2023
Sample 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 TrialMediation analysis in the presence of continuous exposure measurement error
Cheng C, Spiegelman D, Li F. Mediation analysis in the presence of continuous exposure measurement error. Statistics In Medicine 2023, 42: 1669-1686. PMID: 36869626, PMCID: PMC11320713, DOI: 10.1002/sim.9693.Peer-Reviewed Original ResearchConceptsBody mass indexExposure measurement errorPhysical activityMediation proportionHealth Professionals FollowCardiovascular disease incidenceProfessionals FollowMediation analysisMass indexCardiovascular diseaseLower riskStudy designEffect estimatesValidation study designContinuous exposureBiased effect estimatesTrue exposureMediatorsExposureValidation studyBinary outcomesHealth science studiesOutcomesRiskDisease incidence
2022
Power 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 modelsSample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes
Tian Z, Esserman D, Tong G, Blaha O, Dziura J, Peduzzi P, Li F. Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes. Statistics In Medicine 2022, 41: 645-664. PMID: 34978097, PMCID: PMC8962918, DOI: 10.1002/sim.9284.Peer-Reviewed Original Research
2021
Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity
Tong G, Esserman D, Li F. Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity. Statistics In Medicine 2021, 41: 1376-1396. PMID: 34923655, PMCID: PMC10197222, DOI: 10.1002/sim.9283.Peer-Reviewed Original ResearchMarginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes
Li F, Yu H, Rathouz PJ, Turner EL, Preisser JS. Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes. Biostatistics 2021, 23: 772-788. PMID: 33527999, PMCID: PMC9291643, DOI: 10.1093/biostatistics/kxaa056.Peer-Reviewed Original ResearchConceptsPopulation-averaged interpretationFinite sample inferenceMarginal inferenceMarginal meansRigorous justificationBinary outcomesComputational burdenIndividual-level observationsMarginal modelsInterval estimationMarginal modelingCorrelated binary outcomesCluster-period sizesJoint estimationEquationsLinear modelEstimating EquationsSW-CRTsFlexible toolFast pointInferenceEstimationAdditional mappingModelApproach
2020
Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials
Yang S, Li F, Starks MA, Hernandez AF, Mentz RJ, Choudhury KR. Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials. Statistics In Medicine 2020, 39: 4218-4237. PMID: 32823372, PMCID: PMC7948251, DOI: 10.1002/sim.8721.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisHumansLinear ModelsRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsAnalysis of CRTsNumerous statistical methodsNew sample size formulaTreatment effect heterogeneitySample size proceduresFinite samplesSample size formulaStatistical methodsSize proceduresBinary covariateEffect heterogeneityEmpirical powerCovariates of interestEffect formulaParameter constellationsSize formulaAdjusted intraclass correlation coefficientsSample size requirementsExtensive simulationsHeterogeneous treatment effectsFormulaCovariate interactionsSize requirementsCluster Randomized TrialSample size