2025
How 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 ResearchMeSH KeywordsCritical CareCritical IllnessEvidence-Based MedicineHumansPrecision MedicineRandomized Controlled Trials as TopicRespiratory Distress SyndromeTreatment OutcomeConceptsAverage treatment effectCritical careHeterogeneity of treatment effectsTreatment decisionsTreatment effectsCritical care syndromesResponse to treatmentClinical careRandomised clinical trialsCareRandomised trialsEffects of treatmentTreatment responseClinical trialsAggregate differencesPatientsOutcomesPersonalised medicineTreatmentEffect ITrialsPower 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 ResearchMeSH KeywordsBiometryCluster AnalysisComputer SimulationCross-Sectional StudiesEndpoint DeterminationHumansProportional Hazards ModelsRandomized Controlled Trials as TopicConceptsSW-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 modelWaldFormulaTrialsGuidelines 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 ResearchMeSH KeywordsCluster AnalysisConsensusData Interpretation, StatisticalGuidelines as TopicHumansRandomized Controlled Trials as TopicResearch DesignAnalysis 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 ResearchMeSH KeywordsAgedCluster AnalysisCohort StudiesComputer SimulationHumansLongitudinal StudiesModels, StatisticalPatient DropoutsRandomized Controlled Trials as TopicSurvival AnalysisConceptsStepped 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 practicesWeighting 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 ResearchMeSH KeywordsCluster AnalysisHumansModels, StatisticalQuality of LifeRandomized Controlled Trials as TopicConceptsSurvivor 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 populationAddressing 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 ResearchMeSH KeywordsCluster AnalysisComputer SimulationHumansModels, StatisticalPatient SelectionRandomized Controlled Trials as TopicSelection BiasConceptsCluster-randomized experimentCluster randomized trialAverage treatment effectSelection biasInverse probability weightingOverall populationTreatment effectsCo-paymentControl armRecruited populationProbability weightingRandomized experimentRandomized trialsPopulationEstimation strategyTreatment assignmentIndividualsRecruitment assumptionR packageOverallAnalysis approachInterventionRecruitment
2024
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
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 ResearchMeSH KeywordsAgedAged, 80 and overAlzheimer DiseaseCluster AnalysisDementiaEmergency Service, HospitalFemaleHospitalizationHumansMaleMedicareRandomized Controlled Trials as TopicResearch DesignSample SizeUnited StatesConceptsIntra-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 ResearchMeSH KeywordsBiometryComputer SimulationCross-Over StudiesData Interpretation, StatisticalHumansLinear ModelsModels, StatisticalRandomized Controlled Trials as TopicResearch DesignConceptsTreatment 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 modelsPrepare 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 ResearchMeSH KeywordsAlgorithmsCluster AnalysisCross-Over StudiesHumansModels, StatisticalRandomized Controlled Trials as TopicResearch DesignConceptsMaximin 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 equationsAssessing 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 ResearchMeSH KeywordsBayes TheoremBiasCluster AnalysisComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalRandomized Controlled Trials as TopicTreatment OutcomeConceptsMultilevel 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
A mixed model approach to estimate the survivor average causal effect in cluster‐randomized trials
Wang W, Tong G, Hirani S, Newman S, Halpern S, Small D, Li F, Harhay M. A mixed model approach to estimate the survivor average causal effect in cluster‐randomized trials. Statistics In Medicine 2023, 43: 16-33. PMID: 37985966, DOI: 10.1002/sim.9939.Peer-Reviewed Original ResearchAgedHumansModels, StatisticalOutcome Assessment, Health CareQuality of LifeRandomized Controlled Trials as TopicSurvivorsR ealtime Diagnosis from E lectrocardiogram Artificial Intelligence- G uided Screening for A trial Fibrillation with L ong Follow-Up (REGAL): Rationale and design of a pragmatic, decentralized, randomized controlled trial
Yao X, Attia Z, Behnken E, Hart M, Inselman S, Weber K, Li F, Stricker N, Stricker J, Friedman P, Noseworthy P. R ealtime Diagnosis from E lectrocardiogram Artificial Intelligence- G uided Screening for A trial Fibrillation with L ong Follow-Up (REGAL): Rationale and design of a pragmatic, decentralized, randomized controlled trial. American Heart Journal 2023, 267: 62-69. PMID: 37913853, DOI: 10.1016/j.ahj.2023.10.005.Peer-Reviewed Original ResearchMeSH KeywordsAgedArtificial IntelligenceAtrial FibrillationElectrocardiographyFollow-Up StudiesHumansPragmatic Clinical Trials as TopicRandomized Controlled Trials as TopicStrokeConceptsConsumer wearable devicesOlder adultsUnrecognized AFAssociated with increased risk of strokeApple WatchFollow-upAtrial fibrillationAssociated with increased riskEarly diagnosis of AFAll-cause mortalityRandomized controlled trialsCognitive function declineEarly diagnosisIntervention armPragmatic trialRisk of strokeDiagnosis of AFAI-ECGDisease preventionSecondary outcomesFunctional declineDisease burdenPrimary outcomeLong-term follow-upHigh-risk patientsSample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes
Maleyeff L, Wang R, Haneuse S, Li F. Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes. Statistics In Medicine 2023, 42: 5054-5083. PMID: 37974475, PMCID: PMC10659142, DOI: 10.1002/sim.9901.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationHumansLinear ModelsMonte Carlo MethodRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsSample size proceduresSize proceduresEfficient Monte Carlo approachTreatment effect heterogeneitySample size methodsMonte Carlo approachContinuous effect modifiersBinary outcomesEffect heterogeneityCarlo approachNumerical illustrationsNecessary sample sizeGeneralized linear mixed modelLinear mixed modelsPopular classSample size requirementsStatistical powerAverage treatment effectHeterogeneous treatment effectsSample size calculationMixed modelsSize methodSize calculationSize requirementsCluster Randomized TrialMaximin optimal cluster randomized designs for assessing treatment effect heterogeneity
Ryan M, Esserman D, Li F. Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity. Statistics In Medicine 2023, 42: 3764-3785. PMID: 37339777, PMCID: PMC10510425, DOI: 10.1002/sim.9830.Peer-Reviewed Original ResearchSample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances
Tong G, Taljaard M, Li F. Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances. Statistics In Medicine 2023, 42: 3392-3412. PMID: 37316956, DOI: 10.1002/sim.9811.Peer-Reviewed Original ResearchConceptsGroup treatment trialsTreatment effect modificationRandomized trialsTreatment trialsEffect modificationEffect modifiersIntracluster correlation coefficientIndividual-level effect modifiersStudy armsTreatment effect heterogeneityOutcome observationsContinuous outcomesTrialsGroup treatmentTreatment effectsOutcome varianceEffect heterogeneityIntracluster correlationSample sizeSample size formulaIs low-risk status a surrogate outcome in pulmonary arterial hypertension? An analysis of three randomised trials
Blette B, Moutchia J, Al-Naamani N, Ventetuolo C, Cheng C, Appleby D, Urbanowicz R, Fritz J, Mazurek J, Li F, Kawut S, Harhay M. Is low-risk status a surrogate outcome in pulmonary arterial hypertension? An analysis of three randomised trials. The Lancet Respiratory Medicine 2023, 11: 873-882. PMID: 37230098, PMCID: PMC10592525, DOI: 10.1016/s2213-2600(23)00155-8.Peer-Reviewed Original ResearchMeSH KeywordsEpoprostenolFamilial Primary Pulmonary HypertensionFemaleHumansMaleMiddle AgedPulmonary Arterial HypertensionRandomized Controlled Trials as TopicRisk FactorsConceptsPulmonary arterial hypertensionPulmonary arterial hypertension trialsWorsening pulmonary arterial hypertensionFood and Drug AdministrationLow-risk statusClinical worseningLong-term outcomesRisk scoreArterial hypertensionPAH associated with connective tissue diseaseIdiopathic pulmonary arterial hypertensionPulmonary arterial hypertension treatmentSurrogate outcomesObservational study of outcomesLong-term follow-upDiscontinuation of study treatmentWHO functional classUS Food and Drug AdministrationMeta-analysisMeta-analysis of RCTsAll-cause deathConnective tissue diseaseEffects of therapyPredictive of outcomeTreatment effectsAccounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity
Tong J, Li F, Harhay M, Tong G. Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity. BMC Medical Research Methodology 2023, 23: 85. PMID: 37024809, PMCID: PMC10077680, DOI: 10.1186/s12874-023-01887-8.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsSample size methodsSample size proceduresSize proceduresTreatment effect heterogeneityHeterogeneous treatment effectsSize methodMissingness ratesSample size formulaSample size estimationMissingness indicatorsEffect heterogeneityReal-world examplesSimulation studyIntracluster correlation coefficientInflation methodSize formulaAverage treatment effectResultsSimulation resultsSample size estimatesSize estimationMissingnessSample sizeClustersEstimationFormulaAccounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control
Ouyang Y, Kulkarni M, Protopopoff N, Li F, Taljaard M. Accounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control. BMC Medical Research Methodology 2023, 23: 64. PMID: 36932347, PMCID: PMC10021932, DOI: 10.1186/s12874-023-01871-2.Peer-Reviewed Original ResearchAnimalsAnophelesCluster AnalysisCross-Sectional StudiesHumansMalariaMosquito VectorsRandomized Controlled Trials as TopicSample Size
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