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.

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

Simple Example

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.

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

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)

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

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

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

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

EDC Generation REDCap data dictionary

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

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

Results – Examples Search: smoke+day

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

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)

Questions Thank you!