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 dataTrialsDoubly robust estimation and sensitivity analysis for marginal structural quantile models
Cheng C, Hu L, Li F. Doubly robust estimation and sensitivity analysis for marginal structural quantile models. Biometrics 2024, 80: ujae045. PMID: 38884127, DOI: 10.1093/biomtc/ujae045.Peer-Reviewed Original ResearchMeSH KeywordsAntihypertensive AgentsBiometryComputer SimulationElectronic Health RecordsHumansHypertensionModels, StatisticalConceptsQuantile modelDistribution of potential outcomesEfficient influence functionPotential outcome distributionsDoubly robust estimatorsTime-varying treatmentsSequential ignorability assumptionSemiparametric frameworkIgnorability assumptionVariance estimationOutcome distributionInfluence functionRobust estimationPotential outcomesEfficient computationFunction approachTime-varying confoundersElectronic health record dataEstimationTreatment assignmentHealth record dataEffect of antihypertensive medicationEquationsRecord dataAntihypertensive medications
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 ResearchMeSH KeywordsBiasCluster AnalysisComputer SimulationHumansModels, StatisticalResearch DesignSample SizeConceptsSample 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 calculationA mixed model approach to estimate the survivor average causal effect in cluster‐randomized trials
Wang W, Tong G, Hirani S, Newman S, Halpern S, Small D, Li F, Harhay M. A mixed model approach to estimate the survivor average causal effect in cluster‐randomized trials. Statistics In Medicine 2023, 43: 16-33. PMID: 37985966, DOI: 10.1002/sim.9939.Peer-Reviewed Original ResearchORTH.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 ResearchMeSH KeywordsBiasCluster AnalysisComputer SimulationHumansLogistic ModelsLongitudinal StudiesModels, StatisticalConceptsOrdinal outcomesSandwich estimatorR packageSimulation studyCorrelated ordinal dataFinite sample biasesNumber of clustersCovariance estimationMarginal modelsEquationsParameter estimatesOrdinal responsesAssociation parametersCluster associationsBias correctionOrdinal dataEstimatorEstimating EquationsNominal levelMarginal meansResidualsEstimationPairwise odds ratiosAssociation modelGEE modelAccounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity
Tong J, Li F, Harhay M, Tong G. Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity. BMC Medical Research Methodology 2023, 23: 85. PMID: 37024809, PMCID: PMC10077680, DOI: 10.1186/s12874-023-01887-8.Peer-Reviewed Original ResearchConceptsSample size methodsSample size proceduresSize proceduresTreatment effect heterogeneityHeterogeneous treatment effectsSize methodMissingness ratesSample size formulaSample size estimationMissingness indicatorsEffect heterogeneityReal-world examplesSimulation studyIntracluster correlation coefficientInflation methodSize formulaAverage treatment effectResultsSimulation resultsSample size estimatesSize estimationMissingnessSample sizeClustersEstimationFormulaA Bayesian Approach for Estimating the Survivor Average Causal Effect When Outcomes Are Truncated by Death in Cluster-Randomized Trials
Tong G, Li F, Chen X, Hirani S, Newman S, Wang W, Harhay M. A Bayesian Approach for Estimating the Survivor Average Causal Effect When Outcomes Are Truncated by Death in Cluster-Randomized Trials. American Journal Of Epidemiology 2023, 192: 1006-1015. PMID: 36799630, PMCID: PMC10236525, DOI: 10.1093/aje/kwad038.Peer-Reviewed Original ResearchGEEMAEE: 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 ResearchMeSH KeywordsCluster AnalysisComputer SimulationLongitudinal StudiesModels, StatisticalRandomized Controlled Trials as TopicConceptsNumber 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
Simulating time-to-event data subject to competing risks and clustering: A review and synthesis
Meng C, Esserman D, Li F, Zhao Y, Blaha O, Lu W, Wang Y, Peduzzi P, Greene E. Simulating time-to-event data subject to competing risks and clustering: A review and synthesis. Statistical Methods In Medical Research 2022, 32: 305-333. PMID: 36412111, DOI: 10.1177/09622802221136067.Peer-Reviewed Original ResearchIs the Product Method More Efficient Than the Difference Method for Assessing Mediation?
Cheng C, Spiegelman D, Li F. Is the Product Method More Efficient Than the Difference Method for Assessing Mediation? American Journal Of Epidemiology 2022, 192: 84-92. PMID: 35921210, PMCID: PMC10144745, DOI: 10.1093/aje/kwac144.Peer-Reviewed Original ResearchMeSH KeywordsBiomedical ResearchEpidemiologic StudiesEswatiniHumansMediation AnalysisModels, StatisticalTwo weights make a wrong: Cluster randomized trials with variable cluster sizes and heterogeneous treatment effects
Wang X, Turner EL, Li F, Wang R, Moyer J, Cook AJ, Murray DM, Heagerty PJ. Two weights make a wrong: Cluster randomized trials with variable cluster sizes and heterogeneous treatment effects. Contemporary Clinical Trials 2022, 114: 106702. PMID: 35123029, PMCID: PMC8936048, DOI: 10.1016/j.cct.2022.106702.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationEarly Detection of CancerHumansModels, StatisticalRandomized Controlled Trials as TopicSample SizeConceptsInverse cluster sizeVariable cluster sizesCluster sizeCorrelation matrixTreatment effect estimatesCluster correlationEquation frameworkEstimation characteristicsTheoretical derivationSimulation studyAverage treatment effectHeterogeneous treatment effectsDistinct weightsEstimandsCluster levelHierarchical nestingMatrixHypothetical populationEstimatesValid resultsDerivationClustersConceptual populationEstimationEffect estimates
2021
Estimating 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 sizeImpact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial
Tong G, Seal KH, Becker WC, Li F, Dziura JD, Peduzzi PN, Esserman DA. Impact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial. Clinical Trials 2021, 19: 3-13. PMID: 34693748, PMCID: PMC8847260, DOI: 10.1177/17407745211051288.Peer-Reviewed Original ResearchConceptsGroup treatment trialsIntraclass correlation coefficientTreatment trialsTreatment sessionsHealth optionsEducation trialThree-armWhole health teamFuture trial designNumber of cliniciansGroup treatment designTrue intraclass correlation coefficientsGroup treatment sessionsTreatment armsClinical trialsClinician levelMultiple cliniciansBACKGROUND/Health teamsOutcome dataTreatment groupsTrial designGroup educationClinical scenariosDifferent cliniciansEstimating heterogeneous survival treatment effect in observational data using machine learning
Hu L, Ji J, Li F. Estimating heterogeneous survival treatment effect in observational data using machine learning. Statistics In Medicine 2021, 40: 4691-4713. PMID: 34114252, PMCID: PMC9827499, DOI: 10.1002/sim.9090.Peer-Reviewed Original ResearchSample 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 ResearchMeSH KeywordsCluster AnalysisComputer SimulationModels, StatisticalRandomized Controlled Trials as TopicRiskSample SizeConceptsSample 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
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 ResearchMeSH KeywordsCluster AnalysisCross-Over StudiesModels, StatisticalRandomized Controlled Trials as TopicResearch DesignStatistical Considerations for Embedded Pragmatic Clinical Trials in People Living with Dementia
Allore HG, Goldfeld KS, Gutman R, Li F, Monin JK, Taljaard M, Travison TG. Statistical Considerations for Embedded Pragmatic Clinical Trials in People Living with Dementia. Journal Of The American Geriatrics Society 2020, 68: s68-s73. PMID: 32589276, PMCID: PMC7396162, DOI: 10.1111/jgs.16616.Peer-Reviewed Original ResearchMeSH KeywordsCaregiversDelivery of Health CareDementiaHumansModels, StatisticalPragmatic Clinical Trials as TopicReproducibility of ResultsResearch DesignConceptsEmbedded Pragmatic Clinical TrialsPragmatic clinical trialsClinical trialsAlzheimer's diseaseImbedded Pragmatic Alzheimer's DiseaseIMPACT CollaboratoryOutcome ascertainmentPoint of careNonpharmacological interventionsReal-world settingComplex interventionsTrialsIntervention effectivenessVulnerable populationsPragmatic designHealthcare systemNational InstituteClinical scientistsSuch interventionsInterventionDementiaPLWDDiseaseCaregiversResearch priorities
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
Power and sample size requirements for GEE analyses of cluster randomized crossover trials
Li F, Forbes AB, Turner EL, Preisser JS. Power and sample size requirements for GEE analyses of cluster randomized crossover trials. Statistics In Medicine 2018, 38: 636-649. PMID: 30298551, PMCID: PMC6461037, DOI: 10.1002/sim.7995.Peer-Reviewed Original Research
2017
Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.
Turner EL, Prague M, Gallis JA, Li F, Murray DM. Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis. American Journal Of Public Health 2017, 107: 1078-1086. PMID: 28520480, PMCID: PMC5463203, DOI: 10.2105/ajph.2017.303707.Peer-Reviewed Original Research