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A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks.

Qoua L Her1, Jessica M Malenfant1, Sarah Malek1

  • 1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, US.

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|August 11, 2018
PubMed
Summary
This summary is machine-generated.

Automating distributed regression analysis (DRA) in distributed data networks (DDNs) enhances patient privacy. A new framework and workflow enable DRA in PopMedNet-driven DDNs, facilitating broader adoption of secure data analysis methods.

Keywords:
Distributed data networksDistributed regressionPharmacoepidemiologyPopMedNet™Privacy-protecting methodsSentinel

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Area of Science:

  • Health Informatics
  • Data Security
  • Statistical Analysis

Background:

  • Patient privacy concerns hinder multi-site data pooling.
  • Distributed data networks (DDNs) and privacy-preserving methods like distributed regression analysis (DRA) offer solutions.
  • DRA is underutilized in large-scale DDNs.

Purpose of the Study:

  • To design and implement a process framework and query workflow for automatable DRA.
  • To enable DRA within real-world DDNs using the PopMedNet™ platform.
  • To minimize disruptions and modifications for data partners.

Main Methods:

  • Surveyed hardware and software configurations across Sentinel System data partners.
  • Developed a three-step process framework for automatable DRA.
  • Created a PopMedNet query workflow with adjustable automation levels.

Main Results:

  • Implemented a framework enabling automatable DRA in DDNs.
  • The process involves local dataset assembly, distributed analysis, and iterative file transfer.
  • The DRA query workflow supports various statistical software and regression models.

Conclusions:

  • The developed process framework and query workflow are generalizable to other PopMedNet-based DDNs.
  • Successful DRA implementation in Sentinel can drive wider adoption in other DDNs.
  • DRA has the potential to transform data analysis paradigms in distributed networks.