2025
Model‐Robust Standardization in Cluster‐Randomized Trials
Li F, Tong J, Fang X, Cheng C, Kahan B, Wang B. Model‐Robust Standardization in Cluster‐Randomized Trials. Statistics In Medicine 2025, 44: e70270. PMID: 40968363, DOI: 10.1002/sim.70270.Peer-Reviewed Original ResearchConceptsInformative cluster sizeWorking regression modelsJackknife variance estimatorTreatment effect parametersCluster randomized trialData generating processAverage treatment effectVariance estimationCoefficient estimatesGeneralized linear mixed modelsEstimandsR packageNatural testCluster averagesLinear mixed modelsEstimationTreatment effectsExtensive simulation experimentsCluster sizeInferenceRegression modelsGeneralized Estimating EquationsGeneration processMixed modelsEquationsSelecting 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 algorithmA Bayesian nonparametric approach to mediation and spillover effects with multiple mediators in cluster-randomized trials
Ohnishi Y, Li F. A Bayesian nonparametric approach to mediation and spillover effects with multiple mediators in cluster-randomized trials. Journal Of The American Statistical Association 2025, 1-20. DOI: 10.1080/01621459.2025.2544366.Peer-Reviewed Original ResearchCluster randomized trialSpillover effectsAssessing Mediation in Cross‐Sectional Stepped Wedge Cluster Randomized Trials
Cao Z, Li F. Assessing Mediation in Cross‐Sectional Stepped Wedge Cluster Randomized Trials. Statistics In Medicine 2025, 44: e70175. PMID: 40662697, DOI: 10.1002/sim.70175.Peer-Reviewed Original ResearchConceptsStepped wedge cluster randomized trialSW-CRTsTreatment effect heterogeneityCluster randomized trialSW-CRTBinary mediatorEffect heterogeneityGeneralized linear mixed modelsNatural indirect effectLinear mixed modelsEstimationEffective structureExamplesMixed modelsType combinationsMediation proportionPractical implementationPermutation 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 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 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 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 methodEquationsHow 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 modelsOptimal 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 EquationsMaintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures
Ouyang Y, Taljaard M, Forbes A, Li F. Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures. Statistical Methods In Medical Research 2024, 33: 1497-1516. PMID: 38807552, PMCID: PMC11499024, DOI: 10.1177/09622802241248382.Peer-Reviewed Original ResearchRandom effects structureVariance estimationComplex correlation structureRobust variance estimationFixed effects parametersDegrees of freedom correctionCluster randomized trialEstimates of standard errorsCorrelation structureRandom effectsStepped-wedge cluster randomized trialComprehensive simulation studyLinear mixed modelsStatistical inferenceRandom intercept modelSimulation studyMixed modelsMisspecificationValidity of inferencesRandom interceptContinuous outcomesEstimationComputational challengesIntercept modelStandard errorAssessing 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 dataTrialsMultiply 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 ProjectCorrection: Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials
Wang X, Goldfeld K, Taljaard M, Li F. Correction: Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials. Prevention Science 2024, 25: 1004-1004. PMID: 38180545, PMCID: PMC11390812, DOI: 10.1007/s11121-023-01615-0.Peer-Reviewed Original ResearchSubgroup-specific treatment effectsSample size requirementsCluster randomized trialSize requirementsTreatment effects
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