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Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
Published on: January 22, 2011
Successes and lessons learned in database development for national multi-site cancer care delivery research trials:
David Zahrieh1, Shauna L Hillman2, Angelina D Tan2
1Department of Quantitative Health Sciences and Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN, 55905, USA. zahrieh.david@mayo.edu.
Introduction:
Alliance for Clinical Trials in Oncology (Alliance) coordinated trials utilize Medidata Rave® (Rave) as the primary clinical data capture system. A growing number of innovative and complex cancer care delivery research (CCDR) trials are being conducted within the Alliance with the aims of studying and improving cancer-related care. Because these trials encompass patients, providers, practices, and their interactions, a defining characteristic of CCDR trials is multilevel data collection in pragmatic settings. Consequently, CCDR trials necessitated innovative strategies for database development, centralized data management, and data monitoring in the presence of these real-world multilevel relationships. Having real trial experience in working with community and academic centers, and having recently implemented five CCDR trials in Rave, we are committed to sharing our strategies and lessons learned in implementing such pragmatic trials in oncology.
Methods:
Five Alliance CCDR trials are used to describe our approach to analyzing the database development needs and the novel strategies applied to overcome the unanticipated challenges we encountered. The strategies applied are organized into 3 categories: multilevel (clinic, clinic stakeholder, patient) enrollment, multilevel quantitative and qualitative data capture, including nontraditional data capture mechanisms being applied, and multilevel data monitoring.
Results:
A notable lesson learned in each category was (1) to seek long-term solutions when developing the functionality to push patient and non-patient enrollments to their respective Rave study database that affords flexibility if new participant types are later added; (2) to be open to different data collection modalities, particularly if such modalities remove barriers to participation, recognizing that additional resources are needed to develop the infrastructure to exchange data between that modality and Rave; and (3) to facilitate multilevel data monitoring, orient site coordinators to the their trial's multiple study databases, each corresponding to a level in the hierarchy, and remind them to establish the link between patient and non-patient participants in the site-facing NCI web-based enrollment system.
Conclusion:
Although the challenges due to multilevel data collection in pragmatic settings were surmountable, our shared experience can inform and foster collaborations to collectively build on our past successes and improve on our past failures to address the gaps.
Insights
Implementing complex cancer care delivery research (CCDR) trials requires innovative strategies for multilevel data collection and management. Sharing lessons learned from five Alliance CCDR trials in Medidata Rave can improve future pragmatic oncology research.
Area of Science:
- Oncology
- Clinical Trials
- Health Services Research
Background:
- Alliance for Clinical Trials in Oncology (Alliance) uses Medidata Rave® for clinical data capture.
- Cancer Care Delivery Research (CCDR) trials are increasing in complexity, involving patients, providers, and practices.
- Multilevel data collection in pragmatic settings presents unique challenges for database development and data management.
Purpose of the Study:
- To describe strategies for database development and data management in multilevel CCDR trials.
- To share lessons learned from implementing five pragmatic CCDR trials within the Alliance.
- To inform future innovative strategies for oncology clinical trials.
Main Methods:
- Analysis of five Alliance CCDR trials to identify database development needs.
- Description of novel strategies applied to overcome challenges in multilevel enrollment, data capture, and monitoring.
- Categorization of strategies into multilevel enrollment, quantitative/qualitative data capture, and multilevel data monitoring.
Main Results:
- Developed flexible enrollment functionality for patient and non-patient participants in Rave.
- Utilized diverse data collection modalities, requiring infrastructure for data exchange with Rave.
- Implemented multilevel data monitoring by orienting site coordinators to multiple databases and participant links.
Conclusions:
- Challenges in multilevel data collection for pragmatic trials are surmountable.
- Sharing experiences can foster collaboration and improve future CCDR trial implementations.
- Addressing data management gaps is crucial for advancing cancer care delivery research.
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