on parameter estimates? – use ODS to capture parameter estimates and the use them to calculate predicted values.
• How can I compare a series of models from linear regression based on several statistics? – use ODS to capture the statistics and use data step programming and macro to produce a comparison table.
Cynthia Zender
Zender

Cynthia Zender has been with SAS since 1996 as an instructor and course developer. She currently serves as the Curriculum Manager for the Report Writing and Output Delivery System curriculum. She has over 20 years' experience programming and reporting with SAS in a number of different industries such as Education, Public Utility, Telecom, Litigation Support, and Research Support (clinical studies and survey analysis). Cynthia recently finished a book entitled, "Output Delivery System: The Basics and Beyond", co-authored with Lauren Haworth and Michele Burlew.
SG Procedures and ODS GRAPHICS for the Non-Statistician
Do you need to produce simple series plots and bar charts and maybe the occasional box plot? Do you want to generate "small multiple" or paneled charts, as recommended by Edward Tufte?  This seminar illustrates how to use the new SG procedures, in particular, SGPLOT and SGPANEL to produce simple plots and bar charts. Primary SGPLOT types covered will be VBAR, HBAR, SERIES, VBOX and HBOX. Once you know the basics of the SGPLOT statements to produce single graphs, learning SGPANEL to created paneled output will be a cinch. Through concrete examples, this seminar will guide you through the basics of producing and customizing simple graphs using the new SG procedures. (Note: The SGSCATTER and SGRENDER procedures are topics that are not covered in this seminar. ) In addition, use of the ODS GRAPHICS statement for setting or changing graph options will be covered.