JMIR Publications

ABSTRACT

Background:

A distributed data network approach combined with distributed regression analysis (DRA) can reduce the risk of disclosing sensitive individual and institutional information in multi-center studies. However, software that facilitate large-scale and efficient implementation of DRA are limited.

Objective:

To assess the precision and operational performance of a DRA application comprised of a SAS-based DRA package and a file transfer workflow developed within the open-source distributed networking software PopMedNetTM in a horizontally partitioned distributed data network.

Methods:

We executed the SAS-based DRA package to perform distributed linear, logistic, and Cox proportional hazards regression analysis on a real-world test case with three data partners. We used PopMedNet to iteratively and automatically transfer highly summarized information between the data partners and the analysis center. We compared the DRA results to the results from standard SAS procedures executed on the pooled individual-level dataset to evaluate the precision of the SAS-based DRA package. We computed the execution time of each step in the workflow to evaluate the operational performance of the PopMedNet-driven file transfer workflow.

Results:

All DRA results were precise (< 10-12) and DRA model fit curves were identical or similar to those obtained from the corresponding pooled individual-level data analyses. All regression models required less than 20 minutes for full end-to-end execution.

Conclusions:

We integrated a SAS-based DRA package with PopMedNet and successfully tested the new capability within an active distributed data network. The study demonstrated the validity and feasibility of using DRA to enable more privacy-protecting analysis in multi-center studies.