WebDISCO: a web service for distributed cox model learning without patient-level data sharing.
Objective: The Cox proportional hazards model is a widely used method for analyzing survival data. To achieve sufficient statistical power in a survival analysis, it usually requires a large amount of data. Data sharing across institutions could be a potential workaround for providing this added pow...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 22; no. 6; pp. 1212 - 1220 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110875735&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110875735 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Nov2015 vid: 22 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 110875735 110875735 NLM26159465 110875735 10.1093/jamia/ocv083 NLM26159465 PMC5009917 [Available on 11/01/16] 110875735 ppf: 1212 ppct: 8 formats: tig: atl: WebDISCO: a web service for distributed cox model learning without patient-level data sharing. aug: au: Chia-Lun Lu Shuang Wang Zhanglong Ji Yuan Wu Li Xiong Xiaoqian Jiang Ohno-Machado, Lucila Lu, Chia-Lun Wang, Shuang Ji, Zhanglong Wu, Yuan Xiong, Li Jiang, Xiaoqian affil: Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA sug: subj: Algorithms Survival Analysis Cox Proportional Hazards Model Internet Computer Communication Networks Communication Methods Data Collection Decision Support Systems, Clinical Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source ab: Objective: The Cox proportional hazards model is a widely used method for analyzing survival data. To achieve sufficient statistical power in a survival analysis, it usually requires a large amount of data. Data sharing across institutions could be a potential workaround for providing this added power.Methods and Materials: The authors develop a web service for distributed Cox model learning (WebDISCO), which focuses on the proof-of-concept and algorithm development for federated survival analysis. The sensitive patient-level data can be processed locally and only the less-sensitive intermediate statistics are exchanged to build a global Cox model. Mathematical derivation shows that the proposed distributed algorithm is identical to the centralized Cox model.Results: The authors evaluated the proposed framework at the University of California, San Diego (UCSD), Emory, and Duke. The experimental results show that both distributed and centralized models result in near-identical model coefficients with differences in the range [Formula: see text] to [Formula: see text]. The results confirm the mathematical derivation and show that the implementation of the distributed model can achieve the same results as the centralized implementation.Limitation: The proposed method serves as a proof of concept, in which a publicly available dataset was used to evaluate the performance. The authors do not intend to suggest that this method can resolve policy and engineering issues related to the federated use of institutional data, but they should serve as evidence of the technical feasibility of the proposed approach.Conclusions WebDISCO (Web-based Distributed Cox Regression Model; https://webdisco.ucsd-dbmi.org:8443/cox/) provides a proof-of-concept web service that implements a distributed algorithm to conduct distributed survival analysis without sharing patient level data. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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