Doubly robust estimation and sensitivity analysis for marginal structural quantile models
Cheng C, Hu L, Li F. Doubly robust estimation and sensitivity analysis for marginal structural quantile models. Biometrics 2024, 80: ujae045. PMID: 38884127, DOI: 10.1093/biomtc/ujae045.Peer-Reviewed Original ResearchConceptsQuantile modelDistribution of potential outcomesEfficient influence functionPotential outcome distributionsDoubly robust estimatorsTime-varying treatmentsSequential ignorability assumptionSemiparametric frameworkIgnorability assumptionVariance estimationOutcome distributionInfluence functionRobust estimationPotential outcomesEfficient computationFunction approachTime-varying confoundersElectronic health record dataEstimationTreatment assignmentHealth record dataEffect of antihypertensive medicationEquationsRecord dataAntihypertensive medicationsModel-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