2024
A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials.
Tong G, Nevins P, Ryan M, Davis-Plourde K, Ouyang Y, Pereira Macedo J, Meng C, Wang X, Caille A, Li F, Taljaard M. A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials. Clinical Trials 2024, 17407745241276137. PMID: 39377196, DOI: 10.1177/17407745241276137.Peer-Reviewed Original ResearchSmall-sample correctionsStepped-wedge cluster randomized trialCluster randomized trialSample size calculation methodGeneralized linear mixed modelsLongitudinal correlation structureSize calculation methodLinear mixed modelsPermutation testSample sizeBayesian approachRandomized trialsCorrelation structureMixed modelsBayesian analysisGeneralized estimating equationsPermutationMedian sample sizeIntervention conditionRandomization methodEquationsMaintaining 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 errorCRTFASTGEEPWR: A SAS Macro for Power of Generalized Estimating Equations Analysis of Multi-Period Cluster Randomized Trials with Application to Stepped Wedge Designs
Zhang Y, Preisser J, Li F, Turner E, Rathouz P. CRTFASTGEEPWR: A SAS Macro for Power of Generalized Estimating Equations Analysis of Multi-Period Cluster Randomized Trials with Application to Stepped Wedge Designs. Journal Of Statistical Software 2024, 108: 1-27. DOI: 10.18637/jss.v108.c01.Peer-Reviewed Original ResearchSAS macroMarginal mean modelCluster randomized trialContinuous responseStepped wedge designMean modelCorrelation structureGeneralized estimating equationsPower methodIncomplete designsGeneral methodWedge designStatistical powerTrial scenariosMulti-parameterEvaluation of interventionsRandomized trialsGeneralized estimating equation analysisEquationsInference
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 calculationCompeting risks regression for clustered survival data via the marginal additive subdistribution hazards model
Chen X, Esserman D, Li F. Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model. Statistica Neerlandica 2023, 78: 281-301. DOI: 10.1111/stan.12317.Peer-Reviewed Original ResearchSandwich variance estimatorCorrelated failure time dataVariance estimatorUnknown dependency structureFailure time dataAdditive hazards modelFinite samplesEquation approachCensoring timeCorrelation structureAdditive structureDependent censoringFit testSimulation studyEvent of interestDependency structureFailure timeEstimatorSubdistribution hazardRegression coefficientsIncidence functionCumulative incidence functionSubdistribution hazard modelTime dataOverall modelInformative 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 structureGEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials
Zhang Y, Preisser J, Li F, Turner E, Toles M, Rathouz P. GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials. Computer Methods And Programs In Biomedicine 2023, 230: 107362. PMID: 36709555, PMCID: PMC10037297, DOI: 10.1016/j.cmpb.2023.107362.Peer-Reviewed Original ResearchConceptsNumber of clustersBias-corrected estimationCorrelation structurePopulation-averaged interpretationMarginal regression modelsDeletion diagnosticsEstimating EquationsFinite-sample adjustmentInfluence of observationsLarge valuesStandard errorEquationsSandwich estimatorVariance estimatorCook's distanceSAS macroDesign of clusterCount outcomesLongitudinal responseCorrelation parametersValid inferencesCorrelated outcomesFlexible specificationBiased estimatesEstimator
2022
Estimands 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 ResearchConceptsInformative cluster sizeCluster sizeCommon estimatorsCorrelation structureAlternative estimatorsEstimatorUnbiased estimatesBiased estimatesEstimandsDifferent estimandsTarget estimandAnalytic approachCareful specificationLarge clustersEquationsDifferent analytic approachesEstimatesMixed-effects 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
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