The following software is required prior to attending the class. Some of the software will be supplied by the instructor while others
are required on the student's machine.
- Windows XP
- Internet Explorer or Mozilla Firefox
- SAS version 8.12 or higher
- Microsoft Office: Word and Excel
- Transdata - Transformation of data into CDISC Standards.
Review Transdata Installation Instructions.
- CDISC Builder - Building CDISC standard data structures and perform validation.
Review CDISC Builder Installation
- Definedoc - Domain data definition documentation .
Review the Definedoc Installation
Latest download will be made available before week of class.
Sy Truong is the cofounder and president of MXI (Meta-Xceed, Inc.) since 1997. MXI provides software solutions within the Pharmaceutical Industry specializing
in CDISC data standards, SAS validation, electronic submission, data analysis and reporting. Sy is one of the committee members of the Bay Area SAS User Group (www.basas.com). He is a frequent contributor and presenter
at PharmaSUG, WUSS, and SUGI conferences. He's currently writing a book for SAS Publishing entitled
Becoming a SAS Clinical Trials Programmer.
Some of the topics covered in the course
- Project Definition, Plan and Management
- Data Standard Analysis and Review
- Data Transformation Specification and Definition
- Performing Data Transformation to Standards
- Review and Validation of Transformations and Standards
- Domain Documentation for DEFINE.PDF and DEFINE.XML
The regulatory requirements are going to include CDISC in the near future and the benefits are obvious. It is therefore wise and prudent to implement with techniques and processes refined from lessons learned based on real life implementations.
CDISC standards have been in development for many years. There have been structural changes to the recommended standards going forward from version 2 to 3. It is still an evolving process but it has reached a point of critical mass that organizations are recognizing the benefits of taking the proposed standard data model out of the theoretical and putting it into real life applications. The complexity of clinical data coupled with technologies involved can make implementation of a new standard challenging. This paper will explore the pitfalls and present methodologies and technologies that would make the transformation of nonstandard data into CDISC efficient and accurate.
It is important to have a clear vision of the processes for the project before you start. This provides the ability to resource and plan for all the processes. This is an important step since the projects can push deadlines and break budgets due to the resource intensive nature of this effort. The organization and planning for this undertaking can become an essential first step towards an effective implementation.
Course slides, sample data, quiz and other
resources are available here.