TY  - JOUR
AU  - Her, Qoua
AU  - Kent, Thomas
AU  - Samizo, Yuji
AU  - Slavkovic, Aleksandra
AU  - Vilk, Yury
AU  - Toh, Sengwee
PY  - 2021
DA  - 2021/4/23
TI  - Automatable Distributed Regression Analysis of Vertically Partitioned Data Facilitated by PopMedNet: Feasibility and Enhancement Study
JO  - JMIR Med Inform
SP  - e21459
VL  - 9
IS  - 4
KW  - distributed regression analysis
KW  - distributed data networks
KW  - privacy-protecting analytics
KW  - vertically partitioned data
KW  - informatics
KW  - data networks
KW  - data
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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2021/4/e21459
UR  - https://doi.org/10.2196/21459
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33890866
DO  - 10.2196/21459
ID  - info:doi/10.2196/21459
ER  -