Automatable distributed regression analysis of vertically partitioned data facilitated by PopMedNet: Feasibility and enhancement study

Qoua Her, Thomas Kent, Yuji Samizo, Aleksandra Slavkovic, Yury Vilk, Sengwee Toh

Research output: Contribution to journalArticlepeer-review

Abstract

Background: In clinical research, important variables may be collected from multiple data sources. Physical pooling of patient-level data from multiple sources often raises several challenges, including proper protection of patient privacy and proprietary interests. We previously developed an SAS-based package to perform distributed regression—a suite of privacy-protecting methods that perform multivariable-adjusted regression analysis using only summary-level information—with horizontally partitioned data, a setting where distinct cohorts of patients are available from different data sources. We integrated the package with PopMedNet, an open-source file transfer software, to facilitate secure file transfer between the analysis center and the data-contributing sites. The feasibility of using PopMedNet to facilitate distributed regression analysis (DRA) with vertically partitioned data, a setting where the data attributes from a cohort of patients are available from different data sources, was unknown. Objective: The objective of the study was to describe the feasibility of using PopMedNet and enhancements to PopMedNet to facilitate automatable vertical DRA (vDRA) in real-world settings. Methods: We gathered the statistical and informatic requirements of using PopMedNet to facilitate automatable vDRA. We enhanced PopMedNet based on these requirements to improve its technical capability to support vDRA. Results: PopMedNet can enable automatable vDRA. We identified and implemented two enhancements to PopMedNet that improved its technical capability to perform automatable vDRA in real-world settings. The first was the ability to simultaneously upload and download multiple files, and the second was the ability to directly transfer summary-level information between the data-contributing sites without a third-party analysis center. Conclusions: PopMedNet can be used to facilitate automatable vDRA to protect patient privacy and support clinical research in real-world settings.

Original languageEnglish (US)
Article numbere21459
JournalJMIR Medical Informatics
Volume9
Issue number4
DOIs
StatePublished - Apr 2021

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Health Information Management

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