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
Sample 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 ResearchConceptsSample 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 TrialAccounting 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 sizeClustersEstimationFormula
2022
Two 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 ResearchConceptsInverse cluster sizeVariable cluster sizesCluster sizeCorrelation matrixTreatment effect estimatesCluster correlationEquation frameworkEstimation characteristicsTheoretical derivationSimulation studyAverage treatment effectHeterogeneous treatment effectsDistinct weightsEstimandsCluster levelHierarchical nestingMatrixHypothetical populationEstimatesValid resultsDerivationClustersConceptual populationEstimationEffect estimates
2020
Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials
Yang S, Li F, Starks MA, Hernandez AF, Mentz RJ, Choudhury KR. Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials. Statistics In Medicine 2020, 39: 4218-4237. PMID: 32823372, PMCID: PMC7948251, DOI: 10.1002/sim.8721.Peer-Reviewed Original ResearchConceptsAnalysis of CRTsNumerous statistical methodsNew sample size formulaTreatment effect heterogeneitySample size proceduresFinite samplesSample size formulaStatistical methodsSize proceduresBinary covariateEffect heterogeneityEmpirical powerCovariates of interestEffect formulaParameter constellationsSize formulaAdjusted intraclass correlation coefficientsSample size requirementsExtensive simulationsHeterogeneous treatment effectsFormulaCovariate interactionsSize requirementsCluster Randomized TrialSample size