2025
Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge.
Liu J, Li F, Sutcliffe S, Colditz G. Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge. Statistical Methods In Medical Research 2025, 9622802251360409. PMID: 40785501, DOI: 10.1177/09622802251360409.Peer-Reviewed Original ResearchSW-CRTsLongitudinal cluster randomized trialsStepped wedge cluster randomized trialCluster-period sizesTreatment effect estimatesCluster randomized trialContinuous outcomesGeneralized Estimating EquationsOptimal designCluster randomized designEffect estimatesFixed budgetCluster randomized trial designFormulaEquationsAlgorithmOptimal numberCross-sectional designGlobal optimal designEstimationRandomized trial designRandomized trialsStepped-wedgeCRXO trialsOD algorithmPermutation tests for detecting treatment effect heterogeneity in cluster randomized trials
Maleyeff L, Li F, Haneuse S, Wang R. Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials. Statistical Methods In Medical Research 2025, 34: 1617-1632. PMID: 40525570, PMCID: PMC12365356, DOI: 10.1177/09622802251348999.Peer-Reviewed Original ResearchCluster randomized trialDetect treatment effect heterogeneityTreatment effect heterogeneityEffect heterogeneityNominal type I error rateRandomized trialsType I error rateTreatment-covariate interactionsAssess treatment effect heterogeneityTests of interaction termsPermutation testAverage treatment effectPain programEvaluation of intervention strategiesParametric assumptionsEffect modificationHealthcare ResearchActive copingSimulation studyTreatment effectsIntervention strategiesTrial contextPermutationInteraction termsTrialsWhat Is a Stepped-Wedge Cluster Randomized Trial?
Li F, Wang B, Heagerty P. What Is a Stepped-Wedge Cluster Randomized Trial? JAMA Internal Medicine 2025, 185: 593-594. PMID: 40063042, PMCID: PMC12052484, DOI: 10.1001/jamainternmed.2024.8216.Peer-Reviewed Original ResearchHow 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 teamsPower 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 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 ResearchWeighting 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 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 ResearchConceptsCluster-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 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 coefficientA review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials
Tong G, Nevins P, Ryan M, Davis-Plourde K, Ouyang Y, Macedo J, Meng C, Wang X, Caille A, Li F, Taljaard M. A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials. Clinical Trials 2024, 22: 45-56. PMID: 39377196, PMCID: PMC11810615, DOI: 10.1177/17407745241276137.Peer-Reviewed Original ResearchSmall-sample correctionsStepped-wedge cluster randomized trialCluster randomized trialSample size calculation methodGeneralized linear mixed modelsLongitudinal correlation structureSize calculation methodLinear mixed modelsPermutation testSample sizeBayesian approachRandomized trialsCorrelation structureMixed modelsBayesian analysisGeneralized Estimating EquationsPermutationMedian sample sizeIntervention conditionRandomization methodEquationsAssessing 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 dataTrialsRationale and Design of a Phase 2, Double-blind, Placebo-Controlled, Randomized Trial Evaluating AMP Kinase-Activation by Metformin in Focal Segmental Glomerulosclerosis
Barsotti G, Luciano R, Kumar A, Meliambro K, Kakade V, Tokita J, Naik A, Fu J, Peck E, Pell J, Reghuvaran A, Tanvir E, Patel P, Zhang W, Li F, Moeckel G, Perincheri S, Cantley L, Moledina D, Wilson F, He J, Menon M. Rationale and Design of a Phase 2, Double-blind, Placebo-Controlled, Randomized Trial Evaluating AMP Kinase-Activation by Metformin in Focal Segmental Glomerulosclerosis. Kidney International Reports 2024, 9: 1354-1368. PMID: 38707807, PMCID: PMC11068976, DOI: 10.1016/j.ekir.2024.02.006.Peer-Reviewed Original ResearchMinimal change diseaseRandomized Controlled TrialsSafety of metforminDouble-blindPodocyte injuryAdjunctive therapyPlacebo-controlled randomized controlled trialsPhase III studyPhase II trialPrimary glomerular diseaseFocal segmental glomerulosclerosisEffect of metforminPhase IIPlacebo-ControlledPreclinical dataNovel urineChange diseaseTissue markersRandomized trialsSegmental glomerulosclerosisGlomerular diseaseMechanistic biomarkersObservational studyFSGSInexpensive agentMultiply robust generalized estimating equations for cluster randomized trials with missing outcomes
Rabideau D, Li F, Wang R. Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes. Statistics In Medicine 2024, 43: 1458-1474. PMID: 38488532, PMCID: PMC12186826, DOI: 10.1002/sim.10027.Peer-Reviewed Original ResearchPropensity score modelMarginal regression parametersWeighted generalized estimating equationsRobust estimationCluster randomized trialRegression parametersMarginal meansMean modelIterative algorithmMonte Carlo simulationsGeneralized Estimating EquationsOutcome modelBotswana Combination Prevention ProjectCarlo simulationsEquationsCorrelation parametersEstimationReduce HIV incidenceHIV prevention measuresScore modelMultipliersRandomized trialsHIV incidencePrevention ProjectCRTFASTGEEPWR: A SAS Macro for Power of Generalized Estimating Equations Analysis of Multi-Period Cluster Randomized Trials with Application to Stepped Wedge Designs
Zhang Y, Preisser J, Li F, Turner E, Rathouz P. CRTFASTGEEPWR: A SAS Macro for Power of Generalized Estimating Equations Analysis of Multi-Period Cluster Randomized Trials with Application to Stepped Wedge Designs. Journal Of Statistical Software 2024, 108: 1-27. DOI: 10.18637/jss.v108.c01.Peer-Reviewed Original ResearchSAS macroMarginal mean modelCluster randomized trialContinuous responseStepped wedge designMean modelCorrelation structureGeneralized Estimating EquationsPower methodIncomplete designsGeneral methodWedge designStatistical powerTrial scenariosMulti-parameterEvaluation of interventionsRandomized trialsGeneralized estimating equation analysisEquationsInference
2023
Sample 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 formula
2022
Stepped Wedge Cluster Randomized Trials: A Methodological Overview
Li F, Wang R. Stepped Wedge Cluster Randomized Trials: A Methodological Overview. World Neurosurgery 2022, 161: 323-330. PMID: 35505551, PMCID: PMC9074087, DOI: 10.1016/j.wneu.2021.10.136.Peer-Reviewed Original ResearchConceptsStepped wedge designStepped wedge cluster randomized trialIntervention programsSample size determinationDelivery of patient careWedge designStepped wedge trial designHealth intervention programsCluster randomized trialRandomized trialsPatient careStudy designPragmatic settingSize determinationTrial designGeneralizing Trial Evidence to Target Populations in Non-Nested Designs: Applications to AIDS Clinical Trials
Li F, Buchanan AL, Cole SR. Generalizing Trial Evidence to Target Populations in Non-Nested Designs: Applications to AIDS Clinical Trials. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2022, 71: 669-697. PMID: 35968541, PMCID: PMC9367209, DOI: 10.1111/rssc.12550.Peer-Reviewed Original ResearchAIDS Clinical Trials GroupTarget populationClinical Trials GroupComparative effectiveness evidenceTreatment effectsRandomized trialsTrial evidenceClinical trialsTrial groupHIV interventionsACTG trialsTrial participantsTrial designPropensity scoreEffectiveness evidenceTrialsAIDS clinical trialsSpecified populationPopulationAverage treatment effectMost casesHIVEvidenceRegression
2021
A note on semiparametric efficient generalization of causal effects from randomized trials to target populations
Li F, Hong H, Stuart E. A note on semiparametric efficient generalization of causal effects from randomized trials to target populations. Communication In Statistics- Theory And Methods 2021, 52: 5767-5798. PMID: 37484707, PMCID: PMC10361688, DOI: 10.1080/03610926.2021.2020291.Peer-Reviewed Original ResearchClarifying selection bias in cluster randomized trials
Li F, Tian Z, Bobb J, Papadogeorgou G, Li F. Clarifying selection bias in cluster randomized trials. Clinical Trials 2021, 19: 33-41. PMID: 34894795, DOI: 10.1177/17407745211056875.Peer-Reviewed Original ResearchConceptsAverage treatment effectCluster randomized trialPost-randomization selection biasPrincipal strataAnalysis of cluster randomized trialsSelection biasCausal effectsCovariate adjustment methodsData generating processRecruited populationPrincipal stratification frameworkPresence of selection biasHeterogeneous treatment effectsRegression adjustment methodEstimate causal effectsRandomized trialsElectronic health recordsOverall populationEffect heterogeneityIntention-to-treat analysisSimulation studyTreatment effectsEmpirical performanceEstimandsEstimation strategyMethodological challenges in pragmatic trials in Alzheimer’s disease and related dementias: Opportunities for improvement
Taljaard M, Li F, Qin B, Cui C, Zhang L, Nicholls SG, Carroll K, Mitchell SL. Methodological challenges in pragmatic trials in Alzheimer’s disease and related dementias: Opportunities for improvement. Clinical Trials 2021, 19: 86-96. PMID: 34841910, PMCID: PMC8847324, DOI: 10.1177/17407745211046672.Peer-Reviewed Original ResearchConceptsPragmatic trialAlzheimer's diseasePrimary outcomeSample size calculationGroup treatment trialsPairs of reviewersDementia researchSize calculationCluster randomized trialGroup treatment designRandomized trialsTreatment trialsIntracluster correlationMethodological qualityTrial reportsBaseline assessmentDiseaseTrialsDementiaKey methodological characteristicsOutcomesType IMethodological quality indicatorsUnique methodological challengesSame individual
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