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 ResearchMeSH KeywordsBayes TheoremBiasCluster AnalysisComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalRandomized Controlled Trials as TopicTreatment OutcomeConceptsMultilevel 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
A 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 ResearchAgedHumansModels, StatisticalOutcome Assessment, Health CareQuality of LifeRandomized Controlled Trials as TopicSurvivorsSample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes
Maleyeff L, Wang R, Haneuse S, Li F. Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes. Statistics In Medicine 2023, 42: 5054-5083. PMID: 37974475, PMCID: PMC10659142, DOI: 10.1002/sim.9901.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationHumansLinear ModelsMonte Carlo MethodRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsSample size proceduresSize proceduresEfficient Monte Carlo approachTreatment effect heterogeneitySample size methodsMonte Carlo approachContinuous effect modifiersBinary outcomesEffect heterogeneityCarlo approachNumerical illustrationsNecessary sample sizeGeneralized linear mixed modelLinear mixed modelsPopular classSample size requirementsStatistical powerAverage treatment effectHeterogeneous treatment effectsSample size calculationMixed modelsSize methodSize calculationSize requirementsCluster Randomized TrialMaximin optimal cluster randomized designs for assessing treatment effect heterogeneity
Ryan M, Esserman D, Li F. Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity. Statistics In Medicine 2023, 42: 3764-3785. PMID: 37339777, PMCID: PMC10510425, DOI: 10.1002/sim.9830.Peer-Reviewed Original ResearchSample 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 ResearchMeSH KeywordsEpoprostenolFamilial Primary Pulmonary HypertensionFemaleHumansMaleMiddle AgedPulmonary Arterial HypertensionRandomized Controlled Trials as TopicRisk FactorsConceptsPulmonary 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 effectsAccounting 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 ResearchMeSH KeywordsCluster AnalysisComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsSample 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 sizeClustersEstimationFormulaAccounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control
Ouyang Y, Kulkarni M, Protopopoff N, Li F, Taljaard M. Accounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control. BMC Medical Research Methodology 2023, 23: 64. PMID: 36932347, PMCID: PMC10021932, DOI: 10.1186/s12874-023-01871-2.Peer-Reviewed Original ResearchAnimalsAnophelesCluster AnalysisCross-Sectional StudiesHumansMalariaMosquito VectorsRandomized Controlled Trials as TopicSample SizeA scoping review described diversity in methods of randomization and reporting of baseline balance in stepped-wedge cluster randomized trials
Nevins P, Davis-Plourde K, Pereira Macedo J, Ouyang Y, Ryan M, Tong G, Wang X, Meng C, Ortiz-Reyes L, Li F, Caille A, Taljaard M. A scoping review described diversity in methods of randomization and reporting of baseline balance in stepped-wedge cluster randomized trials. Journal Of Clinical Epidemiology 2023, 157: 134-145. PMID: 36931478, PMCID: PMC10546924, DOI: 10.1016/j.jclinepi.2023.03.010.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisCross-Sectional StudiesHumansRandom AllocationRandomized Controlled Trials as TopicResearch DesignConceptsStepped-wedge clusterIndividual-level characteristicsMethod of randomizationCross-sectional designControl armBaseline imbalancesCohort designMedian numberElectronic searchPrimary analysisBaseline balanceStudy designPrimary reportsBaselineTrialsIntervention conditionSW-CRTsRandomizationReportingA 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 ResearchAgedBayes TheoremHumansModels, StatisticalQuality of LifeRandomized Controlled Trials as TopicSurvivorsGEEMAEE: 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
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 ResearchPower analyses for stepped wedge designs with multivariate continuous outcomes
Davis‐Plourde K, Taljaard M, Li F. Power analyses for stepped wedge designs with multivariate continuous outcomes. Statistics In Medicine 2022, 42: 559-578. PMID: 36565050, PMCID: PMC9985483, DOI: 10.1002/sim.9632.Peer-Reviewed Original ResearchConceptsMultivariate outcomesMultivariate linear mixed modelIntracluster correlation coefficientSample size proceduresClosed cohort designRigorous justificationSample size calculation procedureTreatment effect estimatorJoint distributionSize proceduresTest statisticLinear mixed modelsEfficient treatment effect estimatorsCommon treatment effectMixed modelsCalculation procedureExtensive simulationsEffects estimatorIntersection-union testPower analysisEstimatorWedge designEfficient powerModelContinuous outcomesAssessing Exposure-Time Treatment Effect Heterogeneity in Stepped-Wedge Cluster Randomized Trials
Maleyeff L, Li F, Haneuse S, Wang R. Assessing Exposure-Time Treatment Effect Heterogeneity in Stepped-Wedge Cluster Randomized Trials. Biometrics 2022, 79: 2551-2564. PMID: 36416302, PMCID: PMC10203056, DOI: 10.1111/biom.13803.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisCross-Over StudiesRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsTreatment effect heterogeneityEffect heterogeneityParameter increasesTreatment effect parametersParametric functional formModel choicePermutation testModel formulationSimulation studyPrecise averageNew model formulationFunctional formEffect parametersRandom effectsTreatment effect estimatesCategorical termsVariance componentsA 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 ResearchMeSH KeywordsBiasCluster AnalysisComputer SimulationHumansRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsSample 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 definitionDesigning 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 ResearchMeSH KeywordsCluster AnalysisComputer SimulationHumansRandomized Controlled Trials as TopicResearch DesignSample SizeEstimands in cluster-randomized trials: choosing analyses that answer the right question
Kahan BC, Li F, Copas AJ, Harhay MO. Estimands in cluster-randomized trials: choosing analyses that answer the right question. International Journal Of Epidemiology 2022, 52: 107-118. PMID: 35834775, PMCID: PMC9908044, DOI: 10.1093/ije/dyac131.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationHumansRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsInformative cluster sizeCluster sizeCommon estimatorsCorrelation structureAlternative estimatorsEstimatorUnbiased estimatesBiased estimatesEstimandsDifferent estimandsTarget estimandAnalytic approachCareful specificationLarge clustersEquationsDifferent analytic approachesEstimatesMixed-effects modelsSample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison
Ouyang Y, Li F, Preisser JS, Taljaard M. Sample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison. International Journal Of Epidemiology 2022, 51: 2000-2013. PMID: 35679584, PMCID: PMC9749719, DOI: 10.1093/ije/dyac123.Peer-Reviewed Original ResearchPower Analysis for Cluster Randomized Trials with Continuous Coprimary Endpoints
Yang S, Moerbeek M, Taljaard M, Li F. Power Analysis for Cluster Randomized Trials with Continuous Coprimary Endpoints. Biometrics 2022, 79: 1293-1305. PMID: 35531926, PMCID: PMC11321238, DOI: 10.1111/biom.13692.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisComputer SimulationLinear ModelsRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsMultivariate linear mixed modelTreatment effect estimatorJoint distributionEqual cluster sizesCluster sizeExpectation-maximization algorithmFinite numberEffects estimatorEmpirical powerCorrelation parametersPower analysisEstimatorSize assumptionsSample sizeNull hypothesisPower calculationPower determinationLinear mixed modelsParametersMixed models