2024
Evaluating analytic models for individually randomized group treatment trials with complex clustering in nested and crossed designs
Moyer J, Li F, Cook A, Heagerty P, Pals S, Turner E, Wang R, Zhou Y, Yu Q, Wang X, Murray D. Evaluating analytic models for individually randomized group treatment trials with complex clustering in nested and crossed designs. Statistics In Medicine 2024, 43: 4796-4818. PMID: 39225281, DOI: 10.1002/sim.10206.Peer-Reviewed Original ResearchGroup treatmentRandomized group treatment trialsTreatment trialsDeliver treatmentNominal type I error rateData generating mechanismRating inflationType I error rateMultiple membershipsType I error rate inflationParticipantsAgent settingMultiple agentsOutcome measuresSingle agent settingTrial armsSimulation studyStudy designTherapistsStudy armsEvaluate analytical modelsContinuous outcomesMaintaining 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 errorSample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs
Wang X, Chen X, Goldfeld K, Taljaard M, Li F. Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs. Statistical Methods In Medical Research 2024, 33: 1115-1136. PMID: 38689556, PMCID: PMC11347095, DOI: 10.1177/09622802241247736.Peer-Reviewed Original ResearchCluster randomized crossover designSample size formulaTreatment effect heterogeneityAverage treatment effectHeterogeneity of treatment effectsSize formulaRandomized crossover designCluster-randomized crossover trialRandomized crossover trialEffect heterogeneitySampling schemeCluster randomized designTreatment effectsDifferential treatment effectsCrossover designFormulaContinuous outcomesLinear mixed modelsSample sizeCrossover trialInteraction testMixed modelsCovariatesClinical characteristicsStatistical methodsAssessing 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 dataTrials
2023
Sample 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 formula
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 outcomes
2021
swdpwr: A SAS macro and an R package for power calculations in stepped wedge cluster randomized trials
Chen J, Zhou X, Li F, Spiegelman D. swdpwr: A SAS macro and an R package for power calculations in stepped wedge cluster randomized trials. Computer Methods And Programs In Biomedicine 2021, 213: 106522. PMID: 34818620, PMCID: PMC8665077, DOI: 10.1016/j.cmpb.2021.106522.Peer-Reviewed Original ResearchConceptsWedge clusterIntracluster correlation coefficientContinuous outcomesCross-sectional cohortBinary outcomesExchangeable correlation structureWedge designPublic health intervention evaluationsHealth services researchClosed cohort designPower calculationCohort designClosed cohortStudy designIntracluster correlationIntervention evaluationNeeds of investigatorsOutcomesTrialsCohortServices researchInvestigatorsPrevious studiesSWD
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
2017
An evaluation of constrained randomization for the design and analysis of group‐randomized trials with binary outcomes
Li F, Turner EL, Heagerty PJ, Murray DM, Vollmer WM, DeLong ER. An evaluation of constrained randomization for the design and analysis of group‐randomized trials with binary outcomes. Statistics In Medicine 2017, 36: 3791-3806. PMID: 28786223, PMCID: PMC5624845, DOI: 10.1002/sim.7410.Peer-Reviewed Original ResearchConceptsGroup-level covariatesPossible allocation schemesMonte Carlo simulationsStatistical propertiesRandomization-based testsStatistical issuesCarlo simulationsPrespecified percentageAllocation schemeStatistical testsCandidate allocationsSpaceBinary outcomesAllocation techniquePermutation testPractical limitationsPower lossSchemeSuch designsGroup-randomized trialLarge numberF-testContinuous outcomesCovariate imbalanceInference