2024
How to achieve model-robust inference in stepped wedge trials with model-based methods?
Wang B, Wang X, Li F. How to achieve model-robust inference in stepped wedge trials with model-based methods? Biometrics 2024, 80: ujae123. PMID: 39499239, PMCID: PMC11536888, DOI: 10.1093/biomtc/ujae123.Peer-Reviewed Original ResearchConceptsTreatment effect estimandsWorking correlation structureSandwich variance estimatorExchangeable working correlation structureFunction of calendar timeEffect estimandsVariance estimationLink functionStepped wedge trialEstimandsTheoretical resultsCorrelation structureWedge trialsEstimating equationsCluster randomized trialG-computationLinear mixed modelsInferencePotential outcomesMisspecificationEstimationEffective structureModel-based methodsGeneralized estimating equationsMixed models
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 model
2022
Design and analysis of cluster randomized trials with time‐to‐event outcomes under the additive hazards mixed model
Blaha O, Esserman D, Li F. Design and analysis of cluster randomized trials with time‐to‐event outcomes under the additive hazards mixed model. Statistics In Medicine 2022, 41: 4860-4885. PMID: 35908796, PMCID: PMC9588628, DOI: 10.1002/sim.9541.Peer-Reviewed Original ResearchConceptsSample size formulaCluster sizeNew sample size formulaSample size proceduresSize formulaEffect parametersSandwich variance estimatorStatistical inferenceCluster size variationEvent outcomesRandomization-based testsImproved inferenceSize proceduresTreatment effect parametersVariance estimatorSmall sample biasesAnalysis of clustersSimulation studyUnequal cluster sizesFrailty termVariance inflation factorFailure timeSample size requirementsMixed modelsAppropriate definitionClustered 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 numberSimulationsFunctionAssumptionFinite‐sample adjustments in variance estimators for clustered competing risks regression
Chen X, Li F. Finite‐sample adjustments in variance estimators for clustered competing risks regression. Statistics In Medicine 2022, 41: 2645-2664. PMID: 35288959, DOI: 10.1002/sim.9375.Peer-Reviewed Original ResearchConceptsSandwich variance estimatorVariance estimatorRegression parameter estimatorReal data example
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 analysisClustersFormulaSample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome
Li F, Tong G. Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome. Statistical Methods In Medical Research 2021, 30: 1288-1305. PMID: 33826454, PMCID: PMC9132618, DOI: 10.1177/0962280221990415.Peer-Reviewed Original ResearchConceptsSample size formulaExchangeable working correlationExtensive Monte Carlo simulation studySize formulaMonte Carlo simulation studyFinite sample correctionMarginal relative riskCorresponding sample size formulaeSandwich variance estimatorVariable cluster sizesNumber of clustersAsymptotic efficiencySandwich varianceCluster size variabilityRobust sandwich varianceSample size estimationVariance estimatorAnalytical derivationSimulation studyCluster sizePoisson modelCoefficient estimatesFormulaCorrelation coefficient estimatesBinary outcomes