{"doi":"10.2196/21459","title":"Automatable Distributed Regression Analysis of Vertically Partitioned Data Facilitated by PopMedNet: Feasibility and Enhancement Study","abstract":"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.","journal":"JMIR Medical Informatics","year":2021,"id":204850,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9514,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":787704,"name":"Thomas Kent","orcid":"0000-0003-1247-9373","position":1,"is_corresponding":false},{"id":787705,"name":"Yuji Samizo","orcid":"0000-0001-8219-562X","position":2,"is_corresponding":false},{"id":787706,"name":"Aleksandra Slavković","orcid":"0000-0003-0497-1771","position":3,"is_corresponding":false},{"id":787707,"name":"Yury Vilk","orcid":"0000-0002-0192-2045","position":4,"is_corresponding":false},{"id":352754,"name":"Sengwee Toh","orcid":"0000-0002-5160-0810","position":5,"is_corresponding":false},{"id":787703,"name":"Qoua L. Her","orcid":"0000-0002-6427-1032","position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":null,"created_at":"2026-07-18T23:51:30.197308Z","pmid":"33890866","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}