2024
Assessing 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
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 modelMediation analysis in the presence of continuous exposure measurement error
Cheng C, Spiegelman D, Li F. Mediation analysis in the presence of continuous exposure measurement error. Statistics In Medicine 2023, 42: 1669-1686. PMID: 36869626, PMCID: PMC11320713, DOI: 10.1002/sim.9693.Peer-Reviewed Original ResearchConceptsBody mass indexExposure measurement errorPhysical activityMediation proportionHealth Professionals FollowCardiovascular disease incidenceProfessionals FollowMediation analysisMass indexCardiovascular diseaseLower riskStudy designEffect estimatesValidation study designContinuous exposureBiased effect estimatesTrue exposureMediatorsExposureValidation studyBinary outcomesHealth science studiesOutcomesRiskDisease incidence
2022
Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials
Wang X, Turner E, Li F. Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials. Biometrical Journal 2022, 65: e2200113. PMID: 36567265, PMCID: PMC10482495, DOI: 10.1002/bimj.202200113.Peer-Reviewed Original ResearchA general method for calculating power for GEE analysis of complete and incomplete stepped wedge cluster randomized trials
Zhang Y, Preisser JS, Turner EL, Rathouz PJ, Toles M, Li F. A general method for calculating power for GEE analysis of complete and incomplete stepped wedge cluster randomized trials. Statistical Methods In Medical Research 2022, 32: 71-87. PMID: 36253078, PMCID: PMC9814029, DOI: 10.1177/09622802221129861.Peer-Reviewed Original ResearchDesign 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 numberSimulationsFunctionAssumptionDesign and analysis of partially randomized preference trials with propensity score stratification
Wang Y, Li F, Blaha O, Meng C, Esserman D. Design and analysis of partially randomized preference trials with propensity score stratification. Statistical Methods In Medical Research 2022, 31: 1515-1537. PMID: 35469503, PMCID: PMC10530658, DOI: 10.1177/09622802221095673.Peer-Reviewed Original ResearchA comparison of analytical strategies for cluster randomized trials with survival outcomes in the presence of competing risks
Li F, Lu W, Wang Y, Pan Z, Greene EJ, Meng G, Meng C, Blaha O, Zhao Y, Peduzzi P, Esserman D. A comparison of analytical strategies for cluster randomized trials with survival outcomes in the presence of competing risks. Statistical Methods In Medical Research 2022, 31: 1224-1241. PMID: 35290139, PMCID: PMC10518064, DOI: 10.1177/09622802221085080.Peer-Reviewed Original ResearchFinite‐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 Research
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 analysisClustersFormulaEstimating the natural indirect effect and the mediation proportion via the product method
Cheng C, Spiegelman D, Li F. Estimating the natural indirect effect and the mediation proportion via the product method. BMC Medical Research Methodology 2021, 21: 253. PMID: 34800985, PMCID: PMC8606099, DOI: 10.1186/s12874-021-01425-4.Peer-Reviewed Original ResearchConceptsInterval estimatorsApproximate estimatorExact estimatorMultivariate delta methodFinite sample performanceProduct methodNon-negligible biasBinary outcomesRare outcome assumptionExact expressionDelta methodVariance estimationEmpirical performanceEstimatorCommon data typesBootstrap approachBinary mediatorNatural indirect effectSample size
2019
Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness
Turner EL, Yao L, Li F, Prague M. Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness. Statistical Methods In Medical Research 2019, 29: 1338-1353. PMID: 31293199, DOI: 10.1177/0962280219859915.Peer-Reviewed Original Research
2018
Sample Size Determination for GEE Analyses of Stepped Wedge Cluster Randomized Trials
Li F, Turner EL, Preisser JS. Sample Size Determination for GEE Analyses of Stepped Wedge Cluster Randomized Trials. Biometrics 2018, 74: 1450-1458. PMID: 29921006, PMCID: PMC6461045, DOI: 10.1111/biom.12918.Peer-Reviewed Original Research