2024
Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures
Ouyang Y, Taljaard M, Forbes A, Li F. Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures. Statistical Methods In Medical Research 2024, 33: 1497-1516. PMID: 38807552, PMCID: PMC11499024, DOI: 10.1177/09622802241248382.Peer-Reviewed Original ResearchRandom effects structureVariance estimationComplex correlation structureRobust variance estimationFixed effects parametersDegrees of freedom correctionCluster randomized trialEstimates of standard errorsCorrelation structureRandom effectsStepped-wedge cluster randomized trialComprehensive simulation studyLinear mixed modelsStatistical inferenceRandom intercept modelSimulation studyMixed modelsMisspecificationValidity of inferencesRandom interceptContinuous outcomesEstimationComputational challengesIntercept modelStandard error
2023
GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials
Zhang Y, Preisser J, Li F, Turner E, Toles M, Rathouz P. GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials. Computer Methods And Programs In Biomedicine 2023, 230: 107362. PMID: 36709555, PMCID: PMC10037297, DOI: 10.1016/j.cmpb.2023.107362.Peer-Reviewed Original ResearchConceptsNumber of clustersBias-corrected estimationCorrelation structurePopulation-averaged interpretationMarginal regression modelsDeletion diagnosticsEstimating EquationsFinite-sample adjustmentInfluence of observationsLarge valuesStandard errorEquationsSandwich estimatorVariance estimatorCook's distanceSAS macroDesign of clusterCount outcomesLongitudinal responseCorrelation parametersValid inferencesCorrelated outcomesFlexible specificationBiased estimatesEstimator
2018
Addressing Extreme Propensity Scores via the Overlap Weights
Li F, Thomas L, Li F. Addressing Extreme Propensity Scores via the Overlap Weights. American Journal Of Epidemiology 2018, 188: 250-257. PMID: 30189042, DOI: 10.1093/aje/kwy201.Peer-Reviewed Original ResearchConceptsPropensity score distributionInverse probability weighting methodConfidence interval coverageOverlap weightingProbability weighting methodTreatment effect estimatesPropensity scoreInterval coverageScore distributionEffect estimatesTarget populationOverlap weighting methodExcess varianceInverse probabilityCutoff pointStandard error
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