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
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