A semi-automated pipeline for fulfillment of resource requests from a longitudinal Alzheimer's disease registry. Issue 4 (26th August 2019) - McKenzie, Katelyn A; Hunt, Suzanne L; Hulshof, Genevieve; Mudaranthakam, Dinesh Pal; Meyer, Kayla; Vidoni, Eric D; Burns, Jeffrey M; Mahnken, Jonathan D
Abstract: Objective: Managing registries with continual data collection poses challenges, such as following reproducible research protocols and guaranteeing data accessibility. The University of Kansas (KU) Alzheimer's Disease Center (ADC) maintains one such registry: Curated Clinical Cohort Phenotypes and Observations (C3PO). We created an automated and reproducible process by which investigators have access to C3PO data. Materials and Methods: Data was input into Research Electronic Data Capture. Monthly, data part of the Uniform Data Set (UDS), that is data also collected at other ADCs, was uploaded to the National Alzheimer's Coordinating Center (NACC). Quarterly, NACC cleaned, curated, and returned the UDS to the KU Data Management and Statistics (DMS) Core, where it was stored in C3PO with other quarterly curated site-specific data. Investigators seeking to utilize C3PO submitted a research proposal and requested variables via the publicly accessible and searchable data dictionary. The DMS Core used this variable list and an automated SAS program to create a subset of C3PO. Results: C3PO contained 1913 variables stored in 15 datasets. From 2017 to 2018, 38 data requests were completed for several KU departments and other research institutions. Completing data requests became more efficient; C3PO subsets were produced in under 10 seconds. Discussion: The data management strategy outlined above facilitated reproducible research practices, which is fundamental to the future of research as it allows replication and verification to occur. Conclusion: We created a transparent, automated, and efficient process of extracting subsets of data from a registry where data was changing daily.