2023
Per- and Polyfluoroalkyl Substances (PFAS) and Lipid Trajectories in Women 45–56 Years of Age: The Study of Women’s Health Across the Nation
Kang H, Ding N, Karvonen-Gutierrez C, Mukherjee B, Calafat A, Park S. Per- and Polyfluoroalkyl Substances (PFAS) and Lipid Trajectories in Women 45–56 Years of Age: The Study of Women’s Health Across the Nation. Environmental Health Perspectives 2023, 131: 087004. PMID: 37552133, PMCID: PMC10408595, DOI: 10.1289/ehp12351.Peer-Reviewed Original ResearchConceptsStudy of Women's HealthOdds ratioWomen's HealthMidlife womenConfidence intervalsFavorable blood lipid profileEstimate odds ratiosAdverse effects of PFASSerum PFAS concentrationsLatent class growth modelingLongitudinal trajectoriesAssociated with totalMenopausal transitionAssociated with trajectoriesLow-density lipoprotein (LDL) cholesterolHigh-density lipoprotein (HDL) cholesterolLipid trajectoriesAverage follow-upBlood total cholesterolBlood lipid profileTriglycerides trajectoriesEpidemiological studiesPositive associationBlood lipidsLDL cholesterol
2021
Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects
Song Y, Zhou X, Kang J, Aung M, Zhang M, Zhao W, Needham B, Kardia S, Liu Y, Meeker J, Smith J, Mukherjee B. Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2021, 70: 1391-1412. PMID: 34887595, PMCID: PMC8653861, DOI: 10.1111/rssc.12518.Peer-Reviewed Original ResearchNatural indirect effectMulti-Ethnic Study of AtherosclerosisMediation analysisMediator-outcome effectsMulti-Ethnic StudyStudy of AtherosclerosisCausal mediation analysisLIFECODES birth cohortBirth cohortEpidemiological studiesJoint prior distributionPotential mediatorsHigh-dimensional mediation analysisGaussian mixtureHigh-dimensional mediatorsIndirect effectsPrior distributionEstimation accuracyExposure-mediated effectsBayesian paradigmExposure effectsAvailability of measurementsComposite structuresIncreasing availability of measurements
2020
Urinary metal mixtures and longitudinal changes in glucose homeostasis: The Study of Women’s Health Across the Nation (SWAN)
Wang X, Mukherjee B, Karvonen-Gutierrez C, Herman W, Batterman S, Harlow S, Park S. Urinary metal mixtures and longitudinal changes in glucose homeostasis: The Study of Women’s Health Across the Nation (SWAN). Environment International 2020, 145: 106109. PMID: 32927284, PMCID: PMC7577932, DOI: 10.1016/j.envint.2020.106109.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreStudy of Women's HealthWomen's HealthExposure to metal mixturesElevated diabetes riskAssociated with lower HOMA-IRUrinary metal mixtureLongitudinal changesMetal mixturesHOMA-BYears of follow-upRate of changeHOMA-IRAdaptive elastic-netStandard deviation increaseDiabetes riskLinear mixed effects modelsLower HOMA-IRRandom interceptMixed effects modelsRisk scoreAssociated with higher HOMA-IRMetal mixture effectsEpidemiological studiesDeviation increase
2018
Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE)
Ferguson K, Yu Y, Cantonwine D, McElrath T, Meeker J, Mukherjee B. Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE). Paediatric And Perinatal Epidemiology 2018, 32: 469-473. PMID: 30016545, PMCID: PMC6939297, DOI: 10.1111/ppe.12486.Peer-Reviewed Original ResearchConceptsLinear mixed modelsComplete-case analysisMultiple imputationEpidemiological studies of risk factorsImputed datasetsComplete-caseDemographic factorsStudy of risk factorsLIFECODES birth cohortUltrasound measurementsCalculate associationsBirth cohortCross-sectionEpidemiological studiesRisk factorsStudy visitsLongitudinal analysisParametric linear mixed modelImputationMissing dataMixed modelsLongitudinal measurementsSample sizeCovariate dataGrowth restriction
2017
Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies
Patel C, Kerr J, Thomas D, Mukherjee B, Ritz B, Chatterjee N, Jankowska M, Madan J, Karagas M, McAllister K, Mechanic L, Fallin M, Ladd-Acosta C, Blair I, Teitelbaum S, Amos C. Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiology Biomarkers & Prevention 2017, 26: 1370-1380. PMID: 28710076, PMCID: PMC5581729, DOI: 10.1158/1055-9965.epi-17-0459.Peer-Reviewed Original ResearchConceptsFollow-up of study participantsInfluence cancer riskExposure and behaviourPopulation-based studyExposure assessmentCase-control studyPhysical activityCancer riskCorrelated exposuresStudy participantsEpidemiological studiesGenetic susceptibilityEnvironmental exposure assessmentFollow-upData collectionMultidimensional indicatorsCancer developmentEvaluated 1Indicators of exposureComplex effects of environmental factorsEpidemiological investigationsOccupational exposure assessmentAssessmentEnvironmental factorsDisease development
2014
A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan
Batterman S, Burke J, Isakov V, Lewis T, Mukherjee B, Robins T. A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan. International Journal Of Environmental Research And Public Health 2014, 11: 9553-9577. PMID: 25226412, PMCID: PMC4199035, DOI: 10.3390/ijerph110909553.Peer-Reviewed Original ResearchConceptsTraffic-related air pollutionAir pollutionTraffic densityTraffic emissionsExposure metricsDispersion modelTransportation planning studiesSource of air pollutant emissionsComparison of exposure distributionNear-road environmentExposure to traffic-related air pollutionEmission densityAir pollutant emissionsAir pollution epidemiologyDispersion modeling systemVehicle mixEpidemiological studiesTraffic volumePollutant concentrationsSpatial variabilityResidential locationHealth risksTrafficPollutionProximity classificationEnvironmental Risk Score as a New Tool to Examine Multi-Pollutants in Epidemiologic Research: An Example from the NHANES Study Using Serum Lipid Levels
Park S, Tao Y, Meeker J, Harlow S, Mukherjee B. Environmental Risk Score as a New Tool to Examine Multi-Pollutants in Epidemiologic Research: An Example from the NHANES Study Using Serum Lipid Levels. PLOS ONE 2014, 9: e98632. PMID: 24901996, PMCID: PMC4047033, DOI: 10.1371/journal.pone.0098632.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreLipid outcomesEpidemiological researchNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk scoreNutrition Examination SurveyAdverse health responsesSocio-demographic factorsMulti-pollutant exposuresDevelopment of chronic diseasesBody mass indexExamination SurveySerum nutrient levelsMulti-pollutant approachSociodemographic factorsHealth responseChronic diseasesSingle-pollutantDisease riskMass indexEpidemiological studiesNHANES studyRisk predictionMulti-pollutants
2012
Environmental Cadmium and Lead Exposures and Hearing Loss in U.S. Adults: The National Health and Nutrition Examination Survey, 1999 to 2004
Choi Y, Hu H, Mukherjee B, Miller J, Park S. Environmental Cadmium and Lead Exposures and Hearing Loss in U.S. Adults: The National Health and Nutrition Examination Survey, 1999 to 2004. Environmental Health Perspectives 2012, 120: 1544-1550. PMID: 22851306, PMCID: PMC3556613, DOI: 10.1289/ehp.1104863.Peer-Reviewed Original ResearchConceptsNational Health and Nutrition Examination SurveyPure-tone averageHealth and Nutrition Examination SurveyHearing lossNutrition Examination SurveyExamination SurveyU.S. adultsRisk factorsGeneral populationU.S. general populationBlood cadmiumHearing thresholdsNonoccupational noiseHearing abilityLow-level exposure to cadmiumLead exposureYears of ageClinical risk factorsU.S. populationExamination componentsEnvironmental cadmiumEpidemiological studiesHearingAnalyzed dataAdultsLikelihood‐based methods for regression analysis with binary exposure status assessed by pooling
Lyles R, Tang L, Lin J, Zhang Z, Mukherjee B. Likelihood‐based methods for regression analysis with binary exposure status assessed by pooling. Statistics In Medicine 2012, 31: 2485-2497. PMID: 22415630, PMCID: PMC3528351, DOI: 10.1002/sim.4426.Peer-Reviewed Original ResearchConceptsPopulation-based case-control study of colorectal cancerCase-control study of colorectal cancerPopulation-based case-control studyStudy of colorectal cancerExposure statusBinary outcomesRegression modelsCase-control sampleLogistic regression modelsGene-disease associationsObserved binary outcomeStudy designEpidemiological studiesColorectal cancerAssess exposureMaximum likelihood analysisRegression analysisLikelihood-based methodsExposure assessmentMaximum likelihood approachLikelihood approachCross-sectionSimulation studyOutcomesLikelihood analysis
2011
Logistic regression analysis of biomarker data subject to pooling and dichotomization
Zhang Z, Liu A, Lyles R, Mukherjee B. Logistic regression analysis of biomarker data subject to pooling and dichotomization. Statistics In Medicine 2011, 31: 2473-2484. PMID: 21953741, DOI: 10.1002/sim.4367.Peer-Reviewed Original ResearchConceptsPopulation-based case-control study of colorectal cancerCase-control study of colorectal cancerProspective logistic regression modelPopulation-based case-control studyStudy of colorectal cancerEpidemiological studiesLogistic regression modelsAnalysis of epidemiological dataLogistic regression analysisBinary exposurePooled measureColorectal cancerRegression modelsEpidemiological dataRegression analysisAnalysis of biomarker dataDisease statusExposed subjectsBiomarker dataChoice of designSubjectsEstimated parametersStatusRecommendations
2009
Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis
Mukherjee B, Ahn J, Gruber S, Ghosh M, Chatterjee N. Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis. Biometrics 2009, 66: 934-948. PMID: 19930190, PMCID: PMC3103064, DOI: 10.1111/j.1541-0420.2009.01357.x.Peer-Reviewed Original ResearchConceptsGene-environment interactionsCase-control study of colorectal cancerStudy of gene-environment interactionsStudy of colorectal cancerGene-environment independenceRed meat consumptionBayesian designCase-control studyBayesian approachSample size determination criteriaCase-controlEpidemiological studiesColorectal cancerFrequentist counterpartsNatural wayMeat consumptionAnalyze current dataHypothesis testingDetermination criteriaSmokingEpidemiological exposureAnalysis strategyStudy