2025
A Bayesian Approach to the G‐Formula via Iterative Conditional Regression
Liu R, Hu L, Wilson F, Warren J, Li F. A Bayesian Approach to the G‐Formula via Iterative Conditional Regression. Statistics In Medicine 2025, 44: e70123. PMID: 40476299, PMCID: PMC12184534, DOI: 10.1002/sim.70123.Peer-Reviewed Original ResearchConceptsCausal effect estimationTime-varying covariatesModel misspecification biasBayesian approachReal world data examplesG-formulaAverage causal effect estimationTime-varying treatmentsBayesian additive regression treesAverage causal effectAdditive regression treesConditional expectationOutcome regressionConditional distributionJoint distributionData examplesPosterior distributionMisspecification biasParametric regressionSimulation studyEffect estimatesSampling algorithmAlgorithm formulaCausal effectsFlexible machine learning techniquesA flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
Chen X, Hu L, Li F. A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding. Lifetime Data Analysis 2025, 31: 394-421. PMID: 40227517, DOI: 10.1007/s10985-025-09652-3.Peer-Reviewed Original ResearchConceptsG-formulaBalance scoresHealth system electronic health recordDiscrete survival dataTime-to-event outcomesPosterior sampling algorithmParametric g-formulaElectronic health recordsBayesian additive regression treesTime-varying treatmentsHypothetical intervention scenariosAdditive regression treesLongitudinal observational studyGeneral classModel misspecificationHealth recordsEmpirical performanceSampling algorithmObservational studySurvival dataIntervention scenariosScoresTreatment strategiesMisspecificationCausality analysis
2024
Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials
Liu J, Li F. Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials. Statistical Methods In Medical Research 2024, 33: 1299-1330. PMID: 38813761, DOI: 10.1177/09622802241247717.Peer-Reviewed Original ResearchConceptsMaximin optimal designsStepped wedge cluster randomized trialLocally optimal designsCluster-period sizesClosed-form formulaCluster-randomized crossover trialCross-sectional sampling schemeInteger estimationOptimal design algorithmDesign algorithmLongitudinal cluster randomized trialsWorking correlation structureCluster randomized trialMethod of generalized estimating equationsTreatment effect estimatesSAS macroVariance expressionsExact valueCorrelation structureMaximinSampling schemeBetween-clusterOptimal designOptimization design researchEstimating Equations
2021
Sample Size Considerations for Stepped Wedge Designs with Subclusters
Davis-Plourde K, Taljaard M, Li F. Sample Size Considerations for Stepped Wedge Designs with Subclusters. Biometrics 2021, 79: 98-112. PMID: 34719017, PMCID: PMC9054939, DOI: 10.1111/biom.13596.Peer-Reviewed Original ResearchEarly 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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