A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth
Comess S, Chang HH, Warren JL. A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth. Biostatistics 2022, 25: 20-39. PMID: 35984351, PMCID: PMC10724312, DOI: 10.1093/biostatistics/kxac034.Peer-Reviewed Original ResearchConceptsFull conditional distributionsEfficient model fittingStatistical modeling approachDensity estimation approachBayesian settingKernel density estimation approachPosterior outputBayesian frameworkConditional distributionModel fittingEstimation approachAccurate inferenceKDE approachModeling approachComparison metricsExposure uncertaintyUncertaintySecond stageApproachFittingInferencePredictionSimulationsModel comparison metricsFirst stageCritical window variable selection: estimating the impact of air pollution on very preterm birth
Warren JL, Kong W, Luben TJ, Chang HH. Critical window variable selection: estimating the impact of air pollution on very preterm birth. Biostatistics 2019, 21: 790-806. PMID: 30958877, PMCID: PMC7422642, DOI: 10.1093/biostatistics/kxz006.Peer-Reviewed Original ResearchConceptsHierarchical Bayesian frameworkBayesian frameworkStatistical modelVariable selectionImproved estimationCritical windowPreterm birthRisk parametersVery preterm birthAdverse birth outcomesControl analysisExposure-disease relationshipsDifferent reproductive outcomesBirth outcomesPregnant womenReproductive outcomesCase/control analysis