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 Trial
2021
Methodological challenges in pragmatic trials in Alzheimer’s disease and related dementias: Opportunities for improvement
Taljaard M, Li F, Qin B, Cui C, Zhang L, Nicholls SG, Carroll K, Mitchell SL. Methodological challenges in pragmatic trials in Alzheimer’s disease and related dementias: Opportunities for improvement. Clinical Trials 2021, 19: 86-96. PMID: 34841910, PMCID: PMC8847324, DOI: 10.1177/17407745211046672.Peer-Reviewed Original ResearchConceptsPragmatic trialAlzheimer's diseasePrimary outcomeSample size calculationGroup treatment trialsPairs of reviewersDementia researchSize calculationCluster Randomized TrialGroup treatment designRandomized trialsTreatment trialsIntracluster correlationMethodological qualityTrial reportsBaseline assessmentDiseaseTrialsDementiaKey methodological characteristicsOutcomesType IMethodological quality indicatorsUnique methodological challengesSame individualSample 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
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
2019
cvcrand: A Package for Covariate-constrained Randomization and the Clustered Permutation Test for Cluster Randomized Trials
Yu H, Li F, Gallis J, Turner E. cvcrand: A Package for Covariate-constrained Randomization and the Clustered Permutation Test for Cluster Randomized Trials. The R Journal 2019, 11: 191. DOI: 10.32614/rj-2019-027.Peer-Reviewed Original Research