2022
Design and analysis of cluster randomized trials with time‐to‐event outcomes under the additive hazards mixed model
Blaha O, Esserman D, Li F. Design and analysis of cluster randomized trials with time‐to‐event outcomes under the additive hazards mixed model. Statistics In Medicine 2022, 41: 4860-4885. PMID: 35908796, PMCID: PMC9588628, DOI: 10.1002/sim.9541.Peer-Reviewed Original ResearchConceptsSample size formulaCluster sizeNew sample size formulaSample size proceduresSize formulaEffect parametersSandwich variance estimatorStatistical inferenceCluster size variationEvent outcomesRandomization-based testsImproved inferenceSize proceduresTreatment effect parametersVariance estimatorSmall sample biasesAnalysis of clustersSimulation studyUnequal cluster sizesFrailty termVariance inflation factorFailure timeSample size requirementsMixed modelsAppropriate definitionEstimands in cluster-randomized trials: choosing analyses that answer the right question
Kahan BC, Li F, Copas AJ, Harhay MO. Estimands in cluster-randomized trials: choosing analyses that answer the right question. International Journal Of Epidemiology 2022, 52: 107-118. PMID: 35834775, PMCID: PMC9908044, DOI: 10.1093/ije/dyac131.Peer-Reviewed Original ResearchConceptsInformative cluster sizeCluster sizeCommon estimatorsCorrelation structureAlternative estimatorsEstimatorUnbiased estimatesBiased estimatesEstimandsDifferent estimandsTarget estimandAnalytic approachCareful specificationLarge clustersEquationsDifferent analytic approachesEstimatesMixed-effects modelsPower Analysis for Cluster Randomized Trials with Continuous Coprimary Endpoints
Yang S, Moerbeek M, Taljaard M, Li F. Power Analysis for Cluster Randomized Trials with Continuous Coprimary Endpoints. Biometrics 2022, 79: 1293-1305. PMID: 35531926, DOI: 10.1111/biom.13692.Peer-Reviewed Original ResearchConceptsMultivariate linear mixed modelTreatment effect estimatorJoint distributionEqual cluster sizesCluster sizeExpectation-maximization algorithmFinite numberEffects estimatorEmpirical powerCorrelation parametersPower analysisEstimatorSize assumptionsSample sizeNull hypothesisPower calculationPower determinationLinear mixed modelsParametersMixed modelsTwo 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
Sample 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 ResearchConceptsSample 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 outcomes