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 ResearchMeSH KeywordsCluster AnalysisComputer SimulationHumansLinear ModelsMonte Carlo MethodRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsSample 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
2017
An evaluation of constrained randomization for the design and analysis of group‐randomized trials with binary outcomes
Li F, Turner EL, Heagerty PJ, Murray DM, Vollmer WM, DeLong ER. An evaluation of constrained randomization for the design and analysis of group‐randomized trials with binary outcomes. Statistics In Medicine 2017, 36: 3791-3806. PMID: 28786223, PMCID: PMC5624845, DOI: 10.1002/sim.7410.Peer-Reviewed Original ResearchConceptsGroup-level covariatesPossible allocation schemesMonte Carlo simulationsStatistical propertiesRandomization-based testsStatistical issuesCarlo simulationsPrespecified percentageAllocation schemeStatistical testsCandidate allocationsSpaceBinary outcomesAllocation techniquePermutation testPractical limitationsPower lossSchemeSuch designsGroup-randomized trialLarge numberF-testContinuous outcomesCovariate imbalanceInference