2024
Demystifying estimands in cluster-randomised trials
Kahan B, Blette B, Harhay M, Halpern S, Jairath V, Copas A, Li F. Demystifying estimands in cluster-randomised trials. Statistical Methods In Medical Research 2024, 33: 1211-1232. PMID: 38780480, PMCID: PMC11348634, DOI: 10.1177/09622802241254197.Peer-Reviewed Original ResearchCluster randomised trialPotential outcomes notationTreatment effect estimatesOverview of estimationPublished cluster randomised trialsCluster-level summariesTarget estimandEstimandsTreatment effectsEffect estimatesInterpretation of treatment effectsOdds ratioEstimationRandomised trialsStudy objectiveSample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs
Wang X, Chen X, Goldfeld K, Taljaard M, Li F. Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs. Statistical Methods In Medical Research 2024, 33: 1115-1136. PMID: 38689556, PMCID: PMC11347095, DOI: 10.1177/09622802241247736.Peer-Reviewed Original ResearchCluster randomized crossover designSample size formulaTreatment effect heterogeneityAverage treatment effectHeterogeneity of treatment effectsSize formulaRandomized crossover designCluster-randomized crossover trialRandomized crossover trialEffect heterogeneitySampling schemeCluster randomized designTreatment effectsDifferential treatment effectsCrossover designFormulaContinuous outcomesLinear mixed modelsSample sizeCrossover trialInteraction testMixed modelsCovariatesClinical characteristicsStatistical methodsCausal interpretation of the hazard ratio in randomized clinical trials.
Fay M, Li F. Causal interpretation of the hazard ratio in randomized clinical trials. Clinical Trials 2024, 21: 623-635. PMID: 38679930, PMCID: PMC11502288, DOI: 10.1177/17407745241243308.Peer-Reviewed Original ResearchProportional hazards assumptionHazard ratioHazards assumptionConstant hazard ratioRandomized clinical trialsMeasure of treatment effectTime-varying effectsEstimandsRate ratiosUntestable assumptionsIndividual-levelPopulation-level interpretationCausal effectsClinical trialistsIndividual-level interpretationsClinical trialsAssumptionsCausal interpretationAverage changeTreatment effectsPotential outcomesAssessing 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 dataTrialsTransporting randomized trial results to estimate counterfactual survival functions in target populations
Cao Z, Cho Y, Li F. Transporting randomized trial results to estimate counterfactual survival functions in target populations. Pharmaceutical Statistics 2024, 23: 442-465. PMID: 38233102, DOI: 10.1002/pst.2354.Peer-Reviewed Original ResearchSurvival functionFinite-sample performanceSample average treatment effectApproximate variance estimatorsIncorrect model specificationAverage treatment effectRight censoringDistributions of treatment effect modifiersVariance estimationInverse probability weightingSimulation studyRobust estimationComplex surveysAverage treatmentProbability weightingTreatment effect modifiersEstimationTreatment effectsModel specificationCensoringTarget populationDifferential distributionSurvey weightsEffect modifiersFunctionModel-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, ahead-of-print: 1-13. 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 effectsCovariatesCorrection: Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials
Wang X, Goldfeld K, Taljaard M, Li F. Correction: Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials. Prevention Science 2024, 25: 1004-1004. PMID: 38180545, PMCID: PMC11390812, DOI: 10.1007/s11121-023-01615-0.Peer-Reviewed Original Research
2023
Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity
Li F, Chen X, Tian Z, Wang R, Heagerty P. Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity. Statistics In Medicine 2023, 43: 890-911. PMID: 38115805, DOI: 10.1002/sim.9990.Peer-Reviewed Original ResearchTreatment effectsWedge designTreatment effect heterogeneityPatient subpopulationsLumbar imagingTreatment effect analysisWedge clusterWedge trialCandidate interventionsSubgroup treatment effectsCovariate adjustmentTrialsEffect heterogeneityAverage treatment effectRigorous evaluationFormal evaluationSample sizeResearch designInformative 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 structureSample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances
Tong G, Taljaard M, Li F. Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances. Statistics In Medicine 2023, 42: 3392-3412. PMID: 37316956, DOI: 10.1002/sim.9811.Peer-Reviewed Original ResearchConceptsGroup treatment trialsTreatment effect modificationRandomized trialsTreatment trialsEffect modificationEffect modifiersIntracluster correlation coefficientIndividual-level effect modifiersStudy armsTreatment effect heterogeneityOutcome observationsContinuous outcomesTrialsGroup treatmentTreatment effectsOutcome varianceEffect heterogeneityIntracluster correlationSample sizeSample size formulaIs low-risk status a surrogate outcome in pulmonary arterial hypertension? An analysis of three randomised trials
Blette B, Moutchia J, Al-Naamani N, Ventetuolo C, Cheng C, Appleby D, Urbanowicz R, Fritz J, Mazurek J, Li F, Kawut S, Harhay M. Is low-risk status a surrogate outcome in pulmonary arterial hypertension? An analysis of three randomised trials. The Lancet Respiratory Medicine 2023, 11: 873-882. PMID: 37230098, PMCID: PMC10592525, DOI: 10.1016/s2213-2600(23)00155-8.Peer-Reviewed Original ResearchConceptsPulmonary arterial hypertensionPulmonary arterial hypertension trialsWorsening pulmonary arterial hypertensionFood and Drug AdministrationLow-risk statusClinical worseningLong-term outcomesRisk scoreArterial hypertensionPAH associated with connective tissue diseaseIdiopathic pulmonary arterial hypertensionPulmonary arterial hypertension treatmentSurrogate outcomesObservational study of outcomesLong-term follow-upDiscontinuation of study treatmentWHO functional classUS Food and Drug AdministrationMeta-analysisMeta-analysis of RCTsAll-cause deathConnective tissue diseaseEffects of therapyPredictive of outcomeTreatment effects
2022
Designing three-level cluster randomized trials to assess treatment effect heterogeneity
Li F, Chen X, Tian Z, Esserman D, Heagerty PJ, Wang R. Designing three-level cluster randomized trials to assess treatment effect heterogeneity. Biostatistics 2022, 24: 833-849. PMID: 35861621, PMCID: PMC10583727, DOI: 10.1093/biostatistics/kxac026.Peer-Reviewed Original ResearchGeneralizing Trial Evidence to Target Populations in Non-Nested Designs: Applications to AIDS Clinical Trials
Li F, Buchanan AL, Cole SR. Generalizing Trial Evidence to Target Populations in Non-Nested Designs: Applications to AIDS Clinical Trials. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2022, 71: 669-697. PMID: 35968541, PMCID: PMC9367209, DOI: 10.1111/rssc.12550.Peer-Reviewed Original ResearchAIDS Clinical Trials GroupTarget populationClinical Trials GroupComparative effectiveness evidenceTreatment effectsRandomized trialsTrial evidenceClinical trialsTrial groupHIV interventionsACTG trialsTrial participantsTrial designPropensity scoreEffectiveness evidenceTrialsAIDS clinical trialsSpecified populationPopulationAverage treatment effectMost casesHIVEvidenceRegression
2021
A note on semiparametric efficient generalization of causal effects from randomized trials to target populations
Li F, Hong H, Stuart E. A note on semiparametric efficient generalization of causal effects from randomized trials to target populations. Communication In Statistics- Theory And Methods 2021, 52: 5767-5798. PMID: 37484707, PMCID: PMC10361688, DOI: 10.1080/03610926.2021.2020291.Peer-Reviewed Original Research