المؤلفون: Lu, Chia-Lun, Wang, Shuang, Ji, Zhanglong, Wu, Yuan, Xiong, Li, Jiang, Xiaoqian, Ohno-Machado, Lucila

المصدر: Journal of the American Medical Informatics Association. 22(6)

مصطلحات موضوعية: Distributed Computing and Systems Software, Information and Computing Sciences, Bioengineering, Health and social care services research, 8.4 Research design and methodologies (health services), Algorithms, Computer Communication Networks, Datasets as Topic, Decision Support Systems, Clinical, Humans, Information Dissemination, Internet, Proportional Hazards Models, Survival Analysis, clinical information systems, decision support systems, distributed modeling, cox model, Engineering, Medical and Health Sciences, Medical Informatics, Biomedical and clinical sciences, Health sciences, Information and computing sciences

الوصف: ObjectiveThe 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 materialsThe 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.ResultsThe 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.LimitationThe 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/coxTest/) provides a proof-of-concept web service that implements a distributed algorithm to conduct distributed survival analysis without sharing patient level data.

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/1br1w1z6Test

المؤلفون: 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

المصدر: Journal of the American Medical Informatics Association; Nov2015, Vol. 22 Issue 6, p1212-1219, 8p, 1 Color Photograph, 1 Diagram, 5 Charts, 2 Graphs

مستخلص: 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/coxTest/) provides a proof-of-concept web service that implements a distributed algorithm to conduct distributed survival analysis without sharing patient level data. [ABSTRACT FROM AUTHOR]

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