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 definitionDesigning three-level cluster randomized trials to assess treatment effect heterogeneity
Li F, Chen X, Tian Z, Esserman D, Heagerty PJ, Wang R. Designing three-level cluster randomized trials to assess treatment effect heterogeneity. Biostatistics 2022, 24: 833-849. PMID: 35861621, PMCID: PMC10583727, DOI: 10.1093/biostatistics/kxac026.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationHumansRandomized Controlled Trials as TopicResearch DesignSample SizeEstimands in cluster-randomized trials: choosing analyses that answer the right question
Kahan BC, Li F, Copas AJ, Harhay MO. Estimands in cluster-randomized trials: choosing analyses that answer the right question. International Journal Of Epidemiology 2022, 52: 107-118. PMID: 35834775, PMCID: PMC9908044, DOI: 10.1093/ije/dyac131.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationHumansRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsInformative cluster sizeCluster sizeCommon estimatorsCorrelation structureAlternative estimatorsEstimatorUnbiased estimatesBiased estimatesEstimandsDifferent estimandsTarget estimandAnalytic approachCareful specificationLarge clustersEquationsDifferent analytic approachesEstimatesMixed-effects modelsSample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison
Ouyang Y, Li F, Preisser JS, Taljaard M. Sample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison. International Journal Of Epidemiology 2022, 51: 2000-2013. PMID: 35679584, PMCID: PMC9749719, DOI: 10.1093/ije/dyac123.Peer-Reviewed Original ResearchPragmatic clinical trial design in emergency medicine: Study considerations and design types
Gettel CJ, Yiadom MYAB, Bernstein SL, Grudzen CR, Nath B, Li F, Hwang U, Hess EP, Melnick ER. Pragmatic clinical trial design in emergency medicine: Study considerations and design types. Academic Emergency Medicine 2022, 29: 1247-1257. PMID: 35475533, PMCID: PMC9790188, DOI: 10.1111/acem.14513.Peer-Reviewed Original ResearchConceptsPragmatic clinical trialsClinical trial designTrial designReal-world clinical practicePragmatic clinical trial designElectronic health recordsEmergency departmentClinical trialsStudy design typeClinical practiceStudy typeTrial componentsHealth recordsEmergency medicineEmergency medicine investigatorsHuman subjects concernsInvestigatorsStudy findingsStudy considerationsTrialistsTrialsPower Analysis for Cluster Randomized Trials with Continuous Coprimary Endpoints
Yang S, Moerbeek M, Taljaard M, Li F. Power Analysis for Cluster Randomized Trials with Continuous Coprimary Endpoints. Biometrics 2022, 79: 1293-1305. PMID: 35531926, PMCID: PMC11321238, DOI: 10.1111/biom.13692.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationLinear ModelsRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsMultivariate linear mixed modelTreatment effect estimatorJoint distributionEqual cluster sizesCluster sizeExpectation-maximization algorithmFinite numberEffects estimatorEmpirical powerCorrelation parametersPower analysisEstimatorSize assumptionsSample sizeNull hypothesisPower calculationPower determinationLinear mixed modelsParametersMixed modelsStepped 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 designDesign and analysis of partially randomized preference trials with propensity score stratification
Wang Y, Li F, Blaha O, Meng C, Esserman D. Design and analysis of partially randomized preference trials with propensity score stratification. Statistical Methods In Medical Research 2022, 31: 1515-1537. PMID: 35469503, PMCID: PMC10530658, DOI: 10.1177/09622802221095673.Peer-Reviewed Original Research