{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T04:25:43Z","timestamp":1727238343153},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Patient-Centered Outcomes Research Institute (PCORI) Project Program Award","award":["ME-2019C3-18315"]},{"name":"PCORI, its Board of Governors or Methodology Committee"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,12]]},"abstract":"Abstract<\/jats:title>Objective<\/jats:title>To develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.<\/jats:p><\/jats:sec>Materials and Methods<\/jats:title>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.<\/jats:p><\/jats:sec>Results<\/jats:title>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.<\/jats:p><\/jats:sec>Conclusion<\/jats:title>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.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocac067","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T19:18:48Z","timestamp":1652210328000},"page":"1366-1371","source":"Crossref","is-referenced-by-count":10,"title":["dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling"],"prefix":"10.1093","volume":"29","author":[{"given":"Chongliang","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania , Philadelphia, Pennsylvania, USA"},{"name":"Division of Public Health Sciences, Washington University School of Medicine in St. Louis , St Louis, Missouri, USA"}]},{"given":"Md Nazmul","family":"Islam","sequence":"additional","affiliation":[{"name":"OptumLabs , Minnetonka, Minnesota, USA"}]},{"given":"Natalie E","family":"Sheils","sequence":"additional","affiliation":[{"name":"OptumLabs , Minnetonka, Minnesota, USA"}]},{"given":"John","family":"Buresh","sequence":"additional","affiliation":[{"name":"OptumLabs , Minnetonka, Minnesota, USA"}]},{"given":"Martijn J","family":"Schuemie","sequence":"additional","affiliation":[{"name":"Janssen Research and Development LLC , Titusville, New Jersey, USA"}]},{"given":"Jalpa A","family":"Doshi","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine, University of Pennsylvania , Philadelphia, Pennsylvania, USA"},{"name":"Leonard Davis Institute of Health Economics , Philadelphia, Pennsylvania, USA"}]},{"given":"Rachel M","family":"Werner","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine, University of Pennsylvania , Philadelphia, Pennsylvania, USA"},{"name":"Leonard Davis Institute of Health Economics , Philadelphia, Pennsylvania, USA"},{"name":"Cpl Michael J Crescenz VA Medical Center , Philadelphia, Pennsylvania, USA"}]},{"given":"David A","family":"Asch","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine, University of Pennsylvania , Philadelphia, Pennsylvania, USA"},{"name":"Leonard Davis Institute of Health Economics , Philadelphia, Pennsylvania, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0835-0788","authenticated-orcid":false,"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania , Philadelphia, Pennsylvania, USA"},{"name":"Leonard Davis Institute of Health Economics , Philadelphia, Pennsylvania, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"2022071310040055400_ocac067-B1","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1214\/088342307000000096","article-title":"Statistical and clinical aspects of hospital outcomes profiling","volume":"22","author":"Normand","year":"2007","journal-title":"Stat. 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