JMIR Publications

ABSTRACT

Background:

In clinical research important variables may be collected in multiple data sources. Physical pooling of patient-level data across multiple sources often raises several challenges, including proper protection of patient privacy and proprietary interest. We previously developed a SAS-based package to perform distributed regression analysis (DRA), a privacy-protecting method that performs multivariable-adjusted regression analysis with only summary-level information, with horizontally partitioned data, a setting where distinct cohorts of patients are available in different data sources. We integrated the package with PopMedNet, an open-source file transfer software, to facilitate information exchange between the analysis center and the data-contributing sites. The feasibility of using PopMedNet to facilitate DRA with vertically partitioned data, a setting where the data attributes of a cohort of patients are available in different data sources, was unknown.

Objective:

To describe the feasibility of and enhancements to PopMedNet to facilitate vertical DRA (vDRA) in the real-world setting.

Methods:

We gathered the statistical and informatic requirements of using PopMedNet to facilitate automatable vDRA. We implemented enhancements to PopMedNet to improve its technical capability to facilitate 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 the real-world setting. The first was the ability to simultaneously upload and download multiple files, and second was the ability to directly transfer summary-level information between the data sources without a semi-trusted third party.

Conclusions:

PopMedNet can be used to facilitate automatable vDRA to protect patient privacy and support clinical research discoveries in the real-world setting.