2025
Guidelines 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 ResearchPrioritizing 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 approach
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 ResearchMeSH KeywordsBuprenorphineCausalityHumansObservational Studies as TopicOpioid-Related DisordersResearch DesignEstimates 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 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 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 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 effectsAssumptionsNoncompliance
2023
Group sequential two‐stage preference designs
Liu R, Li F, Esserman D, Ryan M. Group sequential two‐stage preference designs. Statistics In Medicine 2023, 43: 315-341. PMID: 38010193, DOI: 10.1002/sim.9962.Peer-Reviewed Original ResearchDesigning individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations
Wang X, Turner E, Li F. Designing individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations. Statistics In Medicine 2023, 43: 358-378. PMID: 38009329, PMCID: PMC10939061, DOI: 10.1002/sim.9966.Peer-Reviewed Original ResearchMeSH KeywordsBiasCluster AnalysisComputer SimulationHumansModels, StatisticalResearch DesignSample SizeConceptsSample size proceduresConstant treatment effectCorrelation structureSize proceduresMarginal mean modelClosed-form sample size formulaCorrelation parametersSandwich variance estimatorGroup treatment trialsEquation approachExchangeable correlation structureSample size formulaBinary outcomesVariance estimatorEmpirical powerLinear timeMean modelCorrelation matrixDifferent correlation parametersEstimating EquationsSize formulaEquationsSample size calculationDifferent assumptionsProper sample size calculationSample 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 ResearchConceptsSample 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 TrialInformative cluster size in cluster-randomised trials: A case study from the TRIGGER trial
Kahan B, Li F, Blette B, Jairath V, Copas A, Harhay M. Informative cluster size in cluster-randomised trials: A case study from the TRIGGER trial. Clinical Trials 2023, 20: 661-669. PMID: 37439089, PMCID: PMC10638852, DOI: 10.1177/17407745231186094.Peer-Reviewed Original ResearchConceptsCluster-randomised trialCluster-level summariesAcute upper gastrointestinal bleedingExchangeable correlation structureRed blood cell transfusionEQ-5D VAS scoreMixed-effects modelsUpper gastrointestinal bleedingBlood cell transfusionMixed effects modelsTreatment effectsCell transfusionGastrointestinal bleedingIschemic eventsVAS scoresOdds ratioMost outcomesTRIGGER trialTreatment effect estimatesEffect estimatesInformative cluster sizeTrialsOutcomesParticipant outcomesCorrelation structureMaximin 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 formulaAccounting 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 ResearchConceptsSample 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 sizeClustersEstimationFormulaA scoping review described diversity in methods of randomization and reporting of baseline balance in stepped-wedge cluster randomized trials
Nevins P, Davis-Plourde K, Pereira Macedo J, Ouyang Y, Ryan M, Tong G, Wang X, Meng C, Ortiz-Reyes L, Li F, Caille A, Taljaard M. A scoping review described diversity in methods of randomization and reporting of baseline balance in stepped-wedge cluster randomized trials. Journal Of Clinical Epidemiology 2023, 157: 134-145. PMID: 36931478, PMCID: PMC10546924, DOI: 10.1016/j.jclinepi.2023.03.010.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisCross-Sectional StudiesHumansRandom AllocationRandomized Controlled Trials as TopicResearch DesignConceptsStepped-wedge clusterIndividual-level characteristicsMethod of randomizationCross-sectional designControl armBaseline imbalancesCohort designMedian numberElectronic searchPrimary analysisBaseline balanceStudy designPrimary reportsBaselineTrialsIntervention conditionSW-CRTsRandomizationReportingEliminating Ambiguous Treatment Effects Using Estimands
Kahan B, Cro S, Li F, Harhay M. Eliminating Ambiguous Treatment Effects Using Estimands. American Journal Of Epidemiology 2023, 192: 987-994. PMID: 36790803, PMCID: PMC10236519, DOI: 10.1093/aje/kwad036.Peer-Reviewed Original Research
2022
Power analyses for stepped wedge designs with multivariate continuous outcomes
Davis‐Plourde K, Taljaard M, Li F. Power analyses for stepped wedge designs with multivariate continuous outcomes. Statistics In Medicine 2022, 42: 559-578. PMID: 36565050, PMCID: PMC9985483, DOI: 10.1002/sim.9632.Peer-Reviewed Original ResearchConceptsMultivariate outcomesMultivariate linear mixed modelIntracluster correlation coefficientSample size proceduresClosed cohort designRigorous justificationSample size calculation procedureTreatment effect estimatorJoint distributionSize proceduresTest statisticLinear mixed modelsEfficient treatment effect estimatorsCommon treatment effectMixed modelsCalculation procedureExtensive simulationsEffects estimatorIntersection-union testPower analysisEstimatorWedge designEfficient powerModelContinuous outcomesAssessing Exposure-Time Treatment Effect Heterogeneity in Stepped-Wedge Cluster Randomized Trials
Maleyeff L, Li F, Haneuse S, Wang R. Assessing Exposure-Time Treatment Effect Heterogeneity in Stepped-Wedge Cluster Randomized Trials. Biometrics 2022, 79: 2551-2564. PMID: 36416302, PMCID: PMC10203056, DOI: 10.1111/biom.13803.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisCross-Over StudiesRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsTreatment effect heterogeneityEffect heterogeneityParameter increasesTreatment effect parametersParametric functional formModel choicePermutation testModel formulationSimulation studyPrecise averageNew model formulationFunctional formEffect parametersRandom effectsTreatment effect estimatesCategorical termsVariance componentsA general method for calculating power for GEE analysis of complete and incomplete stepped wedge cluster randomized trials
Zhang Y, Preisser JS, Turner EL, Rathouz PJ, Toles M, Li F. A general method for calculating power for GEE analysis of complete and incomplete stepped wedge cluster randomized trials. Statistical Methods In Medical Research 2022, 32: 71-87. PMID: 36253078, PMCID: PMC9814029, DOI: 10.1177/09622802221129861.Peer-Reviewed Original ResearchDesign and analysis of cluster randomized trials with time‐to‐event outcomes under the additive hazards mixed model
Blaha O, Esserman D, Li F. Design and analysis of cluster randomized trials with time‐to‐event outcomes under the additive hazards mixed model. Statistics In Medicine 2022, 41: 4860-4885. PMID: 35908796, PMCID: PMC9588628, DOI: 10.1002/sim.9541.Peer-Reviewed Original ResearchMeSH KeywordsBiasCluster AnalysisComputer SimulationHumansRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsSample size formulaCluster sizeNew sample size formulaSample size proceduresSize formulaEffect parametersSandwich variance estimatorStatistical inferenceCluster size variationEvent outcomesRandomization-based testsImproved inferenceSize proceduresTreatment effect parametersVariance estimatorSmall sample biasesAnalysis of clustersSimulation studyUnequal cluster sizesFrailty termVariance inflation factorFailure timeSample size requirementsMixed modelsAppropriate definition
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