Distributed Regression Analysis Application in Large Distributed Data Networks: Analysis of Precision and Operational Performance.

Autor: Her Q; Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States., Malenfant J; Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States., Zhang Z; Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States., Vilk Y; Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States., Young J; Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States., Tabano D; Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, United States.; Center for Observational Research and Data Science, Bristol-Meyers Squibb, Lawrenceville, NJ, United States., Hamilton J; Division of Research, Kaiser Permanete North California, Oakland, CA, United States., Johnson R; Health Research Institute, Kaiser Permanente Washington, Seattle, WA, United States., Raebel M; Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, United States., Boudreau D; Health Research Institute, Kaiser Permanente Washington, Seattle, WA, United States., Toh S; Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States.
Jazyk: angličtina
Zdroj: JMIR medical informatics [JMIR Med Inform] 2020 Jun 04; Vol. 8 (6), pp. e15073. Date of Electronic Publication: 2020 Jun 04.
DOI: 10.2196/15073
Abstrakt: Background: A distributed data network approach combined with distributed regression analysis (DRA) can reduce the risk of disclosing sensitive individual and institutional information in multicenter studies. However, software that facilitates large-scale and efficient implementation of DRA is limited.
Objective: This study aimed to assess the precision and operational performance of a DRA application comprising a SAS-based DRA package and a file transfer workflow developed within the open-source distributed networking software PopMedNet 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 3 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 with 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 min 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 multicenter studies.
(©Qoua Her, Jessica Malenfant, Zilu Zhang, Yury Vilk, Jessica Young, David Tabano, Jack Hamilton, Ron Johnson, Marsha Raebel, Denise Boudreau, Sengwee Toh. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 04.06.2020.)
Databáze: MEDLINE
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