2024
Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs
Wang X, Chen X, Goldfeld K, Taljaard M, Li F. Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs. Statistical Methods In Medical Research 2024, 33: 1115-1136. PMID: 38689556, PMCID: PMC11347095, DOI: 10.1177/09622802241247736.Peer-Reviewed Original ResearchCluster randomized crossover designSample size formulaTreatment effect heterogeneityAverage treatment effectHeterogeneity of treatment effectsSize formulaRandomized crossover designCluster-randomized crossover trialRandomized crossover trialEffect heterogeneitySampling schemeCluster randomized designTreatment effectsDifferential treatment effectsCrossover designFormulaContinuous outcomesLinear mixed modelsSample sizeCrossover trialInteraction testMixed modelsCovariatesClinical characteristicsStatistical methodsAssessing 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
Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity
Li F, Chen X, Tian Z, Wang R, Heagerty P. Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity. Statistics In Medicine 2023, 43: 890-911. PMID: 38115805, DOI: 10.1002/sim.9990.Peer-Reviewed Original ResearchTreatment effectsWedge designTreatment effect heterogeneityPatient subpopulationsLumbar imagingTreatment effect analysisWedge clusterWedge trialCandidate interventionsSubgroup treatment effectsCovariate adjustmentTrialsEffect heterogeneityAverage treatment effectRigorous evaluationFormal evaluationSample sizeResearch designSample 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 TrialSample 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 formulaCausal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia
Blette B, Granholm A, Li F, Shankar-Hari M, Lange T, Munch M, Møller M, Perner A, Harhay M. Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia. Scientific Reports 2023, 13: 6570. PMID: 37085591, PMCID: PMC10120498, DOI: 10.1038/s41598-023-33425-3.Peer-Reviewed Original ResearchConceptsCritical COVID-19Better long-term outcomesCOVID-19Entire trial populationStandardized dosing protocolsMultiple patient characteristicsDose of dexamethasoneLong-term outcomesIL-6 inhibitorsDexamethasone doseSevere hypoxemiaMost patientsPatient characteristicsRespiratory supportDiabetes mellitusClinical outcomesDosing protocolTrial populationTreatment effect heterogeneityPatient featuresPatientsAdditional studiesDexamethasoneDoseMore evidenceAccounting 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
Assessing 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 ResearchConceptsTreatment effect heterogeneityEffect heterogeneityParameter increasesTreatment effect parametersParametric functional formModel choicePermutation testModel formulationSimulation studyPrecise averageNew model formulationFunctional formEffect parametersRandom effectsTreatment effect estimatesCategorical termsVariance componentsDesigning 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 Research
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