2024
Four targets: an enhanced framework for guiding causal inference from observational data
Lu H, Li F, Lesko C, Fink D, Rudolph K, Harhay M, Rentsch C, Fiellin D, Gonsalves G. Four targets: an enhanced framework for guiding causal inference from observational data. International Journal Of Epidemiology 2024, 54: dyaf003. PMID: 39868475, PMCID: PMC11769716, DOI: 10.1093/ije/dyaf003.Peer-Reviewed Original Research
2021
Clarifying selection bias in cluster randomized trials
Li F, Tian Z, Bobb J, Papadogeorgou G, Li F. Clarifying selection bias in cluster randomized trials. Clinical Trials 2021, 19: 33-41. PMID: 34894795, DOI: 10.1177/17407745211056875.Peer-Reviewed Original ResearchConceptsAverage treatment effectCluster randomized trialPost-randomization selection biasPrincipal strataAnalysis of cluster randomized trialsSelection biasCausal effectsCovariate adjustment methodsData generating processRecruited populationPrincipal stratification frameworkPresence of selection biasHeterogeneous treatment effectsRegression adjustment methodEstimate causal effectsRandomized trialsElectronic health recordsOverall populationEffect heterogeneityIntention-to-treat analysisSimulation studyTreatment effectsEmpirical performanceEstimandsEstimation strategy
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 score
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