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 methodsTransporting randomized trial results to estimate counterfactual survival functions in target populations
Cao Z, Cho Y, Li F. Transporting randomized trial results to estimate counterfactual survival functions in target populations. Pharmaceutical Statistics 2024, 23: 442-465. PMID: 38233102, DOI: 10.1002/pst.2354.Peer-Reviewed Original ResearchSurvival functionFinite-sample performanceSample average treatment effectApproximate variance estimatorsIncorrect model specificationAverage treatment effectRight censoringDistributions of treatment effect modifiersVariance estimationInverse probability weightingSimulation studyRobust estimationComplex surveysAverage treatmentProbability weightingTreatment effect modifiersEstimationTreatment effectsModel specificationCensoringTarget populationDifferential distributionSurvey weightsEffect modifiersFunction
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 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
Missing Data
Tong G, Li F, Allen A. Missing Data. 2022, 1681-1701. DOI: 10.1007/978-3-319-52636-2_117.Peer-Reviewed Original ResearchLikelihood-based analysisMissingness modelMissingness processData mechanismAverage treatment effectStatistical methodsComplete case analysisConsistent estimatesRobust approachInverse probability weightingBiased estimatesMissingnessOutcome distributionModeling approachProbability weightingData processSensitivity analysisOutcome modelModelEstimatesBrief discussionPractical considerationsInferenceApproachImputationGeneralizing Trial Evidence to Target Populations in Non-Nested Designs: Applications to AIDS Clinical Trials
Li F, Buchanan AL, Cole SR. Generalizing Trial Evidence to Target Populations in Non-Nested Designs: Applications to AIDS Clinical Trials. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2022, 71: 669-697. PMID: 35968541, PMCID: PMC9367209, DOI: 10.1111/rssc.12550.Peer-Reviewed Original ResearchAIDS Clinical Trials GroupTarget populationClinical Trials GroupComparative effectiveness evidenceTreatment effectsRandomized trialsTrial evidenceClinical trialsTrial groupHIV interventionsACTG trialsTrial participantsTrial designPropensity scoreEffectiveness evidenceTrialsAIDS clinical trialsSpecified populationPopulationAverage treatment effectMost casesHIVEvidenceRegressionTwo 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
2021
A note on semiparametric efficient generalization of causal effects from randomized trials to target populations
Li F, Hong H, Stuart E. A note on semiparametric efficient generalization of causal effects from randomized trials to target populations. Communication In Statistics- Theory And Methods 2021, 52: 5767-5798. PMID: 37484707, PMCID: PMC10361688, DOI: 10.1080/03610926.2021.2020291.Peer-Reviewed Original Research
2019
Missing Data
Tong G, Li F, Allen A. Missing Data. 2019, 1-21. DOI: 10.1007/978-3-319-52677-5_117-1.Peer-Reviewed Original ResearchLikelihood-based analysisMissingness modelMissingness processData mechanismAverage treatment effectStatistical methodsComplete case analysisConsistent estimatesRobust approachInverse probability weightingBiased estimatesMissingnessOutcome distributionModeling approachProbability weightingData processSensitivity analysisOutcome modelModelEstimatesBrief discussionPractical considerationsInferenceApproachImputation