2025
Addressing selection bias in cluster randomized experiments via weighting
Papadogeorgou G, Liu B, Li F, Li F. Addressing selection bias in cluster randomized experiments via weighting. Biometrics 2025, 81: ujaf013. PMID: 40052595, DOI: 10.1093/biomtc/ujaf013.Peer-Reviewed Original ResearchConceptsCluster-randomized experimentCluster randomized trialAverage treatment effectSelection biasInverse probability weightingOverall populationTreatment effectsCo-paymentControl armRecruited populationProbability weightingRandomized experimentRandomized trialsPopulationEstimation strategyTreatment assignmentIndividualsRecruitment assumptionR packageOverallAnalysis approachInterventionRecruitment
2024
Transporting randomized trial results to estimate counterfactual survival functions in target populations
Cao Z, Cho Y, Li F. Transporting randomized trial results to estimate counterfactual survival functions in target populations. Pharmaceutical Statistics 2024, 23: 442-465. PMID: 38233102, DOI: 10.1002/pst.2354.Peer-Reviewed Original ResearchSurvival functionFinite-sample performanceSample average treatment effectApproximate variance estimatorsIncorrect model specificationAverage treatment effectRight censoringDistributions of treatment effect modifiersVariance estimationInverse probability weightingSimulation studyRobust estimationComplex surveysAverage treatmentProbability weightingTreatment effect modifiersEstimationTreatment effectsModel specificationCensoringTarget populationDifferential distributionSurvey weightsEffect modifiersFunction
2022
Missing Data
Tong G, Li F, Allen A. Missing Data. 2022, 1681-1701. DOI: 10.1007/978-3-319-52636-2_117.Peer-Reviewed Original ResearchLikelihood-based analysisMissingness modelMissingness processData mechanismAverage treatment effectStatistical methodsComplete case analysisConsistent estimatesRobust approachInverse probability weightingBiased estimatesMissingnessOutcome distributionModeling approachProbability weightingData processSensitivity analysisOutcome modelModelEstimatesBrief discussionPractical considerationsInferenceApproachImputation
2019
Propensity score weighting for causal inference with multiple treatments
Li F, Li F. Propensity score weighting for causal inference with multiple treatments. The Annals Of Applied Statistics 2019, 13: 2389-2415. DOI: 10.1214/19-aoas1282.Peer-Reviewed Original ResearchGeneralized propensity scoreInverse probability weightingSandwich variance estimatorOverlap weightingProbability weightingEstimate causal effectsCausal effectsCovariate distributionsVariance estimationBalance weightTarget estimandWeight estimationLinear contrastsMultiple groupsWeighting frameworkHarmonic meanMedical Expenditure Panel SurveyTrim weightEstimationEstimandsWeighting schemeMultiple treatmentsTarget populationHealth services researchPropensity scoreMissing Data
Tong G, Li F, Allen A. Missing Data. 2019, 1-21. DOI: 10.1007/978-3-319-52677-5_117-1.Peer-Reviewed Original ResearchLikelihood-based analysisMissingness modelMissingness processData mechanismAverage treatment effectStatistical methodsComplete case analysisConsistent estimatesRobust approachInverse probability weightingBiased estimatesMissingnessOutcome distributionModeling approachProbability weightingData processSensitivity analysisOutcome modelModelEstimatesBrief discussionPractical considerationsInferenceApproachImputation
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