2023
Designing 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 ResearchConceptsSample 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 calculation
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, DOI: 10.1111/biom.13692.Peer-Reviewed Original ResearchConceptsMultivariate linear mixed modelTreatment effect estimatorJoint distributionEqual cluster sizesCluster sizeExpectation-maximization algorithmFinite numberEffects estimatorEmpirical powerCorrelation parametersPower analysisEstimatorSize assumptionsSample sizeNull hypothesisPower calculationPower determinationLinear mixed modelsParametersMixed models
2021
Power considerations for generalized estimating equations analyses of four‐level cluster randomized trials
Wang X, Turner EL, Preisser JS, Li F. Power considerations for generalized estimating equations analyses of four‐level cluster randomized trials. Biometrical Journal 2021, 64: 663-680. PMID: 34897793, PMCID: PMC9574475, DOI: 10.1002/bimj.202100081.Peer-Reviewed Original ResearchConceptsCorrelation structureClosed-form sample size formulaModel-based varianceTrue correlation structureSandwich variance estimatorSandwich varianceSample size formulaVariance functionVariance estimatorEmpirical powerCorrelation parametersCorrelation matrixEstimating EquationsSize formulaEquationsArbitrary linkPower considerationsSame clusterPower calculationEstimatorSample sizeEquation analysisClustersFormula
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 ResearchConceptsAnalysis 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
2019
Design and analysis considerations for cohort stepped wedge cluster randomized trials with a decay correlation structure
Li F. Design and analysis considerations for cohort stepped wedge cluster randomized trials with a decay correlation structure. Statistics In Medicine 2019, 39: 438-455. PMID: 31797438, PMCID: PMC7027591, DOI: 10.1002/sim.8415.Peer-Reviewed Original ResearchConceptsQuasi-least squaresCorrelation structureAdditional correlation parameterCluster correlation structureCorrelation parametersSample size proceduresPeriod correlationMultiple outcome measurementsSandwich varianceCorrelation decayPower proceduresSize proceduresEmpirical powerSimulation studySame clusterTrial exampleSquaresAnalysis considerationsWedge designParametersSample sizeContinuous outcomesClusters