BASUG Quarterly Meeting Announcement
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Short Meeting
Description The non-profit
Clinical Data Interchange Standards Consortium (aka CDISC) continues to
develop data standards that are being warmly embraced by the FDA. Come
learn about streamlining the implementation of these standards.
Resistance is futile. This meeting is
particularly relevant to SAS programmers, Biostatisticians, Clinical Data
Managers, and Regulatory Specialists in the Biotechnology/Pharmaceutical and
Medical Device industries. Those
outside clinical research in these industries may find this meeting helpful
for informing development of standards for their own data. Immediately
following the meeting, we will provide an informal light buffet (free) lunch
for all meeting attendees. We hope you can stay for this opportunity to
network and socialize with your fellow SAS users. The mission of the Clinical Data Interchange Standards Consortium
(CDISC) is to “develop and support global, platform-independent data
standards that enable information system interoperability to improve medical
research and related areas of healthcare.”
Since its formation as an independent, non-profit in 2000, CDISC has
made substantial progress towards it mission and has developed a suite of
data and metadata standards that are being adopted by the Food and Drug
Administration (FDA). The ultimate goal of data standardization across organizations is to
maximize efficiency and quality of the FDA submission process overall. Often, however, efficiency is initially
reduced until processes are developed and streamlined to fully implement
standards. The speakers will discuss
challenges and opportunities related to implementing two CDISC standards, the
Study Data Tabulation Model (SDTM) and Case Report Tabulation Data Definition
Specification using define.xml. Given that the FDA has adopted SDTM and define.xml for submitting
tabulation data it is inevitable that all submissions will eventually be
expected to conform to these standards.
Thus it is imperative for those working with clinical data in the
Biotechnology and Pharmaceutical industry to become proficient in these
standards. |
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Topic: |
Managing
Clinical Data in the Age of CDISC |
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When: |
March 2, 2011 8:15am – Noon |
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Where: |
Microsoft New
England Research & Development Center (857) 453-6000 |
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Directions: |
Please
visit the meeting site directions page.
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How: |
Individual, On-Line Registration
Required. No Email! |
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To register: |
Please
visit the meeting registration page. |
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Contact: |
If
you have questions about the meeting, |
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Agenda* |
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8:15 |
Sign in and Refreshments |
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9:00 |
Announcements |
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9:15 |
In-Depth Review of Validation Tools to Check Compliance of CDISC SDTM-Ready Clinical Datasets by Bhavin Busa, Sr. Statistical Programmer, Cubist Pharmaceuticals, Inc. |
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10:30 |
Break |
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10:45 |
The Use of Metadata in Creating, Transforming and Transporting Clinical Data by Gregory Steffens, Director, Data Management and SAS Programming, ICON Development Solutions |
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Noon |
Meeting Adjourned |
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Noon-1:00pm |
FREE Networking Lunch |
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*Note: Times (and sequence) are approximate and
subject to change. Please re-visit the BASUG website for updated information.
Abstracts and Bios
In-Depth Review of Validation Tools to Check Compliance of CDISC SDTM-Ready Clinical Datasets by Bhavin Busa, Sr. Statistical Programmer, Cubist Pharmaceuticals, Inc.
Abstract
As the pharmaceutical organizations becomes increasingly involved in developing efficient and cost effective ways to produce CDISC SDTM-compliant clinical trial domains, it has become more crucial to identify a validation tool to check compliance and streamline operations for the preparation of submission-ready files in accordance with the most recent SDTM Implementation Guide. The ultimate need of a SDTM validation tool is to check the compliance of the SDTM domains for successful load into Janus (clinical data repository at FDA) and thereby reducing risk of delay in the submission review process.
The presentation will provide an
in-depth review of various SDTM validation tools and provide some insight on
the framework, installation and pros & cons of each solution by implementing
them on a real submission-ready clinical datasets. The high-level outline of
the presentation is as follows:
·
Brief
introduction of CDISC SDTM standards
·
FDA
initiatives on CDISC SDTM standards
·
SDTM
Data Flow from Sponsor to FDA
·
Needs
and requirements of an SDTM validation tool
·
In-depth
Review of various SDTM validation tools:
o
o
SAS®
– Clinical Standard Toolkit
o
OpenCDISC
Validator
o
In-House
SAS Macro Based Solution
·
Side-by-Side
comparison of SDTM validation tools
·
Summarizing
evaluation results and recommendation
·
Open
the floor for discussion on other implementation ideas and strategies
Speaker Bio
Bhavin Busa is a Senior Statistical Programmer at
Cubist Pharmaceuticals. He is an active participant in CDISC initiative and is
well adept with SDTM, ADaM, and Define.xml standards. He is a regular presenter at Pharmaceutical
SAS User Group, Mass Biotech Council, and is a committee member of Boston Area
CDISC User Network. In the past, he has
been honored with the best paper award in the Regulatory Submission section of
the PharmaSUG 2008 on the very topic of CDISC SDTM validation. In addition to his expertise in CDISC SDTM,
he has also developed various SAS based solution to automate creation of
analysis tables and Define.xml. On a personal note, he believes in sharing
knowledge and is therefore very active in presenting topics that will benefit
the SAS community and the industry as a whole.
The Use of
Metadata in Creating, Transforming and Transporting Clinical Data
by Gregory Steffens, Director, Global SAS Programming, ICON Development Solutions
Abstract
Pharmaceutical
companies must address the need to define database structures quickly and
easily in order to
·
support
clinical trial analysis databases
·
import
data from central laboratories and CROs
·
export
data to Data Safety Monitoring boards
·
share
data between corporate sites within and without the
·
submit
data to the FDA and other regulatory agencies
Industry-level
standards are being discussed and developed to help address this need and most
pharmaceutical companies have corporate standards of database structures. However, generally these industry and
corporate standards are stored in .pdf or MSword documents. As
such they are accessible only to people reading the documents. Storing this same information in meta data sets adds much value to the effort put into
defining these standards because this information is now available to computer
programs. This presentation describes
standard meta data set structures that are capable of
storing specifications for any of the above database requirements. These meta data,
along with SAS macros and a SAS/AF application that access these meta data,
supports the specification, creation, importation, exportation, comparison and
validation of databases and format catalogs.
Further, these meta data can be used to export
data and metadata to XML format. The meta data structures can be implemented in any relational
database or in SAS itself. The macros
support such functions as:
·
printing
the specification in several formats, including the define.xml and define.html
formats
·
adding
all the data set and variable attributes to the database
·
listing
discrepancies between the specification and the database
·
sorting
the data by primary keys
·
reordering
variables according to FDA preferences
·
creation
of format catalogs
·
creation
of character decode variables from code variables
·
comparison
of database structures to assist in enforcing project standards
·
transforming
a database from one database structure to another, using map metadata
As industry
and corporate standards are developed there is great value in documenting these
in meta data sets, that are accessible to computer programs, rather than in
word or .pdf.
In summary,
when importing data from outside sources, the data specification is implemented
in meta data so that computer programs can
automatically compare the data submitted by the lab or CRO to the specification
and list discrepancies where the database does not conform to the
specification. When specifying study
analysis databases, macros assist in the building of the database by
automatically adding all the specified variable and data set attributes, such
as labels and formats, adding decode variables, sorting the data, building
format catalogs, etc. When submitting
data to the FDA a thorough specification can be printed from the metadata or
exported to XML format, with bookmarks and hyperlinks.
Speaker Bio
Greg Steffens
has been using SAS for programming and applications development since 1981,
primarily in the pharmaceutical and health insurance industries. He has held job positions ranging from lead
technical to director-level management.
He is currently Director of SAS Programming at ICON PLC and a member of two
CDISC teams. Greg's experience includes
the design and development of metadata and software to automate data
definition, data transformation, data validation and FDA submissions.
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Development Center
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