2025
How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?—A Survey of Estimands and Common Estimators
Lee K, Li F. How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?—A Survey of Estimands and Common Estimators. Biometrical Journal 2025, 67: e70052. PMID: 40302411, PMCID: PMC12041842, DOI: 10.1002/bimj.70052.Peer-Reviewed Original ResearchConceptsInformative cluster sizeIndependence estimating equationsCluster-period sizesParallel cluster randomized trialsTreatment effect estimatesCluster randomized trialInconsistent estimatesSimulation studyEstimandsEstimating EquationsCluster sizeContinuous outcomesEstimationTreatment effectsEffect estimatesImprove mental healthRandomized trialsConvergenceEquationsRural eastern IndiaMental healthMixed-effects modelsYouth teams
2024
Inverting estimating equations for causal inference on quantiles
Cheng C, Li F. Inverting estimating equations for causal inference on quantiles. Biometrika 2024, 112: asae058. DOI: 10.1093/biomet/asae058.Peer-Reviewed Original ResearchHow 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 modelsOptimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials
Liu J, Li F. Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials. Statistical Methods In Medical Research 2024, 33: 1299-1330. PMID: 38813761, DOI: 10.1177/09622802241247717.Peer-Reviewed Original ResearchConceptsMaximin optimal designsStepped wedge cluster randomized trialLocally optimal designsCluster-period sizesClosed-form formulaCluster-randomized crossover trialCross-sectional sampling schemeInteger estimationOptimal design algorithmDesign algorithmLongitudinal cluster randomized trialsWorking correlation structureCluster randomized trialMethod of generalized estimating equationsTreatment effect estimatesSAS macroVariance expressionsExact valueCorrelation structureMaximinSampling schemeBetween-clusterOptimal designOptimization design researchEstimating EquationsModel-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments
Wang B, Park C, Small D, Li F. Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments. Journal Of The American Statistical Association 2024, 119: 2959-2971. PMID: 39911293, PMCID: PMC11795269, DOI: 10.1080/01621459.2023.2289693.Peer-Reviewed Original ResearchCluster-randomized experimentCluster size variationNuisance functionsParametric working modelsFlexible covariate adjustmentCovariate-adjusted estimatesCovariate adjustment methodsCovariate adjustmentModel-based covariate adjustmentEfficient estimationSimulation studyRobust inferenceSupplementary materialsEstimandsEstimating EquationsModel-based estimatesG-computationCluster averagesEstimationTreatment assignmentRoutine practice conditionsRisk of biasEquationsTreatment effectsCovariates
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 calculationORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals
Meng C, Ryan M, Rathouz P, Turner E, Preisser J, Li F. ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals. Computer Methods And Programs In Biomedicine 2023, 237: 107567. PMID: 37207384, DOI: 10.1016/j.cmpb.2023.107567.Peer-Reviewed Original ResearchConceptsOrdinal outcomesSandwich estimatorR packageSimulation studyCorrelated ordinal dataFinite sample biasesNumber of clustersCovariance estimationMarginal modelsEquationsParameter estimatesOrdinal responsesAssociation parametersCluster associationsBias correctionOrdinal dataEstimatorEstimating EquationsNominal levelMarginal meansResidualsEstimationPairwise odds ratiosAssociation modelGEE modelGEEMAEE: 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
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 analysisClustersFormulaMarginal 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
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