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 techniques
2024
Exposure range matters: considering nonlinear associations in the meta-analysis of environmental pollutant exposure using examples of per- and polyfluoroalkyl substances and birth outcomes
Guo P, Warren J, Deziel N, Liew Z. Exposure range matters: considering nonlinear associations in the meta-analysis of environmental pollutant exposure using examples of per- and polyfluoroalkyl substances and birth outcomes. American Journal Of Epidemiology 2024, 194: 1043-1051. PMID: 39227151, DOI: 10.1093/aje/kwae309.Peer-Reviewed Original ResearchNon-linear associationMeta-analysisHeterogeneity of effect estimatesEffect estimatesPotential non-linear associationsExposure-outcome relationshipsRisk of preterm birthSubgroup meta-analysisBirth outcomesPollution exposureMeta-analytic approachMeta-analysesEffect sizeEnvironmental pollutant exposureResults heterogeneityExposure levelsHealth effectsPreterm birthEvidence-based policymakingPrenatal exposurePotential heterogeneityMean birth weightBirth weightMethodological challengesCut-off choice
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply