dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling.
Objective: To develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.Materials and Methods: The GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 29; no. 8; pp. 1366 - 1372 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Aug2022
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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=158017522&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158017522 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: Aug2022 vid: 29 iid: 8 pid: 622 pub: Oxford University Press / USA artinfo: ui: 158017522 158017522 NLM35579348 10.1093/jamia/ocac067 NLM35579348 158017522 ppf: 1366 ppct: 6 formats: tig: atl: dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling. aug: au: Luo, Chongliang Islam, Md Nazmul Sheils, Natalie E Buresh, John Schuemie, Martijn J Doshi, Jalpa A Werner, Rachel M Asch, David A Chen, Yong affil: Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania , Philadelphia, Pennsylvania, USA sug: ab: Objective: To develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.Materials and Methods: The GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual patient data (IPD) privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed penalized quasi-likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than IPD, can be shared across hospitals. We also show that the standardized mortality rates, which are often reported as the results of hospital profiling, can also be calculated distributively without sharing IPD. We demonstrate the applicability of the proposed dPQL algorithm by ranking 929 hospitals for coronavirus disease 2019 (COVID-19) mortality or referral to hospice that have been previously studied.Results: The proposed dPQL algorithm is mathematically proven to be lossless, that is, it obtains identical results as if IPD were pooled from all hospitals. In the example of hospital profiling regarding COVID-19 mortality, the dPQL algorithm reached convergence with only 5 iterations, and the estimation of fixed effects, random effects, and mortality rates were identical to that of the PQL from pooled data.Conclusion: The dPQL algorithm is lossless, privacy-preserving and fast-converging for fitting GLMM. It provides an extremely suitable and convenient distributed approach for hospital profiling. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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