A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks. Issue 1 (25th May 2018)

• Record Type: Journal Article
• Authors: Her, Qoua L.; Malenfant, Jessica M.; Malek, Sarah; Vilk, Yury; Young, Jessica; Li, Lingling; Brown, Jeffery; Toh, Sengwee
• Is Part Of: EGEMS. Volume 6:Issue 1(2018)
• Publication Date: 2018-05-25
• Subjects: Distributed regression -- Distributed data networks -- Privacy-protecting methods -- Sentinel -- Pharmacoepidemiology -- PopMedNet™; Medical records -- Data processing -- Periodicals; Medical care -- Data processing -- Periodicals; Medical Records; Automatic Data Processing; Medical care -- Data processing; Medical records -- Data processing; Periodicals; Periodicals; 651.504261
• DOI: 10.5334/egems.209
• Languages: English
• ISSNs: 2327-9214

Abstract

Introduction: Patient privacy and data security concerns often limit the feasibility of pooling patient-level data from multiple sources for analysis. Distributed data networks (DDNs) that employ privacy-protecting analytical methods, such as distributed regression analysis (DRA), can mitigate these concerns. However, DRA is not routinely implemented in large DDNs. Objective: We describe the design and implementation of a process framework and query workflow that allow automatable DRA in real-world DDNs that use PopMedNet™, an open-source distributed networking software platform. Methods: We surveyed and catalogued existing hardware and software configurations at all data partners in the Sentinel System, a PopMedNet-driven DDN. Key guiding principles for the design included minimal disruptions to the current PopMedNet query workflow and minimal modifications to data partners' hardware configurations and software requirements. Results: We developed and implemented a three-step process framework and PopMedNet query workflow that enables automatable DRA: 1) assembling a de-identified patient-level dataset at each data partner, 2) distributing a DRA package to data partners for local iterative analysis, and 3) iteratively transferring intermediate files between data partners and analysis center. The DRA query workflow is agnostic to statistical software, accommodates different regression models, and allows different levels of user-specified automation. Discussion: The process framework can be generalized to and the query workflow can be adopted by other PopMedNet-based DDNs. Conclusion: DRA has great potential to change the paradigm of data analysis in DDNs. Successful implementation of DRA in Sentinel will facilitate adoption of the analytic approach in other DDNs.