TY - JOUR
T1 - Automatable distributed regression analysis of vertically partitioned data facilitated by PopMedNet
T2 - Feasibility and enhancement study
AU - Her, Qoua
AU - Kent, Thomas
AU - Samizo, Yuji
AU - Slavkovic, Aleksandra
AU - Vilk, Yury
AU - Toh, Sengwee
N1 - Funding Information:
This work was supported by the National Institute of Biomedical Imaging and Bioengineering (U01EB023683) and the National Center for Advancing Translational Sciences (UL1 TR002014) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© Qoua Her, Thomas Kent, Yuji Samizo, Aleksandra Slavkovic, Yury Vilk, Sengwee Toh.
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
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U2 - 10.2196/21459
DO - 10.2196/21459
M3 - Article
C2 - 33890866
AN - SCOPUS:85104897139
SN - 2291-9694
VL - 9
JO - JMIR Medical Informatics
JF - JMIR Medical Informatics
IS - 4
M1 - e21459
ER -