Insights from Adopting a Data Commons Approach for Large-Scale Observational Cohort Studies: The California Teachers Study

James V. Lacey, Nadia T. Chung, Paul Hughes, Jennifer L Benbow, Christine Duffy, Kristen E Savage, Emma S Spielfogel, Sophia S. Wang, Maria Elena Martinez, Sandeep Chandra
2020 Cancer Epidemiology, Biomarkers and Prevention  
Large-scale cancer epidemiology cohorts (CECs) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more Findable, Accessible, Interoperable, and Reusable, or FAIR. How CECs should approach this transformation is unclear. The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With
more » ... ng strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a Data Warehouse, to integrate and manage data; and a secure shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses. Our new CTS infrastructure includes a Data Warehouse and data marts, which are focused subsets from the Data Warehouse designed for efficiency. The secure CTS workspace utilizes a Remote Desktop service that operates within a HIPAA and FISMA compliant platform. Our infrastructure offers broad access to CTS data; includes statistical analysis and data visualization software and tools; flexibly manages other key data activities (e.g., cleaning, updates, & data sharing); and will continue to evolve to advance FAIR principles. Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's Cancer Research Data Commons Framework. The CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.
doi:10.1158/1055-9965.epi-19-0842 pmid:32051191 fatcat:t7xkuuqo3zevbfwqpo34pypsqe