Domain-specific common data elements (CDEs) are emerging as an effective approach to standards-based clinical research data storage and retrieval and have been broadly adopted. For example, the National Cancer Institute (NCI) created the Cancer Data Standards Repository (caDSR) based on the ISO/IEC 11179 standard for metadata repositories. However, cancer clinical research community faces significant challenges related to scalability, governance, and data quality for CDE modeling. In particular, the lack of robust, principled and automated QA algorithms contributes to CDE content errors that can have a significant negative impact on downstream CDE uses. Our overall goal is to build a novel quality assurance (QA) framework to overcome methodological and computational challenges with respect to error detection in the modeling of common data elements (CDEs), recognition of duplicates or similar CDEs, and CDE usability, thereby producing high-quality CDEs for cancer clinical research studies. Our proposed approach is to design, develop and evaluate an integrative platform known as caCDE-QA that implements a suite of QA tools to audit experimental cancer study CDEs represented in a semantic web framework, deploying a QA web-portal with standard semantic services for community collaboration.
Our specific aims are: (1) To develop a suite of QA tools for validation and harmonization of cancer study CDEs. (2) To apply the QA tools to audit experimental cancer study CDEs represented in a semantic web framework. We will also evaluate the performance of the QA tools in terms of efficiency, accuracy and usability by comparing with the baseline tools that exist in the NCI caDSR and CIMI communities. (3) To deploy and evaluate a QA web-portal for collaborative CDE review and harmonization. We will coordinate community-based efforts soliciting requirements regarding cancer study CDE discovery and harmonization and fostering a specification of the common data element services (CDES) standard. We will disseminate and test the newly developed QA methods and tools in collaboration with the Clinical Data Interchange Standards Consortium (CDISC) and CIMI Communities. This project will contribute novel QA methods and tools for validation and semantic harmonization of cancer study CDEs. This is of great significance in that it will be enabling efficient CDE modeling and producing high-quality reusable CDEs, which are critical for facilitating cancer clinical research data sharing and accelerating systematic clinical outcomes capturing.
This project is to build a novel quality assurance (QA) framework to overcome methodological and computational challenges with respect to error detection in the modeling of common data elements (CDEs), recognition of duplicates or similar CDEs, and CDE usability, thereby producing high-quality CDEs for cancer clinical research studies. The ultimate goal is to enable standard cancer clinical research data sharing and systematic clinical outcomes capturing.
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