Featured Publications
Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparisons
Clark-Boucher D, Zhou X, Du J, Liu Y, Needham B, Smith J, Mukherjee B. Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparisons. PLOS Genetics 2023, 19: e1011022. PMID: 37934796, PMCID: PMC10655967, DOI: 10.1371/journal.pgen.1011022.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremDNA MethylationEnvironmental ExposureHumansLinear ModelsMediation AnalysisConceptsBayesian Sparse Linear Mixed ModelMediation analysisHigh-dimensional mediation analysisMulti-ethnic cohortEpigenetic researchHealth outcomesHigh-dimensional DNA methylation dataLinear mixed modelsDNA methylation dataContinuous outcomesEvaluate DNA methylationDNA methylationMethylation dataDNAm dataMixed modelsDiverse simulationsSeamless implementationModern statistical methodsMediation effectR packageUnited StatesOutcomes
2024
Improving prediction of linear regression models by integrating external information from heterogeneous populations: James–Stein estimators
Han P, Li H, Park S, Mukherjee B, Taylor J. Improving prediction of linear regression models by integrating external information from heterogeneous populations: James–Stein estimators. Biometrics 2024, 80: ujae072. PMID: 39101548, PMCID: PMC11299067, DOI: 10.1093/biomtc/ujae072.Peer-Reviewed Original ResearchMeSH KeywordsBiometryComputer SimulationData Interpretation, StatisticalHumansLeadLinear ModelsModels, StatisticalPatellaConceptsJames-Stein estimatorLinear regression modelsIndividual-level dataComprehensive simulation studyRegression modelsNumerical performanceSimulation studyShrinkage methodCoefficient estimatesPredictive meanReduced modelStudy population heterogeneityInternal modelEstimationStudy populationBlood lead levelsInternational studiesCovariatesPatella bonePublished literatureLead levelsExternal studiesSummary informationPopulationSubsets
2022
Predicting cumulative lead (Pb) exposure using the Super Learner algorithm
Wang X, Bakulski K, Mukherjee B, Hu H, Park S. Predicting cumulative lead (Pb) exposure using the Super Learner algorithm. Chemosphere 2022, 311: 137125. PMID: 36347347, PMCID: PMC10160242, DOI: 10.1016/j.chemosphere.2022.137125.Peer-Reviewed Original ResearchConceptsPatella leadNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyNutrition Examination SurveyLong-term health effectsPopulation-based studyK-shell X-ray fluorescenceNormative Aging StudyCumulative lead exposureEvaluate health effectsExamination SurveyLead concentrationsBone lead measurementsAging StudyTibia leadPositive associationStudy populationHealth effectsRegression-based predictive modelBone lead concentrationsBlood pressureFlexible machine learning approachCorrelation coefficientX-ray fluorescence techniqueLead measurements
2021
Maternal lipidomic signatures in relation to spontaneous preterm birth and large-for-gestational age neonates
Aung M, Ashrap P, Watkins D, Mukherjee B, Rosario Z, Vélez-Vega C, Alshawabkeh A, Cordero J, Meeker J. Maternal lipidomic signatures in relation to spontaneous preterm birth and large-for-gestational age neonates. Scientific Reports 2021, 11: 8115. PMID: 33854141, PMCID: PMC8046995, DOI: 10.1038/s41598-021-87472-9.Peer-Reviewed Original ResearchConceptsSpontaneous preterm birthBiomarkers of pregnancy outcomesGestational age neonatesPreterm birthAge neonatesPregnancy outcomesDegree of hydrocarbon chain saturationIncreased riskNeonatal anthropometric parametersAssociated with increased riskPlasmenyl phosphatidylethanolamineMaternal lipidomeWeeks gestationGestational ageLipidomic signatureAnthropometric parametersLiquid chromatography tandem mass spectrometryLipidomic profilesLipid metabolitesHydrocarbon chain saturationPlasma samplesBirthLogistic regressionHigh-performance liquid chromatography tandem mass spectrometryNeonates
2020
Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions
Zhang M, Yu Y, Wang S, Salvatore M, Fritsche L, He Z, Mukherjee B. Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions. Statistics In Medicine 2020, 39: 1675-1694. PMID: 32101638, DOI: 10.1002/sim.8505.Peer-Reviewed Original ResearchConceptsType I error rateType I error inflationIndependence assumptionWald and score testsCorrect type I error ratesSandwich variance estimatorSandwich estimatorScore testVariance estimationSimulation studyMisspecificationMichigan Genomics InitiativeStatistical practiceBinary outcomesTested interactionsEmpirical factsFlexible modelData modelTest of interactionBiobank studyInflationAssumptionsContinuous outcomesEpidemiological literatureLinear regression models
2019
Longitudinal trends in perfluoroalkyl and polyfluoroalkyl substances among multiethnic midlife women from 1999 to 2011: The Study of Women′s Health Across the Nation
Ding N, Harlow S, Batterman S, Mukherjee B, Park S. Longitudinal trends in perfluoroalkyl and polyfluoroalkyl substances among multiethnic midlife women from 1999 to 2011: The Study of Women′s Health Across the Nation. Environment International 2019, 135: 105381. PMID: 31841808, PMCID: PMC7374929, DOI: 10.1016/j.envint.2019.105381.Peer-Reviewed Original ResearchConceptsMidlife womenWomen's HealthBlack womenStudy of Women's HealthChinese womenLongitudinal trendsTemporal trendsBody mass indexSerum concentrationsLinear mixed modelsParous womenFollow-up visitPFAS homologuesPFNA concentrationsRace/ethnicityMass indexSerum PFAS concentrationsMidlifePatterns of exposurePolyfluoroalkyl substances concentrationsLongitudinal declineWomenAssociated with lower concentrationsHealthMixed models
2018
Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering
Tang M, Gao C, Goutman S, Kalinin A, Mukherjee B, Guan Y, Dinov I. Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering. Neuroinformatics 2018, 17: 407-421. PMID: 30460455, PMCID: PMC6527505, DOI: 10.1007/s12021-018-9406-9.Peer-Reviewed Original ResearchConceptsAmyotrophic Lateral Sclerosis Functional Rating ScaleClusters of participantsModel-basedAmyotrophic lateral sclerosisRating ScaleComputable phenotypeFunctional Rating ScaleSets of featuresUnsupervised clusteringUnsupervised machine learning methodsClinical decision supportMachine learning methodsFoetal 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 restrictionEvaluating the Risk of Noise-Induced Hearing Loss Using Different Noise Measurement Criteria
Roberts B, Seixas N, Mukherjee B, Neitzel R. Evaluating the Risk of Noise-Induced Hearing Loss Using Different Noise Measurement Criteria. Annals Of Work Exposures And Health 2018, 62: 295-306. PMID: 29415217, DOI: 10.1093/annweh/wxy001.Peer-Reviewed Original ResearchConceptsHearing threshold levelsHearing outcomesHearing lossOccupational Safety and Health AdministrationRisk of noise-induced hearing lossHealth AdministrationNoise exposureOccupational Safety and HealthNoise-induced hearing lossInstitute of Occupational Safety and HealthSafety and HealthNational Institute of Occupational Safety and HealthCohort of construction workersMixed modelsDuration of participationAssessment of noise exposureMeasures of exposureHearing levelAkaike’s information criterion differencesConstruction workersAverage noise levelLinear mixed modelsLAVGContinuous averagingOSHAAssociations between maternal phenol and paraben urinary biomarkers and maternal hormones during pregnancy: A repeated measures study
Aker A, Johns L, McElrath T, Cantonwine D, Mukherjee B, Meeker J. Associations between maternal phenol and paraben urinary biomarkers and maternal hormones during pregnancy: A repeated measures study. Environment International 2018, 113: 341-349. PMID: 29366524, PMCID: PMC5866216, DOI: 10.1016/j.envint.2018.01.006.Peer-Reviewed Original ResearchConceptsThyroid hormonesAssociated with altered thyroid hormone levelsFetal health outcomesThyroid hormone levelsIQR increaseMultivariate regression analysisGestational ageMultivariate linear regression modelFetal neurodevelopmentPregnant womenFree thyroxinePotential biological mechanismsTime of exposureUrinary biomarkersCohort studyHormone levelsParaben biomarkersTotal triiodothyronineCase-control samplePregnancyBlood samplesTotal thyroxineHormone concentrationsHealth outcomesHormoneImproving estimation and prediction in linear regression incorporating external information from an established reduced model
Cheng W, Taylor J, Vokonas P, Park S, Mukherjee B. Improving estimation and prediction in linear regression incorporating external information from an established reduced model. Statistics In Medicine 2018, 37: 1515-1530. PMID: 29365342, PMCID: PMC5889759, DOI: 10.1002/sim.7600.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremData Interpretation, StatisticalHumansLinear ModelsModels, StatisticalRegression AnalysisConceptsOutcome variable YEfficiency of estimationApproximate Bayesian inferenceBayes solutionVariable YNonlinear constraintsInferential frameworkVariable BE(Y|XImprove inferenceBayesian inferenceEffective computational methodParameter spaceReduced modelImproved estimatesLinear regression modelsTransformation approachStandard errorDunsonInferenceEstimationRegression modelsProblemCovariatesSpace
2017
Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES
Park S, Zhao Z, Mukherjee B. Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environmental Health 2017, 16: 102. PMID: 28950902, PMCID: PMC5615812, DOI: 10.1186/s12940-017-0310-9.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreBayesian kernel machine regressionNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk scoreAssociated with odds ratiosNutrition Examination SurveyAssociated with systolicExamination SurveyMulti-pollutant approachKernel machine regressionPollutant mixturesSD increaseEpidemiological researchDiastolic blood pressureMortality outcomesOdds ratioBayesian additive regression treesDisease endpointsHealth endpointsCumulative riskPositive associationEnvironmental exposuresIntermediate markersCardiovascular diseaseExposure enriched outcome dependent designs for longitudinal studies of gene–environment interaction
Sun Z, Mukherjee B, Estes J, Vokonas P, Park S. Exposure enriched outcome dependent designs for longitudinal studies of gene–environment interaction. Statistics In Medicine 2017, 36: 2947-2960. PMID: 28497531, PMCID: PMC5523112, DOI: 10.1002/sim.7332.Peer-Reviewed Original ResearchConceptsLongitudinal cohort studyCohort studyCase-only designLongitudinal studyG x E interactionNormative Aging StudyComplete-case analysisGene-environmentSampling designCase-controlVeterans AdministrationComplex human diseasesE interactionExposure informationAging StudyOutcome trajectoriesStratified samplingRetrospective genotypingIndividual exposureCovariate dataExposure effectsJoint effectsOutcomesTime-varying outcomeEnvironmental factorsThyroid hormone parameters during pregnancy in relation to urinary bisphenol A concentrations: A repeated measures study
Aung M, Johns L, Ferguson K, Mukherjee B, McElrath T, Meeker J. Thyroid hormone parameters during pregnancy in relation to urinary bisphenol A concentrations: A repeated measures study. Environment International 2017, 104: 33-40. PMID: 28410473, PMCID: PMC5497503, DOI: 10.1016/j.envint.2017.04.001.Peer-Reviewed Original ResearchConceptsThyroid hormone parametersHormonal parametersStudy visitsCases of preterm birthInterquartile rangeSupply of thyroid hormonesNormal birth outcomesUrinary bisphenol A concentrationsNested case-control studyThyrotropin (TSHContext of pregnancyUrinary BPA concentrationsUrinary bisphenol ACase-control studyIn vitro studiesPreterm birthTSH levelsMultivariate linear regression modelPregnant womenFetal developmentFree T4Visit 3Measures analysisBirth outcomesPregnancy
2016
Associations between Repeated Measures of Maternal Urinary Phthalate Metabolites and Thyroid Hormone Parameters during Pregnancy
Johns L, Ferguson K, McElrath T, Mukherjee B, Meeker J. Associations between Repeated Measures of Maternal Urinary Phthalate Metabolites and Thyroid Hormone Parameters during Pregnancy. Environmental Health Perspectives 2016, 124: 1808-1815. PMID: 27152641, PMCID: PMC5089879, DOI: 10.1289/ehp170.Peer-Reviewed Original ResearchMeSH KeywordsAdultCase-Control StudiesFemaleFetal DevelopmentHomeostasisHumansLinear ModelsPhthalic AcidsPregnancyThyroid HormonesConceptsUrinary phthalate metabolitesMaternal urinary phthalate metabolitesThyroid hormone parametersThyroid hormone levelsPregnant womenPhthalate metabolitesStudy visitsHormonal parametersNested case-control study of preterm birthMeasures of urinary phthalate metabolitesHormone levelsCase-control study of preterm birthStudy of preterm birthThyroid hormonesEnvironmental phthalate exposureNested case-control studyNormal thyroid functionLevels of thyroid hormonesThyroid function markersTime pointsCirculating levels of thyroid hormonesTotal thyroid hormonesPreterm birthFetal growthPlasma thyroid hormone levels
2014
Urinary Phthalate Metabolites and Biomarkers of Oxidative Stress in Pregnant Women: A Repeated Measures Analysis
Ferguson K, McElrath T, Chen Y, Mukherjee B, Meeker J. Urinary Phthalate Metabolites and Biomarkers of Oxidative Stress in Pregnant Women: A Repeated Measures Analysis. Environmental Health Perspectives 2014, 123: 210-216. PMID: 25402001, PMCID: PMC4348741, DOI: 10.1289/ehp.1307996.Peer-Reviewed Original ResearchConceptsUrinary phthalate metabolitesPhthalate metabolitesMono-n-butyl phthalateBiomarkers of oxidative stressBirth outcomesNested case-control study of preterm birthCase-control study of preterm birthPhthalate exposureStudy of preterm birthStudy of birth outcomesOxidative stressPopulation of pregnant womenNested case-control studyStudy population of pregnant womenAdverse birth outcomesUrine samplesMarkers of oxidative stressRepeated measures analysisHealth end pointsAssociated with significantly higher concentrationsDi(2-ethylhexyl) phthalatePreterm birthPregnant womenMono-n-butylLinear mixed modelsUrinary Phthalate Metabolite Associations with Biomarkers of Inflammation and Oxidative Stress Across Pregnancy in Puerto Rico
Ferguson K, Cantonwine D, Rivera-González L, Loch-Caruso R, Mukherjee B, Del Toro L, Jiménez-Vélez B, Calafat A, Ye X, Alshawabkeh A, Cordero J, Meeker J. Urinary Phthalate Metabolite Associations with Biomarkers of Inflammation and Oxidative Stress Across Pregnancy in Puerto Rico. Environmental Science And Technology 2014, 48: 7018-7025. PMID: 24845688, PMCID: PMC4066910, DOI: 10.1021/es502076j.Peer-Reviewed Original ResearchConceptsMono-n-butyl phthalateBiomarkers of inflammationIL-10Prospective cohort study of pregnant womenDi-2-ethylhexyl phthalate metabolitesCohort study of pregnant womenIL-6Study of pregnant womenAssociated with increased IL-6Oxidative stressUrinary phthalate metabolitesC-reactive proteinProspective cohort studyAdverse birth outcomesMetabolites of di-2-ethylhexyl phthalateOxidative stress markersPreterm birthMetabolite associationsPregnant womenMono-n-butylDi-2-ethylhexyl phthalateOxidative stress biomarkersBirth outcomesTNF-aInflammation biomarkersPersonal Black Carbon Exposure Influences Ambulatory Blood Pressure
Zhao X, Sun Z, Ruan Y, Yan J, Mukherjee B, Yang F, Duan F, Sun L, Liang R, Lian H, Zhang S, Fang Q, Gu D, Brook J, Sun Q, Brook R, Rajagopalan S, Fan Z. Personal Black Carbon Exposure Influences Ambulatory Blood Pressure. Hypertension 2014, 63: 871-877. PMID: 24420543, PMCID: PMC4445364, DOI: 10.1161/hypertensionaha.113.02588.Peer-Reviewed Original ResearchConceptsPersonal black carbonAir pollutionBlack carbonBeijing Municipal Environmental Monitoring CenterFine particulate matter concentrationsCombustion-related air pollutionFine particulate matterAmbulatory blood pressureEnvironmental Monitoring CenterParticulate matter concentrationsBlood pressureHigh air pollutionReduce air pollutionMm HgLow frequency to high frequency ratioPublic health effectsParticulate matterTwenty-four-hour ambulatory blood pressureExposure to high levelsMatter concentrationSystolic blood pressureDiastolic blood pressurePollutionBlood pressure effectsGeneralized linear model
2013
Determinants of personal, indoor and outdoor VOC concentrations: An analysis of the RIOPA data
Su F, Mukherjee B, Batterman S. Determinants of personal, indoor and outdoor VOC concentrations: An analysis of the RIOPA data. Environmental Research 2013, 126: 192-203. PMID: 24034784, PMCID: PMC4243524, DOI: 10.1016/j.envres.2013.08.005.Peer-Reviewed Original ResearchConceptsVolatile organic compoundsAir exchange rateVOC concentrationsAssociated with citiesEmission sourcesPersonal exposureEnvironmental exposure to volatile organic compoundsOutdoor VOC concentrationsVolatile organic compound levelsExposure to volatile organic compoundsVolatile organic compound exposureWater supply typesNon-smoking householdsVOC sourcesOutdoor sourcesOutdoor concentrationsPersonal airLinear mixed-effects modelsRIOPAIndoor-outdoorOrganic compoundsWind speedIndoor measurementsMixed-effects modelsExposure determinantsAssociations between brominated flame retardants in house dust and hormone levels in men
Johnson P, Stapleton H, Mukherjee B, Hauser R, Meeker J. Associations between brominated flame retardants in house dust and hormone levels in men. The Science Of The Total Environment 2013, 445: 177-184. PMID: 23333513, PMCID: PMC3572297, DOI: 10.1016/j.scitotenv.2012.12.017.Peer-Reviewed Original ResearchConceptsBrominated flame retardantsSex hormone binding globulinIndoor dustHouse dustThyroid-stimulating hormoneExposure to contaminantsFollicle stimulating hormoneFlame retardantsBody mass indexHouse dust concentrationsFlame retardant concentrationUS infertility clinicHormone levelsPBDE congenersLuteinizing hormoneStimulating hormoneEndocrine disruptionAssociated with total T3Inverse associationCommercial mixtureDecaBDE concentrationsReproductive effectsSerum free T4Increased free androgen indexFree androgen index