%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 9
%N 4
%P e21459
%T Automatable Distributed Regression Analysis of Vertically Partitioned Data Facilitated by PopMedNet: Feasibility and Enhancement Study
%A Her,Qoua
%A Kent,Thomas
%A Samizo,Yuji
%A Slavkovic,Aleksandra
%A Vilk,Yury
%A Toh,Sengwee
%+ Department of Population Medicine, Harvard Medical School, 401 Park Drive, 4th Floor East, Boston, MA, 02215, United States, 1 6178674885, qouaher@gmail.com
%K distributed regression analysis
%K distributed data networks
%K privacy-protecting analytics
%K vertically partitioned data
%K informatics
%K data networks
%K data
%D 2021
%7 23.4.2021
%9 Original Paper
%J JMIR Med Inform
%G English
%X 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. 
%M 33890866
%R 10.2196/21459
%U https://medinform.jmir.org/2021/4/e21459
%U https://doi.org/10.2196/21459
%U http://www.ncbi.nlm.nih.gov/pubmed/33890866