2025
A Bayesian Approach to the G‐Formula via Iterative Conditional Regression
Liu R, Hu L, Wilson F, Warren J, Li F. A Bayesian Approach to the G‐Formula via Iterative Conditional Regression. Statistics In Medicine 2025, 44: e70123. PMID: 40476299, PMCID: PMC12184534, DOI: 10.1002/sim.70123.Peer-Reviewed Original ResearchConceptsCausal effect estimationTime-varying covariatesModel misspecification biasBayesian approachReal world data examplesG-formulaAverage causal effect estimationTime-varying treatmentsBayesian additive regression treesAverage causal effectAdditive regression treesConditional expectationOutcome regressionConditional distributionJoint distributionData examplesPosterior distributionMisspecification biasParametric regressionSimulation studyEffect estimatesSampling algorithmAlgorithm formulaCausal effectsFlexible machine learning techniquesA flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
Chen X, Hu L, Li F. A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding. Lifetime Data Analysis 2025, 31: 394-421. PMID: 40227517, DOI: 10.1007/s10985-025-09652-3.Peer-Reviewed Original ResearchConceptsG-formulaBalance scoresHealth system electronic health recordDiscrete survival dataTime-to-event outcomesPosterior sampling algorithmParametric g-formulaElectronic health recordsBayesian additive regression treesTime-varying treatmentsHypothetical intervention scenariosAdditive regression treesLongitudinal observational studyGeneral classModel misspecificationHealth recordsEmpirical performanceSampling algorithmObservational studySurvival dataIntervention scenariosScoresTreatment strategiesMisspecificationCausality analysis
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 ResearchMeSH KeywordsBuprenorphineCausalityHumansObservational Studies as TopicOpioid-Related DisordersResearch DesignMultiply robust estimation of principal causal effects with noncompliance and survival outcomes
Cheng C, Guo Y, Liu B, Wruck L, Li F, Li F. Multiply robust estimation of principal causal effects with noncompliance and survival outcomes. Clinical Trials 2024, 21: 553-561. PMID: 38813813, DOI: 10.1177/17407745241251773.Peer-Reviewed Original ResearchConceptsPrincipal strataRight-censored survival outcomesPrincipal causal effectsCausal effectsSensitivity analysis strategyPrincipal ignorabilityRobust estimationIdentification assumptionsCensoringPragmatic clinical trialsTreatment assignmentTreatment noncomplianceMonotonicityEstimationAssess treatment effectsCardiovascular diseaseClinical trialsMultipliersTreatment effectsAssumptionsNoncompliance
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 strategyEstimating heterogeneous survival treatment effect in observational data using machine learning
Hu L, Ji J, Li F. Estimating heterogeneous survival treatment effect in observational data using machine learning. Statistics In Medicine 2021, 40: 4691-4713. PMID: 34114252, PMCID: PMC9827499, DOI: 10.1002/sim.9090.Peer-Reviewed Original Research
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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