@Article{info:doi/10.2196/21459,
author="Her, Qoua
and Kent, Thomas
and Samizo, Yuji
and Slavkovic, Aleksandra
and Vilk, Yury
and Toh, Sengwee",
title="Automatable Distributed Regression Analysis of Vertically Partitioned Data Facilitated by PopMedNet: Feasibility and Enhancement Study",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="23",
volume="9",
number="4",
pages="e21459",
keywords="distributed regression analysis; distributed data networks; privacy-protecting analytics; vertically partitioned data; informatics; data networks; data",
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. ",
issn="2291-9694",
doi="10.2196/21459",
url="https://medinform.jmir.org/2021/4/e21459",
url="https://doi.org/10.2196/21459",
url="http://www.ncbi.nlm.nih.gov/pubmed/33890866"
}