1. A Web-based System for Management and Storage of Metadata for Multi-Center Clinical Trials
Kevin A. Wilson, Michael Ham, Brenda Hair and Eugene Turner
RTI International, Durham NC.

2. Introduction
It is widely acknowledged that use of data standards and common data elements (CDEs) has the potential to:
Reduce trial development time
Improve data quality
Improve comparability of data
Support adherence to best practices
But implementing common data elements is fraught with practical issues:
Which CDEs? Too many standards
Accessibility
Data systems still have to be built, reviewed and tested
Consensus on measures for a given domain needs to be achieved
Multiple research networks with different goals, constraints and systems
Existing standards and practices that have evolved on networks

3. Simple Example

4. Existing Standards Standard Focus and Functionality
NINDS Common Data Elements
Domain standards for neurological research, including stroke, traumatic brain injury, epilepsy and other neurological conditions. CDEs, guidance and data dictionaries available from NINDS website.
PROMIS Measures
Patient reported outcomes measurement for a variety of domains, including emotional distress, cognition, alcohol consumption, etc. Measures available for administration in Assessment Center or download as CRFs.
PhenX Measures
PhenX toolkit provides shopping cart functionality and a broad range of measures. Data dictionaries, CRFs, and REDCap data dictionaries available for download.
CDISC CDASH
Defines several domains (DM, IE) focused on standardized data collection for submission to FDA.
NCI/caDSR
Cancer Data Standards Registry and Repository provides curation capabilities for cancer CDEs.

5. Research Goals
The goals of this research were to produce a usable toolkit that can be used to:
Extract, review, and harmonize data elements from existing clinical trials within a network, thereby building a library of standardized data elements that can be used in future trials
Select data elements and groups of data elements and organize them into case report forms to meet the needs of a new trial
Generate PDF versions of case report forms in a standard format that are automatically linked to the data elements within the library
Export the CRFs in a format compatible with our electronic data capture systems, which would significantly reduce programmer, data manager and tester time in constructing new trials

6. System Features Key features of the system:
Data connectors to import metadata from existing clinical trial databases for multiple EDC systems (Medidata Rave, REDCap)
Data connectors to generate metadata from the CDE repository
Search ability with Boolean search terms to identify and group individual data elements into new CRFs
Ability to curate all metadata (variable name, data type, question text, update allowable values, required status, key words, category, description)
Ability to generate MS Word versions of the CRFs
Ability to build a library of standardized instruments (e.g., SF-12)

7. Architecture and Data Model
Due to limited funding and time, we opted for a simple data model and architecture as a proof of concept
Standard relational database (MS-SQL), with an ASP.Net front end
Tables: CDE_Elements, CDE_Forms, CDE_FormElements, plus audit tables
Metadata for elements:
[ElementID], [Name], [QuestionText], [Type], [QuestionValues],
[IsRequired], [Keywords], [DateCreated], [DateUpdated], [Category],
[Description], [ApplicableStudies], [OriginalForm].
Metadata for forms:
[FormElementID], [FormID], [QuestionOrder], [Name], [QuestionText]
[Type], [QuestionValues], [IsRequired], [Keywords], [DateCreated]
[DateUpdated], [Category], [Description].
Elements uniquely identified with GUIDs
Emphasis on usability – used ajax techniques to streamline user interface

8. Search
Goal: identify variables related to blast exposure Search term: blast

9. Refine Search
Goal: identify variables related to blast exposure and dizziness Search term: blast+dizziness

10. Constructing Forms
Goal: create custom demographics form Add: add data elements to new form, reorder as needed Generate: EDC or Word

11. EDC Generation
REDCap data dictionary

12. Results – Data Elements / Forms
Ongoing process of curation of data elements for two clinical trial networks
Chronic effects of Neurotrauma Consortium – 1 study (long-term effects of mild TBI in OIF/OEF/OND population)
Pelvic Floor Disorders Network – 4 studies (urinary incontinence, fecal incontinence, pelvic organ prolapse in females)
Data elements imported from existing clinical trial systems
8,620 “unique” data elements
291 “unique” case report forms
5 studies
Significant discrepancies for similar data elements between studies
Same variable, different name
Different variable, same name
Different response values and coding
Different wording for the same concept

13. Results – Examples
Search: uti+3 or more Search: anxiety+depression

14. Results – Examples
Search: smoke+day

15. Discussion
System works very well – curate, search, group, generate systems – but curation remains a manual and time consuming process despite easy-to-use interface
A more detailed analysis of discrepancies would highlight areas for process improvement that could streamline data acquisition, cleaning, analysis, and facilitate cross-study analyses
While the tool is useful, the simple data model limits its potential – a richer, semantic model would support more automated methods, such as the harmonization of extant data

16. Next Steps
Curation of existing data elements and continued import of elements from other trials and networks
Enhancement of curation tools to include basic automation – text-based matching
Enhancement of search techniques
Support for importing of data elements from other standardized resources (e.g., PhenX, NINDS CDEs)
Revision of architecture to support semantic annotation (e.g. through RDF/OWL) and mapping to industry standards (e.g. CDISC/CDASH/ODM)

17. Questions
Thank you!