Featured Publications
Statistical Inference for Association Studies Using Electronic Health Records: Handling Both Selection Bias and Outcome Misclassification
Beesley L, Mukherjee B. Statistical Inference for Association Studies Using Electronic Health Records: Handling Both Selection Bias and Outcome Misclassification. Biometrics 2020, 78: 214-226. PMID: 33179768, DOI: 10.1111/biom.13400.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsElectronic health record data analysisElectronic health record settingsSelection biasMichigan Genomics InitiativeAssociation studiesEHR-linkedHealth researchInverse probability weighting methodStudy sampleEffect estimatesProbability weighting methodLack of representativenessType I errorSurvey sampling literatureStandard error estimatesGold standard labelsDisease statusError estimatesStatistical inferenceMisclassificationInference strategySampling literatureStandard labels
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
Robust distributed lag models using data adaptive shrinkage
Chen Y, Mukherjee B, Adar S, Berrocal V, Coull B. Robust distributed lag models using data adaptive shrinkage. Biostatistics 2017, 19: 461-478. PMID: 29040386, PMCID: PMC6454578, DOI: 10.1093/biostatistics/kxx041.Peer-Reviewed Original ResearchConceptsDistributed lag modelsDistributed LagLag modelTime series dataEffects of air pollutionBias-variance trade-offGeneralized ridge regressionShrinkage methodAir pollution studiesHierarchical Bayes approachShrinkage approachTime seriesDl functionAir pollutionPollution studiesEffect estimatesTrade-offsExtensive simulation studyDependent variableShrinking coefficientsMean square errorLagSimulation studyBayes approachRidge regression
2013
Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons
Sun Z, Tao Y, Li S, Ferguson K, Meeker J, Park S, Batterman S, Mukherjee B. Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environmental Health 2013, 12: 85. PMID: 24093917, PMCID: PMC3857674, DOI: 10.1186/1476-069x-12-85.Peer-Reviewed Original ResearchConceptsMultipollutant modelsHealth impacts of environmental factorsEffect estimatesExposure-response associationsExposure to multiple pollutantsTime series designConsequence of environmental exposureSample sizeHealth impactsEnvironmental exposuresPresence of multicollinearityRisk predictionPotential interactive effectsInitial screeningPollutant mixturesImpact of environmental factorsSupervised principal component analysisModel dimensionsStatistical literatureData examplesTree-based methodsMultiple pollutantsVariable selectionSimulation studyReduce model dimension
2012
Where science meets policy: comparing longitudinal and cross-sectional designs to address diarrhoeal disease burden in the developing world
Markovitz A, Goldstick J, Levy K, Cevallos W, Mukherjee B, Trostle J, Eisenberg J. Where science meets policy: comparing longitudinal and cross-sectional designs to address diarrhoeal disease burden in the developing world. International Journal Of Epidemiology 2012, 41: 504-513. PMID: 22253314, PMCID: PMC3324455, DOI: 10.1093/ije/dyr194.Peer-Reviewed Original ResearchConceptsCross-sectional studyCross-sectional designEffect estimatesLongitudinal studyRisk factorsDisease risk factorsRisk factor distributionInforming public health policyPublic health policiesPublic health communityRisk factor effectsHousehold risk factorsDiarrhoeal disease burdenFactor effect estimatesHealth policyDiarrhoeal disease surveillanceEcuadorian villageNational policy decisionsHealth communityDisease burdenCross-sectionDisease surveillanceFactor distributionRiskGeographic regions