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, 9622802241247736. PMID: 38689556, 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 methodsModel-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments
Wang B, Park C, Small D, Li F. Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments. Journal Of The American Statistical Association 2024, ahead-of-print: 1-13. DOI: 10.1080/01621459.2023.2289693.Peer-Reviewed Original ResearchCluster-randomized experimentCluster size variationNuisance functionsParametric working modelsFlexible covariate adjustmentCovariate-adjusted estimatesCovariate adjustment methodsCovariate adjustmentModel-based covariate adjustmentEfficient estimationSimulation studyRobust inferenceSupplementary materialsEstimandsEstimating equationsModel-based estimatesG-computationCluster averagesEstimationTreatment assignmentRoutine practice conditionsRisk of biasEquationsTreatment effectsCovariates