2024
Assessing 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 ResearchMeSH KeywordsBayes TheoremBiasCluster AnalysisComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalRandomized Controlled Trials as TopicTreatment OutcomeConceptsMultilevel 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
Accounting 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 ResearchMeSH KeywordsCluster AnalysisComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsSample 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 sizeClustersEstimationFormulaEliminating Ambiguous Treatment Effects Using Estimands
Kahan B, Cro S, Li F, Harhay M. Eliminating Ambiguous Treatment Effects Using Estimands. American Journal Of Epidemiology 2023, 192: 987-994. PMID: 36790803, PMCID: PMC10236519, DOI: 10.1093/aje/kwad036.Peer-Reviewed Original Research
2019
Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness
Turner EL, Yao L, Li F, Prague M. Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness. Statistical Methods In Medical Research 2019, 29: 1338-1353. PMID: 31293199, DOI: 10.1177/0962280219859915.Peer-Reviewed Original ResearchMeSH KeywordsBiasCluster AnalysisComputer SimulationData Interpretation, StatisticalModels, StatisticalProbabilityRandomized Controlled Trials as Topic
2018
Power and sample size requirements for GEE analyses of cluster randomized crossover trials
Li F, Forbes AB, Turner EL, Preisser JS. Power and sample size requirements for GEE analyses of cluster randomized crossover trials. Statistics In Medicine 2018, 38: 636-649. PMID: 30298551, PMCID: PMC6461037, DOI: 10.1002/sim.7995.Peer-Reviewed Original ResearchCross-Over StudiesData Interpretation, StatisticalHumansModels, StatisticalRandomized Controlled Trials as TopicSample SizeTreatment Outcome