2023
ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals
Meng C, Ryan M, Rathouz P, Turner E, Preisser J, Li F. ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals. Computer Methods And Programs In Biomedicine 2023, 237: 107567. PMID: 37207384, DOI: 10.1016/j.cmpb.2023.107567.Peer-Reviewed Original ResearchConceptsOrdinal outcomesSandwich estimatorR packageSimulation studyCorrelated ordinal dataFinite sample biasesNumber of clustersCovariance estimationMarginal modelsEquationsParameter estimatesOrdinal responsesAssociation parametersCluster associationsBias correctionOrdinal dataEstimatorEstimating EquationsNominal levelMarginal meansResidualsEstimationPairwise odds ratiosAssociation modelGEE model
2021
Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes
Li F, Yu H, Rathouz PJ, Turner EL, Preisser JS. Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes. Biostatistics 2021, 23: 772-788. PMID: 33527999, PMCID: PMC9291643, DOI: 10.1093/biostatistics/kxaa056.Peer-Reviewed Original ResearchConceptsPopulation-averaged interpretationFinite sample inferenceMarginal inferenceMarginal meansRigorous justificationBinary outcomesComputational burdenIndividual-level observationsMarginal modelsInterval estimationMarginal modelingCorrelated binary outcomesCluster-period sizesJoint estimationEquationsLinear modelEstimating EquationsSW-CRTsFlexible toolFast pointInferenceEstimationAdditional mappingModelApproach