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 outcomesMissing Data
Tong G, Li F, Allen A. Missing Data. 2022, 1681-1701. DOI: 10.1007/978-3-319-52636-2_117.Peer-Reviewed Original ResearchLikelihood-based analysisMissingness modelMissingness processData mechanismAverage treatment effectStatistical methodsComplete case analysisConsistent estimatesRobust approachInverse probability weightingBiased estimatesMissingnessOutcome distributionModeling approachProbability weightingData processSensitivity analysisOutcome modelModelEstimatesBrief discussionPractical considerationsInferenceApproachImputationClustered restricted mean survival time regression
Chen X, Harhay MO, Li F. Clustered restricted mean survival time regression. Biometrical Journal 2022, 65: e2200002. PMID: 35593026, DOI: 10.1002/bimj.202200002.Peer-Reviewed Original ResearchConceptsSandwich variance estimatorVariance estimatorValid inferencesRegression coefficient estimatesSmall sample scenariosContinuous functionsCluster correlationEffects of covariatesEstimatorHazard functionTarget parametersCoefficient estimatesMultilevel observational studyTime regressionRegression coefficientsInferenceEvent outcomesEquationsRegression modelsModelCritical assumptionsSufficient numberSimulationsFunctionAssumption
2021
Marginal 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
Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview
Li F, Hughes JP, Hemming K, Taljaard M, Melnick ER, Heagerty PJ. Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview. Statistical Methods In Medical Research 2020, 30: 612-639. PMID: 32631142, PMCID: PMC7785651, DOI: 10.1177/0962280220932962.Peer-Reviewed Original Research
2019
Missing Data
Tong G, Li F, Allen A. Missing Data. 2019, 1-21. DOI: 10.1007/978-3-319-52677-5_117-1.Peer-Reviewed Original ResearchLikelihood-based analysisMissingness modelMissingness processData mechanismAverage treatment effectStatistical methodsComplete case analysisConsistent estimatesRobust approachInverse probability weightingBiased estimatesMissingnessOutcome distributionModeling approachProbability weightingData processSensitivity analysisOutcome modelModelEstimatesBrief discussionPractical considerationsInferenceApproachImputation