2024
Bayesian pathway analysis over brain network mediators for survival data
Tian X, Li F, Shen L, Esserman D, Zhao Y. Bayesian pathway analysis over brain network mediators for survival data. Biometrics 2024, 80: ujae132. PMID: 39530270, PMCID: PMC11555425, DOI: 10.1093/biomtc/ujae132.Peer-Reviewed Original ResearchMeSH KeywordsAlzheimer DiseaseBayes TheoremBrainComputer SimulationHumansModels, StatisticalNerve NetNeuroimagingSurvival AnalysisConceptsAccelerated failure time modelFailure time modelBrain connectivityAlzheimer's Disease Neuroimaging Initiative studyMaximum information extractionResponse regressionBayesian approachInformation extractionTime modelSurvival dataNoisy componentsUnique edgeWhite matter fiber tractsNetwork configurationBrain networksInterconnection networksNetworkNetwork mediatorsBrainAssessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials
Blette B, Halpern S, Li F, Harhay M. Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials. Statistical Methods In Medical Research 2024, 33: 909-927. PMID: 38567439, PMCID: PMC11041086, DOI: 10.1177/09622802241242323.Peer-Reviewed Original ResearchConceptsMultilevel multiple imputationHeterogeneous treatment effectsCluster randomized trialPotential effect modifiersMultiple imputationAssess treatment effect heterogeneityEffect modifiersTreatment effect heterogeneityComplete-case analysisMissingness mechanismIntracluster correlationSimulation studyUnder-coverageRandomized trialsEffect heterogeneityHealth StudyTreatment effectsContinuous outcomesClinical practiceImputationModel specificationMissingnessData methodsModified dataTrials
2023
Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia
Blette B, Granholm A, Li F, Shankar-Hari M, Lange T, Munch M, Møller M, Perner A, Harhay M. Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia. Scientific Reports 2023, 13: 6570. PMID: 37085591, PMCID: PMC10120498, DOI: 10.1038/s41598-023-33425-3.Peer-Reviewed Original ResearchConceptsCritical COVID-19Better long-term outcomesCOVID-19Entire trial populationStandardized dosing protocolsMultiple patient characteristicsDose of dexamethasoneLong-term outcomesIL-6 inhibitorsDexamethasone doseSevere hypoxemiaMost patientsPatient characteristicsRespiratory supportDiabetes mellitusClinical outcomesDosing protocolTrial populationTreatment effect heterogeneityPatient featuresPatientsAdditional studiesDexamethasoneDoseMore evidenceA Bayesian Approach for Estimating the Survivor Average Causal Effect When Outcomes Are Truncated by Death in Cluster-Randomized Trials
Tong G, Li F, Chen X, Hirani S, Newman S, Wang W, Harhay M. A Bayesian Approach for Estimating the Survivor Average Causal Effect When Outcomes Are Truncated by Death in Cluster-Randomized Trials. American Journal Of Epidemiology 2023, 192: 1006-1015. PMID: 36799630, PMCID: PMC10236525, DOI: 10.1093/aje/kwad038.Peer-Reviewed Original Research
2021
A Bayesian approach for estimating the partial potential impact fraction with exposure measurement error under a main study/internal validation design
Chen X, Chang J, Spiegelman D, Li F. A Bayesian approach for estimating the partial potential impact fraction with exposure measurement error under a main study/internal validation design. Statistical Methods In Medical Research 2021, 31: 404-418. PMID: 34841964, DOI: 10.1177/09622802211060514.Peer-Reviewed Original ResearchConceptsPotential impact fractionImpact fractionExposure measurement errorHealth professionalsStudy designColorectal cancer incidenceValidation study designBurden of diseaseRisk factorsCancer incidenceHealth StudyDisease casesPublic health studiesRed meatContinuous exposureExposureProfessionalsIncidenceReclassification approachValidation designDiseaseIntakeImpact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial
Tong G, Seal KH, Becker WC, Li F, Dziura JD, Peduzzi PN, Esserman DA. Impact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial. Clinical Trials 2021, 19: 3-13. PMID: 34693748, PMCID: PMC8847260, DOI: 10.1177/17407745211051288.Peer-Reviewed Original ResearchConceptsGroup treatment trialsIntraclass correlation coefficientTreatment trialsTreatment sessionsHealth optionsEducation trialThree-armWhole health teamFuture trial designNumber of cliniciansGroup treatment designTrue intraclass correlation coefficientsGroup treatment sessionsTreatment armsClinical trialsClinician levelMultiple cliniciansBACKGROUND/Health teamsOutcome dataTreatment groupsTrial designGroup educationClinical scenariosDifferent cliniciansEstimating heterogeneous survival treatment effect in observational data using machine learning
Hu L, Ji J, Li F. Estimating heterogeneous survival treatment effect in observational data using machine learning. Statistics In Medicine 2021, 40: 4691-4713. PMID: 34114252, PMCID: PMC9827499, DOI: 10.1002/sim.9090.Peer-Reviewed Original Research
2019
Bayesian estimation of genetic regulatory effects in high-throughput reporter assays
Majoros WH, Kim YS, Barrera A, Li F, Wang X, Cunningham SJ, Johnson GD, Guo C, Lowe WL, Scholtens DM, Hayes MG, Reddy TE, Allen AS. Bayesian estimation of genetic regulatory effects in high-throughput reporter assays. Bioinformatics 2019, 36: 331-338. PMID: 31368479, PMCID: PMC7999138, DOI: 10.1093/bioinformatics/btz545.Peer-Reviewed Original ResearchConceptsHigh-throughput reporterBiological replicatesGenetic regulatory effectsAdditional biological replicatesGenetic association testsAllelic effectsReporter geneGene expressionSequencing coverageDisease mutationsDisease-causing variantsRegulatory differencesLinkage disequilibriumGenetic variantsExperimental replicatesRegulatory effectsPoisson binomial distributionAllele frequenciesPatient's DNASupplementary dataBayesian hierarchical modelBayesian estimationRare variantsReporterDNA
2017
Bayesian Word Learning in Multiple Language Environments
Zinszer BD, Rolotti SV, Li F, Li P. Bayesian Word Learning in Multiple Language Environments. Cognitive Science 2017, 42: 439-462. PMID: 29154481, DOI: 10.1111/cogs.12567.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremChild LanguageChild, PreschoolHumansLanguage DevelopmentMultilingualismVerbal LearningVocabularyConceptsMutual exclusivity biasChild-directed speechReferential intentionsInfant language learnersSpeaker’s referential intentionsWord learningBayesian inference modelBilingual inputNew wordsCandidate lexiconsLanguage learnersDifferential demandsInductive problemsLanguage environmentLearning situationsBayesian modelIntentional modelSame corpusComputational modelSpeechWordsInference modelBilingual corpusSuch contextsCorpus
2015
A spatiotemporal quantile regression model for emergency department expenditures
Neelon B, Li F, Burgette LF, Neelon SE. A spatiotemporal quantile regression model for emergency department expenditures. Statistics In Medicine 2015, 34: 2559-2575. PMID: 25782041, DOI: 10.1002/sim.6480.Peer-Reviewed Original ResearchConceptsQuantile regression modelSmall area estimationAsymmetric Laplace distributionSpatiotemporal random effectsFull conditionalsRandom effectsBayesian modeling approachLaplace distributionAutoregressive priorsSampling schemeResponse distributionEmergency department expendituresSpatiotemporal smoothingModeling approach