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
Power 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 outcomes
2021
swdpwr: A SAS macro and an R package for power calculations in stepped wedge cluster randomized trials
Chen J, Zhou X, Li F, Spiegelman D. swdpwr: A SAS macro and an R package for power calculations in stepped wedge cluster randomized trials. Computer Methods And Programs In Biomedicine 2021, 213: 106522. PMID: 34818620, PMCID: PMC8665077, DOI: 10.1016/j.cmpb.2021.106522.Peer-Reviewed Original ResearchConceptsWedge clusterIntracluster correlation coefficientContinuous outcomesCross-sectional cohortBinary outcomesExchangeable correlation structureWedge designPublic health intervention evaluationsHealth services researchClosed cohort designPower calculationCohort designClosed cohortStudy designIntracluster correlationIntervention evaluationNeeds of investigatorsOutcomesTrialsCohortServices researchInvestigatorsPrevious studiesSWD