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 ResearchConceptsJames-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
2019
Urinary concentrations of phenols in association with biomarkers of oxidative stress in pregnancy: Assessment of effects independent of phthalates
Ferguson K, Lan Z, Yu Y, Mukherjee B, McElrath T, Meeker J. Urinary concentrations of phenols in association with biomarkers of oxidative stress in pregnancy: Assessment of effects independent of phthalates. Environment International 2019, 131: 104903. PMID: 31288179, PMCID: PMC6728185, DOI: 10.1016/j.envint.2019.104903.Peer-Reviewed Original ResearchConceptsUrinary phthalate metabolitesOxidative stress biomarkersNon-null associationsPhthalate metabolitesBiomarkers of oxidative stressInterquartile rangeBenzophenone-3Associated with increasesOutcome biomarkersIncreased maternal oxidative stressStress biomarkersExposure to environmental phenolsOxidative stressReduced fetal growthUrinary oxidative stress biomarkersMaternal oxidative stressEffect estimatesAdaptive elastic net modelStudy populationPreterm birthFetal growthConcentration of phenolUrinary phenolPregnancyUrinary concentrations
2017
Environmental phenol associations with ultrasound and delivery measures of fetal growth
Ferguson K, Meeker J, Cantonwine D, Mukherjee B, Pace G, Weller D, McElrath T. Environmental phenol associations with ultrasound and delivery measures of fetal growth. Environment International 2017, 112: 243-250. PMID: 29294443, PMCID: PMC5899051, DOI: 10.1016/j.envint.2017.12.011.Peer-Reviewed Original ResearchConceptsInverse associationBirth weight z-scoreWeight z-scoreZ-scoreFetal weightInterquartile range differenceFetal growthBirth weightUltrasound estimationUltrasound estimation of fetal weightEstimation of fetal weightLIFECODES birth cohortStandard deviation decreaseEstimated fetal weightBirth cohortOutcome measuresInvestigate sex differencesWomen's HospitalPregnant womenStudy populationStudy visitsDeviation decreaseDelivery measuresPregnancyAssociation
2012
Particulate matter concentrations in residences: an intervention study evaluating stand‐alone filters and air conditioners
Batterman S, Du L, Mentz G, Mukherjee B, Parker E, Godwin C, Chin J, O’Toole A, Robins T, Rowe Z, Lewis T. Particulate matter concentrations in residences: an intervention study evaluating stand‐alone filters and air conditioners. Indoor Air 2012, 22: 235-252. PMID: 22145709, PMCID: PMC4233141, DOI: 10.1111/j.1600-0668.2011.00761.x.Peer-Reviewed Original ResearchConceptsEnvironmental tobacco smokeIndoor air qualityParticulate matterPM levelsPM concentrationsExchange ratioIntervention studiesAir qualityHigh concentrations of particulate matterConcentrations of particulate matterEffectiveness of interventionsOutdoor PM levelsParticulate matter concentrationsAir filtersRandomized controlled trialsChild's sleeping areaCharacterize air qualityExposure misclassificationEffective interventionsTobacco smokeChild's bedroomIncreased particulate matterMatter concentrationHouseholds of childrenStudy population