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 analysisAnalysis of Cohort Stepped Wedge Cluster‐Randomized Trials With Nonignorable Dropout via Joint Modeling
Gasparini A, Crowther M, Hoogendijk E, Li F, Harhay M. Analysis of Cohort Stepped Wedge Cluster‐Randomized Trials With Nonignorable Dropout via Joint Modeling. Statistics In Medicine 2025, 44: e10347. PMID: 39963907, PMCID: PMC11833761, DOI: 10.1002/sim.10347.Peer-Reviewed Original ResearchConceptsStepped wedge cluster randomized trialDropout processNonignorable missing outcomesParallel-arm cluster-randomized trialsCluster randomized trialNonignorable dropoutsJoint longitudinal-survival modelLongitudinal submodelData-generating scenariosMissingness patternsJoint modeling methodologyCorrelation structureMonte Carlo simulationsLongitudinal outcomesJoint modelEffective parametrizationPrimary care practicesGeriatric care modelsCarlo simulationsFrail older adultsAssociation structureSubmodelsCare modelUsual careCare practices
2023
ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals
Meng C, Ryan M, Rathouz P, Turner E, Preisser J, Li F. ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals. Computer Methods And Programs In Biomedicine 2023, 237: 107567. PMID: 37207384, DOI: 10.1016/j.cmpb.2023.107567.Peer-Reviewed Original ResearchMeSH KeywordsBiasCluster AnalysisComputer SimulationHumansLogistic ModelsLongitudinal StudiesModels, StatisticalConceptsOrdinal outcomesSandwich estimatorR packageSimulation studyCorrelated ordinal dataFinite sample biasesNumber of clustersCovariance estimationMarginal modelsEquationsParameter estimatesOrdinal responsesAssociation parametersCluster associationsBias correctionOrdinal dataEstimatorEstimating EquationsNominal levelMarginal meansResidualsEstimationPairwise odds ratiosAssociation modelGEE modelGEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials
Zhang Y, Preisser J, Li F, Turner E, Toles M, Rathouz P. GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials. Computer Methods And Programs In Biomedicine 2023, 230: 107362. PMID: 36709555, PMCID: PMC10037297, DOI: 10.1016/j.cmpb.2023.107362.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationLongitudinal StudiesModels, StatisticalRandomized Controlled Trials as TopicConceptsNumber of clustersBias-corrected estimationCorrelation structurePopulation-averaged interpretationMarginal regression modelsDeletion diagnosticsEstimating EquationsFinite-sample adjustmentInfluence of observationsLarge valuesStandard errorEquationsSandwich estimatorVariance estimatorCook's distanceSAS macroDesign of clusterCount outcomesLongitudinal responseCorrelation parametersValid inferencesCorrelated outcomesFlexible specificationBiased estimatesEstimator
2021
Assessment of Acute Kidney Injury and Longitudinal Kidney Function After Hospital Discharge Among Patients With and Without COVID-19
Nugent J, Aklilu A, Yamamoto Y, Simonov M, Li F, Biswas A, Ghazi L, Greenberg J, Mansour S, Moledina D, Wilson FP. Assessment of Acute Kidney Injury and Longitudinal Kidney Function After Hospital Discharge Among Patients With and Without COVID-19. JAMA Network Open 2021, 4: e211095. PMID: 33688965, PMCID: PMC7948062, DOI: 10.1001/jamanetworkopen.2021.1095.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedAged, 80 and overBlack or African AmericanCohort StudiesComorbidityCOVID-19CreatinineFemaleFollow-Up StudiesGlomerular Filtration RateHispanic or LatinoHumansHypertensionKidney Function TestsLongitudinal StudiesMaleMiddle AgedPatient DischargeProportional Hazards ModelsRenal Insufficiency, ChronicRetrospective StudiesSARS-CoV-2United StatesConceptsCOVID-19-associated acute kidney injuryAcute kidney injuryHospital acute kidney injurySubgroup of patientsKidney functionKidney injuryCohort studyHospital dischargeAKI recoveryKidney diseaseCOVID-19Peak creatinine levelsRetrospective cohort studyChronic kidney diseaseDays of dischargeHalf of patientsGlomerular filtration rateCoronavirus disease 2019AKI severityBaseline comorbiditiesEGFR decreaseDialysis requirementEGFR slopeKidney recoveryCreatinine levels
2019
Predicting the Risk of Huntington’s Disease with Multiple Longitudinal Biomarkers
Li F, Li K, Li C, Luo S, Group P. Predicting the Risk of Huntington’s Disease with Multiple Longitudinal Biomarkers. Journal Of Huntington's Disease 2019, 8: 323-332. PMID: 31256145, PMCID: PMC6718328, DOI: 10.3233/jhd-190345.Peer-Reviewed Original ResearchMeSH KeywordsAdultBiomarkersDisease ProgressionFemaleHumansHuntington DiseaseKaplan-Meier EstimateLongitudinal StudiesMaleMiddle AgedProspective StudiesRisk FactorsConceptsEnroll-HDRisk of Huntington's diseasePREDICT-HDRisk of HDMultiple longitudinal markersTime to diagnosisHuntington's diseasePatient's risk categoryPrognostic indexPrognostic scorePrognostic modelHD diagnosisRisk predictionPublic health threatCox modelRisk categoriesLongitudinal measurementsHealth threatRiskLongitudinal biomarkersScoresBiomarker measurementsPatient selectionClinical trialsClinical biomarkers
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