2019
Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction?
Wang X, Mukherjee B, Park S. Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction? Journal Of The American Heart Association 2019, 8: e013571. PMID: 31631727, PMCID: PMC6898859, DOI: 10.1161/jaha.119.013571.Peer-Reviewed Original ResearchConceptsCardiovascular diseaseNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk factorsStudy sampleCardiovascular disease risk factorsCardiovascular disease mortalityCardiovascular disease risk assessmentImprove CVD risk predictionC-statisticNutrition Examination SurveyCardiovascular mortality predictionCVD risk predictionCox modelBlood markersExamination SurveyPrecision healthRisk scorePairwise interaction termsBlood metalsIntegrated discrimination improvementRisk predictionReclassification improvementMortality predictionInteraction terms
2016
Mediation of the Relationship between Maternal Phthalate Exposure and Preterm Birth by Oxidative Stress with Repeated Measurements across Pregnancy
Ferguson K, Chen Y, VanderWeele T, McElrath T, Meeker J, Mukherjee B. Mediation of the Relationship between Maternal Phthalate Exposure and Preterm Birth by Oxidative Stress with Repeated Measurements across Pregnancy. Environmental Health Perspectives 2016, 125: 488-494. PMID: 27352406, PMCID: PMC5332184, DOI: 10.1289/ehp282.Peer-Reviewed Original ResearchConceptsExposure-mediator interactionPreterm birthMaternal phthalate exposureNested case-control study of preterm birthCase-control study of preterm birthNested case-control studyPhthalate exposureStudy of preterm birthPhthalate metabolitesOxidative stressSpontaneous preterm birthLongitudinal measurementsMetabolites of di(2-ethylhexyl) phthalateOdds ratioRepeated measuresDi(2-ethylhexyl) phthalateMediation analysisRegression modelsOxidative stress biomarkersEstimated proportionPretermPregnancyInteraction termsBirthAssociation
2014
Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting
Ko Y, Mukherjee B, Smith J, Park S, Kardia S, Allison M, Vokonas P, Chen J, Diez‐Roux A. Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting. Statistics In Medicine 2014, 33: 5177-5191. PMID: 25112650, PMCID: PMC4227925, DOI: 10.1002/sim.6281.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAgingAtherosclerosisBone and BonesComputer SimulationEnvironmental ExposureEthnicityFemaleGene-Environment InteractionHumansIronLeadLeast-Squares AnalysisLikelihood FunctionsLongitudinal StudiesMaleMiddle AgedModels, GeneticUnited StatesUnited States Department of Veterans AffairsConceptsGene-environment interactionsMulti-Ethnic Study of AtherosclerosisModel of gene-environment interactionMulti-Ethnic StudyTukey's modelLongitudinal settingStudy of AtherosclerosisNormative Aging StudyCase-control studyIncreasing categoriesAging StudyTested interactionsLongitudinal studyCategorical variablesRobust to misspecificationInteraction termsTest departuresShrinkage estimatorsWald testInteraction estimatesIncreased powerOne-degree-of-freedom modelInteraction effectsSetsEnvironmental markers