2025
Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge.
Liu J, Li F, Sutcliffe S, Colditz G. Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge. Statistical Methods In Medical Research 2025, 9622802251360409. PMID: 40785501, DOI: 10.1177/09622802251360409.Peer-Reviewed Original ResearchSW-CRTsLongitudinal cluster randomized trialsStepped wedge cluster randomized trialCluster-period sizesTreatment effect estimatesCluster randomized trialContinuous outcomesGeneralized Estimating EquationsOptimal designCluster randomized designEffect estimatesFixed budgetCluster randomized trial designFormulaEquationsAlgorithmOptimal numberCross-sectional designGlobal optimal designEstimationRandomized trial designRandomized trialsStepped-wedgeCRXO trialsOD algorithm
2024
Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event
Tian X, Ciarleglio M, Cai J, Greene E, Esserman D, Li F, Zhao Y. Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2024, 73: 598-620. PMID: 39072299, PMCID: PMC11271983, DOI: 10.1093/jrsssc/qlae003.Peer-Reviewed Original ResearchSemi-parametric inferenceRecurrent eventsAccelerated failure time modelFailure time modelEfficient sampling algorithmFrailty distributionDirichlet processPosterior inferenceSampling algorithmTime modelTerminal eventSurvival processesComplex data structuresDirichletInferenceData structureFall injury preventionAlgorithm
2022
Simulating time-to-event data subject to competing risks and clustering: A review and synthesis
Meng C, Esserman D, Li F, Zhao Y, Blaha O, Lu W, Wang Y, Peduzzi P, Greene E. Simulating time-to-event data subject to competing risks and clustering: A review and synthesis. Statistical Methods In Medical Research 2022, 32: 305-333. PMID: 36412111, PMCID: PMC11654122, DOI: 10.1177/09622802221136067.Peer-Reviewed Original Research
2021
Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules
Shung D, Tsay C, Laine L, Chang D, Li F, Thomas P, Partridge C, Simonov M, Hsiao A, Tay JK, Taylor A. Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules. Journal Of Gastroenterology And Hepatology 2021, 36: 1590-1597. PMID: 33105045, PMCID: PMC11874507, DOI: 10.1111/jgh.15313.Peer-Reviewed Original ResearchConceptsNatural language processingElectronic health recordsLanguage processingNLP algorithmSystematized NomenclatureReal timeAcute gastrointestinal bleedingBidirectional Encoder RepresentationsDecision rulesEHR-based phenotyping algorithmsGastrointestinal bleedingRisk stratification scoresEncoder RepresentationsData elementsPhenotyping algorithmStratification scoresHealth recordsAlgorithmPhenotyping of patientsEmergency department patientsTime of presentationRisk stratification modelED reviewDeploymentExternal validation
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