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 techniquesHow Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?—A Survey of Estimands and Common Estimators
Lee K, Li F. How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?—A Survey of Estimands and Common Estimators. Biometrical Journal 2025, 67: e70052. PMID: 40302411, PMCID: PMC12041842, DOI: 10.1002/bimj.70052.Peer-Reviewed Original ResearchConceptsInformative cluster sizeIndependence estimating equationsCluster-period sizesParallel cluster randomized trialsTreatment effect estimatesCluster randomized trialInconsistent estimatesSimulation studyEstimandsEstimating equationsCluster sizeContinuous outcomesEstimationTreatment effectsEffect estimatesImprove mental healthRandomized trialsConvergenceEquationsRural eastern IndiaMental healthMixed-effects modelsYouth teamsEvidence-based personalised medicine in critical care: a framework for quantifying and applying individualised treatment effects in patients who are critically ill
Munroe E, Spicer A, Castellvi-Font A, Zalucky A, Dianti J, Linck E, Talisa V, Urner M, Angus D, Baedorf-Kassis E, Blette B, Bos L, Buell K, Casey J, Calfee C, Del Sorbo L, Estenssoro E, Ferguson N, Giblon R, Granholm A, Harhay M, Heath A, Hodgson C, Houle T, Jiang C, Kramer L, Lawler P, Leligdowicz A, Li F, Liu K, Maiga A, Maslove D, McArthur C, McAuley D, Neto A, Oosthuysen C, Perner A, Prescott H, Rochwerg B, Sahetya S, Samoilenko M, Schnitzer M, Seitz K, Shah F, Shankar-Hari M, Sinha P, Slutsky A, Qian E, Webb S, Young P, Zampieri F, Zarychanski R, Fan E, Semler M, Churpek M, Goligher E, investigators P, Group E. Evidence-based personalised medicine in critical care: a framework for quantifying and applying individualised treatment effects in patients who are critically ill. The Lancet Respiratory Medicine 2025, 13: 556-568. PMID: 40250459, DOI: 10.1016/s2213-2600(25)00054-2.Peer-Reviewed Original ResearchConceptsAverage treatment effectCritical careHeterogeneity of treatment effectsTreatment decisionsTreatment effectsCritical care syndromesResponse to treatmentClinical careRandomised clinical trialsCareRandomised trialsEffects of treatmentTreatment responseClinical trialsAggregate differencesPatientsOutcomesPersonalised medicineTreatmentEffect ITrialsA 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 analysisPower calculation for cross-sectional stepped wedge cluster randomized trials with a time-to-event endpoint
Baumann M, Esserman D, Taljaard M, Li F. Power calculation for cross-sectional stepped wedge cluster randomized trials with a time-to-event endpoint. Biometrics 2025, 81: ujaf074. PMID: 40557765, PMCID: PMC12188223, DOI: 10.1093/biomtc/ujaf074.Peer-Reviewed Original ResearchConceptsSW-CRTsCluster randomized trialStepped wedge cluster randomized trialTime-to-event endpointsTime-to-event outcomesRobust sandwich varianceMarginal Cox modelSandwich varianceWithin-periodElectronic reminder systemSW-CRTRandomized trialsBinary outcomesPower calculationsPower formulaReminder systemR Shiny applicationHospital settingCorrelation parametersSample sizePlanned trialsCox modelWaldFormulaTrialsBurden and care time for dementia caregivers in the LIVE@Home.Path trial
Berge L, Angeles R, Gedde M, Fæø S, Mannseth J, Vislapuu M, Puaschitz N, Hillestad E, Aarsland D, Achterberg W, Allore H, Ballard C, Li F, Selbæk G, Vahia I, Husebo B. Burden and care time for dementia caregivers in the LIVE@Home.Path trial. Alzheimer's & Dementia 2025, 21: e14622. PMID: 40042468, PMCID: PMC11881633, DOI: 10.1002/alz.14622.Peer-Reviewed Original ResearchConceptsRelative Stress ScaleInformal care timeCaregiver burdenLiving interventionsCare timeIntervention periodStepped wedge randomized controlled trialVolunteer supportHome-dwelling peopleStepped-wedge trialPersonal activitiesDementia caregiversRandomized controlled trialsIntention-to-treatStress ScaleMunicipal coordinatorCaregiversPersonal supportRandomized dyadsDementiaControlled trialsPrimary outcomeControl periodInterventionPositive changesGuidelines for the content of statistical analysis plans in clinical trials: protocol for an extension to cluster randomized trials
Hemming K, Thompson J, Hooper R, Ukoumunne O, Li F, Caille A, Kahan B, Leyrat C, Grayling M, Mohammed N, Thompson J, Giraudeau B, Turner E, Watson S, Goulão B, Kasza J, Forbes A, Copas A, Taljaard M. Guidelines for the content of statistical analysis plans in clinical trials: protocol for an extension to cluster randomized trials. Trials 2025, 26: 72. PMID: 40011934, PMCID: PMC11866560, DOI: 10.1186/s13063-025-08756-3.Peer-Reviewed Original ResearchAnalysis 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 practicesDepression and Low Social Support Mediate the Association of Marital Stress and 12-Month Cardiac-Specific Quality of Life in Young Adults With Acute Myocardial Infarction
Zhu C, Dreyer R, Li F, Spatz E, Caraballo C, Mahajan S, Raparelli V, Leifheit E, Lu Y, Krumholz H, Spertus J, D’Onofrio G, Pilote L, Lichtman J. Depression and Low Social Support Mediate the Association of Marital Stress and 12-Month Cardiac-Specific Quality of Life in Young Adults With Acute Myocardial Infarction. Biopsychosocial Science And Medicine 2025, 87: 129-137. PMID: 39909011, DOI: 10.1097/psy.0000000000001363.Peer-Reviewed Original ResearchConceptsLow social supportCardiac-specific quality of lifeNatural direct effectSocial supportMonths post-AMIQuality of lifeAcute myocardial infarctionMarital stressComprehensive secondary preventive strategySignificant depressive symptomsPost-AMISecondary prevention strategiesYoung adultsMyocardial infarctionSociodemographic factorsDepressive symptomsAMI survivorsCovariate adjustmentPrevention strategiesSelf-reportContinuous scoresQoLBaseline QoLCategorical depressionDepressionWeighting methods for truncation by death in cluster-randomized trials
Isenberg D, Harhay M, Mitra N, Li F. Weighting methods for truncation by death in cluster-randomized trials. Statistical Methods In Medical Research 2025, 34: 473-489. PMID: 39885759, PMCID: PMC11951466, DOI: 10.1177/09622802241309348.Peer-Reviewed Original ResearchConceptsSurvivor average causal effectAverage causal effectCluster randomized trialAsymptotic variance estimatorsSubgroup treatment effectsCausal effectsPrincipal stratification frameworkFinite-sampleVariance estimationDistributional assumptionsIdentification assumptionsStratification frameworkPatient-centered outcomesNon-mortality outcomesOutcome modelQuality of lifeRandomized trialsIll patient populationMeasurement time pointsTruncationEstimationLength of hospital stayAssumptionsSurvivorsPatient populationPrioritizing attributes of approaches to analyzing patient-centered outcomes that are truncated due to death in critical care clinical trials: a Delphi study
Bahti M, Kahan B, Li F, Harhay M, Auriemma C. Prioritizing attributes of approaches to analyzing patient-centered outcomes that are truncated due to death in critical care clinical trials: a Delphi study. Trials 2025, 26: 15. PMID: 39794867, PMCID: PMC11721323, DOI: 10.1186/s13063-024-08673-x.Peer-Reviewed Original ResearchConceptsCritical care clinical trialsPatient-centerednessDelphi roundsConsensus thresholdModified Delphi processCritical care trialsPatient-centered outcomesInvited individualsCare trialsExperience expertsDelphi processDelphi panelDelphi studyClinical trialsResearch teamResponse rateResultsThirty-oneClinical relevancePersonal experienceTrialsRespondentsCritical attributesDeathOutcomesAnalysis approachAddressing selection bias in cluster randomized experiments via weighting
Papadogeorgou G, Liu B, Li F, Li F. Addressing selection bias in cluster randomized experiments via weighting. Biometrics 2025, 81: ujaf013. PMID: 40052595, DOI: 10.1093/biomtc/ujaf013.Peer-Reviewed Original ResearchConceptsCluster-randomized experimentCluster randomized trialAverage treatment effectSelection biasInverse probability weightingOverall populationTreatment effectsCo-paymentControl armRecruited populationProbability weightingRandomized experimentRandomized trialsPopulationEstimation strategyTreatment assignmentIndividualsRecruitment assumptionR packageOverallAnalysis approachInterventionRecruitment
2024
Four targets: an enhanced framework for guiding causal inference from observational data
Lu H, Li F, Lesko C, Fink D, Rudolph K, Harhay M, Rentsch C, Fiellin D, Gonsalves G. Four targets: an enhanced framework for guiding causal inference from observational data. International Journal Of Epidemiology 2024, 54: dyaf003. PMID: 39868475, PMCID: PMC11769716, DOI: 10.1093/ije/dyaf003.Peer-Reviewed Original ResearchEstimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias
Ouyang Y, Li F, Li X, Bynum J, Mor V, Taljaard M. Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias. Trials 2024, 25: 732. PMID: 39478608, PMCID: PMC11523597, DOI: 10.1186/s13063-024-08404-2.Peer-Reviewed Original ResearchConceptsIntra-cluster correlation coefficientIntra-cluster correlation coefficient estimationSample size calculationED visitsMedicare dataMedicare fee-for-service beneficiariesEmergency departmentFee-for-service beneficiariesSize calculationDiagnosis of ADRDNational Medicare dataCluster randomized trialHospital referral regionsHospital service areasHealth care systemBackgroundCluster randomized trialsPopulation-level dataRandomized trialsDesign of cluster randomized trialsEvaluate interventionsReferral regionsCare systemICC estimatesADRDCorrelation coefficientHow to achieve model-robust inference in stepped wedge trials with model-based methods?
Wang B, Wang X, Li F. How to achieve model-robust inference in stepped wedge trials with model-based methods? Biometrics 2024, 80: ujae123. PMID: 39499239, PMCID: PMC11536888, DOI: 10.1093/biomtc/ujae123.Peer-Reviewed Original ResearchConceptsTreatment effect estimandsWorking correlation structureSandwich variance estimatorExchangeable working correlation structureFunction of calendar timeEffect estimandsVariance estimationLink functionStepped wedge trialEstimandsTheoretical resultsCorrelation structureWedge trialsEstimating equationsCluster randomized trialG-computationLinear mixed modelsInferencePotential outcomesMisspecificationEstimationEffective structureModel-based methodsGeneralized estimating equationsMixed modelsBayesian 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 ResearchConceptsAccelerated 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 mediatorsBrainPrepare Romania: study protocol for a randomized controlled trial of an intervention to promote pre-exposure prophylaxis adherence and persistence among gay, bisexual, and other men who have sex with men
Lelutiu-Weinberger C, Filimon M, Zavodszky A, Lixandru M, Hanu L, Fierbinteanu C, Patrascu R, Streinu-Cercel A, Luculescu S, Bora M, Filipescu I, Jianu C, Heightow-Weidman L, Rochelle A, Yi B, Buckner N, Golub S, van Dyk I, Burger J, Li F, Pachankis J. Prepare Romania: study protocol for a randomized controlled trial of an intervention to promote pre-exposure prophylaxis adherence and persistence among gay, bisexual, and other men who have sex with men. Trials 2024, 25: 470. PMID: 38987812, PMCID: PMC11238350, DOI: 10.1186/s13063-024-08313-4.Peer-Reviewed Original ResearchConceptsGBMSM livingPrEP adherenceRandomized controlled trial designMobile health interventionsRollout of PrEPMonths post-randomizationPre-exposure prophylaxisPre-exposure prophylaxis adherenceSelf-report surveyRandomized controlled trialsPromotion interventionsPrescribed PrEPEfficacy of toolsHealth interventionsNational rolloutHealth systemPrEP rolloutDiscussionThe knowledgeIntervention efficacyEffectiveness trialBlood spot testingPost-randomizationHigh-risk groupHIV transmissionDried blood spot testingOptimal 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 equationsMultiply robust estimation of principal causal effects with noncompliance and survival outcomes
Cheng C, Guo Y, Liu B, Wruck L, Li F, Li F. Multiply robust estimation of principal causal effects with noncompliance and survival outcomes. Clinical Trials 2024, 21: 553-561. PMID: 38813813, DOI: 10.1177/17407745241251773.Peer-Reviewed Original ResearchConceptsPrincipal strataRight-censored survival outcomesPrincipal causal effectsCausal effectsSensitivity analysis strategyPrincipal ignorabilityRobust estimationIdentification assumptionsCensoringPragmatic clinical trialsTreatment assignmentTreatment noncomplianceMonotonicityEstimationAssess treatment effectsCardiovascular diseaseClinical trialsMultipliersTreatment effectsAssumptionsNoncomplianceAssessing 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
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