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 techniquesWeighting methods for truncation by death in cluster-randomized trials
Isenberg D, Harhay M, Mitra N, Li F. Weighting methods for truncation by death in cluster-randomized trials. Statistical Methods In Medical Research 2025, 34: 473-489. PMID: 39885759, PMCID: PMC11951466, DOI: 10.1177/09622802241309348.Peer-Reviewed Original ResearchConceptsSurvivor average causal effectAverage causal effectCluster randomized trialAsymptotic variance estimatorsSubgroup treatment effectsCausal effectsPrincipal stratification frameworkFinite-sampleVariance estimationDistributional assumptionsIdentification assumptionsStratification frameworkPatient-centered outcomesNon-mortality outcomesOutcome modelQuality of lifeRandomized trialsIll patient populationMeasurement time pointsTruncationEstimationLength of hospital stayAssumptionsSurvivorsPatient population
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 ResearchMultiply 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 effectsAssumptionsNoncomplianceCausal interpretation of the hazard ratio in randomized clinical trials
Fay M, Li F. Causal interpretation of the hazard ratio in randomized clinical trials. Clinical Trials 2024, 21: 623-635. PMID: 38679930, PMCID: PMC11502288, DOI: 10.1177/17407745241243308.Peer-Reviewed Original ResearchProportional hazards assumptionHazard ratioHazards assumptionConstant hazard ratioRandomized clinical trialsMeasure of treatment effectTime-varying effectsEstimandsRate ratiosUntestable assumptionsIndividual-levelPopulation-level interpretationCausal effectsClinical trialistsIndividual-level interpretationsClinical trialsAssumptionsCausal interpretationAverage changeTreatment effectsPotential outcomesA BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.
Chen X, Harhay M, Tong G, Li F. A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL. The Annals Of Applied Statistics 2024, 18: 350-374. PMID: 38455841, PMCID: PMC10919396, DOI: 10.1214/23-aoas1792.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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