Learning from local to global: An efficient distributed algorithm for modeling time-to-event data.
Objective: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites.Materials and Methods: Using patient-level data from a single site combined with only aggregated...
| Published in: | Journal of the American Medical Informatics Association Vol. 27; no. 7; pp. 1028 - 1037 |
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| Main Authors: | , , , , , , , , , , , , , , , |
| Format: | research Journal Article |
| Published: |
Oxford University Press / USA
Jul2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144757536&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144757536 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: Jul2020 vid: 27 iid: 7 pid: 622 pub: Oxford University Press / USA artinfo: ui: 144757536 144757536 NLM32626900 144757536 10.1093/jamia/ocaa044 NLM32626900 144757536 ppf: 1028 ppct: 9 formats: tig: atl: Learning from local to global: An efficient distributed algorithm for modeling time-to-event data. aug: au: Duan, Rui Luo, Chongliang Schuemie, Martijn J Tong, Jiayi Liang, C Jason Chang, Howard H Boland, Mary Regina Bian, Jiang Xu, Hua Holmes, John H Forrest, Christopher B Morton, Sally C Berlin, Jesse A Moore, Jason H Mahoney, Kevin B Chen, Yong affil: Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania , Philadelphia, Pennsylvania, USA sug: subj: Cox Proportional Hazards Model Algorithms Time Factors Aged Adult Sample Size Middle Age Male Human Probability Data Collection Computer Simulation Female Models, Statistical Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Aged: 65+ years Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objective: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites.Materials and Methods: Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network.Results: On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC.Conclusions: ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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