2023
Sample 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 formulaAccounting 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
Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes
Tian Z, Esserman D, Tong G, Blaha O, Dziura J, Peduzzi P, Li F. Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes. Statistics In Medicine 2022, 41: 645-664. PMID: 34978097, PMCID: PMC8962918, DOI: 10.1002/sim.9284.Peer-Reviewed Original Research
2021
Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity
Tong G, Esserman D, Li F. Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity. Statistics In Medicine 2021, 41: 1376-1396. PMID: 34923655, PMCID: PMC10197222, DOI: 10.1002/sim.9283.Peer-Reviewed Original ResearchImpact 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 cliniciansSample 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 outcomesSample size and power considerations for cluster randomized trials with count outcomes subject to right truncation
Li F, Tong G. Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation. Biometrical Journal 2021, 63: 1052-1071. PMID: 33751620, PMCID: PMC9132617, DOI: 10.1002/bimj.202000230.Peer-Reviewed Original ResearchConceptsCluster Randomized TrialPrimary outcomeGroup-based interventionRandomized trialsHealth StudySuch trialsPublic health studiesRight truncationTrialsOutcomesVector-borne diseasesCountSample size formulaAnalysis of CRTsPower calculationPopulation-level effectsSample sizeSize formulaClosed-form sample size formulaMarginal modeling approachMalariaDisease