The SQL procedure has a number of powerful and elegant language features for SQL users. This hands-on workshop emphasizes highly valuable and widely usable advanced programming techniques that will help users of Base SAS^® harness the power of PROC SQL. Topics include using PROC SQL to identify FIRST.row, LAST.row, and Between.rows in BY-group processing; constructing and searching the contents of a value-list macro variable for a specific value; data validation operations using various integrity constraints; data summary operations to process down rows and across columns; and using the MSGLEVEL= system option and _METHOD SQL option to capture vital processing and the algorithm selected and used by the optimizer when processing a query.

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There are many good validation tools for Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) such as Pinnacle 21. However, the power and customizability of SAS^® provide an effective tool for validating SDTM data sets used in clinical trials FDA submissions. This paper presents three distinct methods of using SAS to validate the transformation from Electronic Data Capture (EDC) data into CDISC SDTM format. This includes: duplicate programming, an independent SAS program used to transform EDC data with PROC COMPARE; rules checker, a SAS program to verify a specific SDTM or regulatory rules applied to SDTM SAS data sets; and transformation validation, a SAS macro used to compare EDC data and SDTM using PROC FREQ to identify outliers. The three examples illustrate the diverse approaches to applying SAS programs to catch errors in data standard compliance or identify inconsistencies that would otherwise be missed by other general purpose utilities. The stakes are high when preparing for an FDA submission. Catching errors in SDTM during validation prior to a submission can mean the difference between success or failure for a drug or medical device.

It's essential that SAS^® users enhance their skills to implement best-practice programming techniques when using Base SAS^® software. This presentation illustrates core concepts with examples to ensure that code is readable, clearly written, understandable, structured, portable, and maintainable. Attendees learn how to apply good programming techniques including implementing naming conventions for data sets, variables, programs, and libraries; code appearance and structure using modular design, logic scenarios, controlled loops, subroutines and embedded control flow; code compatibility and portability across applications and operating platforms; developing readable code and program documentation; applying statements, options, and definitions to achieve the greatest advantage in the program environment; and implementing program generality into code to enable its continued operation with little or no modifications.

Health insurers have terabytes of transactional data. However, transactional data does not tell a member-level story. Humana Inc. is often faced with requirements for tagging (identifying) members with various clinical conditions such as diabetes, depression, hypertension, hyperlipidemia, and various member-level utilization metrics. For example, Consumer Health Tags are built to identify the condition (that is, diabetes, hypertension, and so on) and to estimate the intensity of the disease using medical and pharmacy administrative claims data. This case study takes you on an analytics journey from the initial problem diagnosis and analytics solution using SAS^®.

Coming off a recent smart grid implementation, OGE Energy Corp. was collecting more data than at any time in its history. This data held the potential to help the organization uncover new insights and chart new paths. Find out how OGE Energy is building a culture of data analytics by using SAS^® tools, a distributed analytics model, and an analytics center of excellence.

Creating an environment that enables and empowers self-service and agile analytic capabilities requires a tremendous amount of working together and extensive agreements between IT and the business. Business and IT users are struggling to know what version of the data is valid, where they should get the data from, and how to combine and aggregate all the data sources to apply analytics and deliver results in a timely manner. All the while, IT is struggling to supply the business with more and more data that is becoming available through many different data sources such as the Internet, sensors, the Internet of Things, and others. In addition, once they start trying to join and aggregate all the different types of data, the manual coding can be very complicated and tedious, can demand extraneous resources and processing, and can negatively impact the overhead on the system. If IT enables agile analytics in a data lab, it can alleviate many of these issues, increase productivity, and deliver an effective self-service environment for all users. This self-service environment using SAS^® analytics in Teradata has decreased the time required to prepare the data and develop the statistical data model, and delivered faster results in minutes compared to days or even weeks. This session discusses how you can enable agile analytics in a data lab, leverage SAS analytics in Teradata to increase performance, and learn how hundreds of organizations have adopted this concept to deliver self-service capabilities in a streamlined process.

Higher education institutions have a plethora of analytical needs. However, the irregular and inconsistent practices in connecting those needs with appropriate analytical delivery systems have resulted in a patchwork this patchwork sometimes overlaps unnecessarily and sometimes exposes unaddressed gaps. The purpose of this paper is to examine a framework of components for addressing institutional analytical needs, while leveraging existing institutional strengths to maximize analytical goal attainment most effectively and efficiently. The core of this paper is a focused review of components for attaining greater analytical strength and goal attainment in the institution.

Managing your career future involves learning outside the box at all stages. The next step is not always on the path we planned as opportunities develop and must be taken when we are ready. Prepare with this paper, which explains important features of Base SAS^® that support teams. In this presentation, you learn about the following: concatenating team shared folders with personal development areas; creating consistent code; guidelines for a team (not standards); knowing where the documentation will provide the basics; thinking of those who follow (a different interface); creating code for use by others; and how code can learn about the SAS environment.

A recurring problem with large research databases containing sensitive information about an individual's health, financial, and personal information is how to make meaningful extracts available to qualified researchers without compromising the privacy of the individuals whose data is in the database. This problem is exacerbated when a large number of extracts need to be made from the database. In addition to using statistical disclosure control methods, this paper recommends limiting the variables included in each extract to the minimum needed and implementing a method of assigning request-specific randomized IDs to each extract that is both secure and self-documenting.

SQL is a universal language that allows you to access data stored in relational databases or tables. This hands-on workshop presents core concepts and features of using PROC SQL to access data stored in relational database tables. Attendees learn how to define, access, and manipulate data from one or more tables using PROC SQL quickly and easily. Numerous code examples are presented on how to construct simple queries, subset data, produce simple and effective output, join two tables, summarize data with summary functions, construct BY-groups, identify FIRST. and LAST. rows, and create and use virtual tables.

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SAS^® Enterprise Guide^® empowers organizations, programmers, business analysts, statisticians, and end users with all the capabilities that SAS has to offer. This hands-on workshop presents the SAS Enterprise Guide graphical user interface (GUI). It covers access to multi-platform enterprise data sources, various data manipulation techniques that do not require you to learn complex coding constructs, built-in wizards for performing reporting and analytical tasks, the delivery of data and results to a variety of mediums and outlets, and support for data management and documentation requirements. Attendees learn how to use the graphical user interface to access SAS^® data sets and tab-delimited and Microsoft Excel input files; to subset and summarize data; to join (or merge) two tables together; to flexibly export results to HTML, PDF, and Excel; and to visually manage projects using flow diagrams.

The intrepid Mars Rovers have inspired awe and curiosity and dreams of mapping Mars using SAS/GRAPH^® software. This presentation demonstrates how to import Esri shapefile (SHP) data (using the MAPIMPORT procedure) from sources other than SAS^® and GfK GeoMarketing map data to produce useful (and sometimes creative) maps. Examples include mapping neighborhoods, ZCTA5 areas, postal codes, and of course, Mars. Products used are Base SAS^® and SAS/GRAPH^®. SAS programmers of any skill level will benefit from this presentation.

At the end of a project, many institutional review boards (IRBs) require project directors to certify that no personally identifiable information (PII) is retained by a project. This paper briefly reviews what information is considered PII and explores how to identify variables containing PII in a given project. It then shows a comprehensive way to ensure that all SAS^® variables containing PII have their values set to NULL and how to use SAS to document that this has been done.

SAS^® has an amazing arsenal of tools for using and displaying geographic information that are relatively unknown and underused. High-quality GfK GeoMarketing maps have been provided by SAS since the second maintenance release for SAS^® 9.3, as sources for inexpensive map data dried up. SAS has been including both GfK and traditional SAS map data sets with licenses for SAS/GRAPH^® software for some time, recognizing there will need to be an extended transitional period. However, for those of us who have been putting off converting our SAS/GRAPH mapping programs to use the new GfK maps, the time has come, as the traditional SAS map data sets are no longer being updated. If you visit SAS^® Maps Online, you can find only GfK maps in current maps. The GfK maps are updated once a year. This presentation walk through the conversion of a long-standing SAS program to produce multiple US maps for a data compendium to take advantage of GfK maps. Products used are Base SAS^® and SAS/GRAPH^®. SAS programmers of any skill level will benefit from this presentation.

This presentation brings together experiences from SAS^® professionals working as volunteers for organizations, charities, and in academic research. Pro bono work, much like that done by physicians, attorneys, and professionals in other areas, is rapidly growing in statistical practice as an important part of a statistical career, offering the opportunity to use your skills in a places where they are so needed but cannot be supported in a for-pay position. Statistical volunteers also gain important learning experiences, mentoring, networking, and other opportunities for professional development. The presenter shares experiences from volunteering for local charities, non-governmental organizations (NGOs) and other organizations and causes, both in the US and around the world. The mission, methods, and focus of some organizations are presented, including DataKind, Statistics Without Borders, Peacework, and others.