Implementing CDISC Data Models in the SAS® Metadata Server



Relational Events in both the Re-Engineering of SAS and the Life Sciences Industry

Edward Helton, Ph.D. and Laurie Rose, SAS Institute, Cary, NC

Many significant events occurred in 2006 that represented the FDA's initiatives to re-engineer the life sciences industry. For example the final draft of the Guidance for Industry for Providing Regulatory Submissions in Electronic Format (April 2006) provides for the use of an XML backbone specification, the CDISC standard and the replacement of define.pdf with the CRTDDS or also called define.xml. SAS has put considerable effort into preparing for the new XML era in life sciences development. Much more is to come in 2006/2007 such as the new Part 11 and the guidance on biostatistics (Analysis Data Model based) and review of the industry mock electronic submission to FDA using standard data and format and supported by SAS and other industry software. Data Integration (DI) is in a new era at SAS and its very probable relationship to the FDA JANUS and Critical Path repository will become increasingly apparent. DI will also very likely be very important to the coming Adverse

Event Reporting System II not far on the distant horizon. Lastly, SAS is a strong player in the over arching metadata model ( BRIDG) for the RCRIM end to end movement of healthcare data ( EHR) to therapeutic product development. Our vision which is in close alignment with the FDA electronic platform concept is supported by SAS integrated and/or validated tools for mapping, mining, analysis, reporting and submitting electronic data to regulatory agencies. eCTD Life-Cycle Management interfaced with pharmacogenomics that uses standards and compliant workflow for annotation, acquisition, aggregation, analysis and archiving is a SAS endgame.

SAS Drug Development: Definition, Use, Adoption and Evolution

Dave Handelsman, SAS Institute, Cary, NC

SAS Drug Development provides a centralized, integrated system for managing, analyzing, reporting and reviewing clinical research information. The solution, at its core, encompasses two key components: a compliant repository and framework for managing clinical research content, and an intelligence layer to develop, test and generate clinical research analyses. From a traditional SAS programmer, biostatistician and testing perspective, SAS Drug Development provides a unique toolset – specifically designed for the life sciences research industries – to work more efficiently within trials, across research compounds and with your internal customers.

This presentation is designed to introduce the capabilities of SAS Drug Development to the audience, describe how the solution is being used to today, discuss the solution’s adoption within the industry, and provide a technical update on the most current release of the solution.

Best Practices for Working with CDISC Metadata in the SAS® Data Integration Server

Michael Kilhullen, SAS Institute, Cary, NC

Over the past few years, SAS has demonstrated how the SAS 9 Metadata Server can be used to implement and manage CDISC metadata and facilitate a data driven approach to standardizing clinical data. In this paper, we examine best practices for using SAS Data Integration Studio to execute and manage key CDISC concepts such as controlled terminology, value level metadata, normalization of data, importing and exporting XML documents, and producing the CRT-DDS. Within the context of these topics, we will also examine considerations for setting up and managing study metadata, writing efficient transformation processes, leveraging metadata to answer key business questions, and effective use of change management.

Implementing CDISC Data Models in the SAS® Metadata Server

Michael Kilhullen, SAS Institute, Cary, NC

 

The SAS metadata server is a core component of all SAS 9 solutions.  It delivers the power to integrate, share, centrally manage and leverage metadata across entire organizations. Through these capabilities, standard data models such as the CDISC Study Data Tabulation Model (SDTM) can be deployed and leveraged by all users in your organization without the need for developing additional metadata libraries or programs. In this paper, we examine the value that the SAS open metadata architecture can bring to your organization, how the SDTM data model is implemented in the metadata server, and how the metadata can be leveraged by SAS products and solutions such as Data Integration Studio.

The use of CDISC Standards in SAS from Data Capture to Reporting

Andrew Fagan, SAS Institute, Cary, NC

John Leveille, d-Wise Consulting, Raleigh, NC

This paper describes how CDISC standards can be applied in a variety of SAS applications commonly used during the clinical development process. Data originates in an electronic data capture (EDC) or clinical data management system (CDMS) and is transferred to SAS Data Integration Studio in a raw or operational data model (ODM) structure. It is then transformed into submission data tabulation (SDTM) format using a SAS Data Integration Studio. From there, the SDTM format is loaded into SAS Drug Development where it can be accessed for compliant reporting and analysis.

Many of these steps can now be automated within the SAS tools, including comparison to the SDTM model, transferring the data from SAS Data Integration Studio to SAS Drug Development, and making the SDTM model available to the Data Explorer component within SAS Drug Development. These technical features are explained, as are a number of alternative methods.

When Proc Report and Tag sets Collide...

Eric Gebhart, SAS Institute, Cary, NC

Sandy McNeill, SAS Institute, Cary, NC

Proc Report and ODS Tag sets are two incredibly powerful tools. Combining these two powerhouses enables us to create customized output that will present your data in ways you have only imagined. See what can happen when we use the power of proc report to drive special behaviors within a tag set.

Creating Multi-Sheet Excel Workbooks the Easy Way with SAS®

Vince DelGobbo, SAS Institute, Cary, NC

Transferring data and analytical results between SAS and Microsoft Excel can be difficult, especially when SAS is not installed on a Windows platform. This presentation discusses using new XML support in Base SAS 9.1 software to create multi-sheet Microsoft Excel workbooks (versions 2002 and later). You will learn step-by-step techniques for quickly and easily creating attractive multi-sheet Excel workbooks that contain your SAS output. Many "tips and tricks" associated with the ExcelXP ODS tagset will be divulged. Most importantly, the techniques that are presented can be used regardless of the platform on which SAS software is installed, including a mainframe! The use of SAS server technology is also discussed. Although the title is similar to a paper presented at last year's User Conference, this talk contains new and revised material not previously presented by this author.

Getting Started with the DATA Step Hash Object

Jason Secosky, SAS Institute, Cary, NC

Janice Bloom, SAS Institute, Cary, NC

A time consuming part of many SAS® programs is looking up a value from one data set in another data set. SAS 6 lookup methods, like SET with KEY= or a format, are good for many applications. However, in SAS 9, there is a better tool, the DATA Step hash object. The hash object provides a fast, easy way to perform lookups without sorting or indexing. This paper introduces the hash object, examines a few common hash object methods, compares SAS 6 techniques with the hash object, and builds rules of thumb for when to apply this new technology to your programs.

SAS/STAT 9.2: Coming to a SAS Site Near You

Maura Stokes, SAS Institute, Cary, NC

Robert N. Rodriguez, SAS Institute, Cary, NC

Tonya Balan, SAS Institute, Cary, NC

The next SAS/STAT release provides major new functionality to statisticians. Bayesian analyses comes to the PHREG, LIFEREG, and GENMOD procedures. Survey data analysis procedures now offer variance computations based on Jackknife and BRR methods. The new EFFECT statement provides more modeling options in procedures such as MIXED, QUANTREG, and GLMSELECT. More procedures have been updated to provide multi-threaded computations where possible for those users with huge data sets. In addition, ODS statistical graphics is production, and numerous new graphics are available in many of the statistical procedures, from FREQ to GLM to GLIMMIX.

This talk will focus on a few of the most important new additions to SAS/STAT software and their benefits to users. In addition, it will provide a sneak peak at several new experimental procedures that will be announced in 2007.

Real-time Data Quality - The Right Answers at the Right Time

Pat Herbert, SAS Institute, Cary, NC

Although Data Quality is a fundamental part of any Data Warehousing effort, there is a need for good Data Quality practices where data is introduced into the enterprise - in real-time at the transactional, ERP and call center applications. Real-time Data Quality initiatives can result in significant improvements in areas that include overall system performance, simplified data entry, and better overall results. Additionally, one of the most important benefits associated with real-time Data Quality is improved customer satisfaction. The DataFlux® Integration Server is perfectly positioned to deliver real-time Data Quality capabilities when and where they are needed, allowing common business rules to be shared by all applications in the enterprise.

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