2023
A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data
Yan Z, Zachrison K, Schwamm L, Estrada J, Duan R. A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data. PLOS ONE 2023, 18: e0280192. PMID: 36649349, PMCID: PMC9844867, DOI: 10.1371/journal.pone.0280192.Peer-Reviewed Original ResearchConceptsFederated algorithmPrivacy-preserving data integrationEHR dataElectronic health record dataComputation resource requirementsHealth record dataLongitudinal EHR dataPrivacy protectionData integrationResource requirementsMultiple healthcare facilitiesNumerical experimentsComputational efficiencyGeneralized linear mixed modelRecord dataCorrelated dataSite‐level heterogeneityAlgorithmNetworkSummary statisticsResearch NetworkLimited amountDatasetLinear mixed modelsGLMM
2016
Hospital Variation in Home-Time After Acute Ischemic Stroke
O'Brien E, Xian Y, Xu H, Wu J, Saver J, Smith E, Schwamm L, Peterson E, Reeves M, Bhatt D, Maisch L, Hannah D, Lindholm B, Olson D, Prvu Bettger J, Pencina M, Hernandez A, Fonarow G. Hospital Variation in Home-Time After Acute Ischemic Stroke. Stroke 2016, 47: 2627-2633. PMID: 27625383, DOI: 10.1161/strokeaha.116.013563.Peer-Reviewed Original ResearchConceptsIschemic strokeStroke survivorsStroke volumeAdmission volumePost dischargeAcute ischemic strokeIschemic stroke survivorsHighest priority outcomesMore comorbiditiesRegistry patientsSevere strokeHospital factorsAdjusted analysisHospital variationHospital characteristicsPost strokeMedicare claimsRural locationsHospitalStrokePatientsRisk adjustmentLinear mixed modelsFirst yearSurvivors