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 informationPopulationSubsetsResidential exposure associations with ALS risk, survival, and phenotype: a Michigan-based case-control study
Goutman S, Boss J, Jang D, Piecuch C, Farid H, Batra M, Mukherjee B, Feldman E, Batterman S. Residential exposure associations with ALS risk, survival, and phenotype: a Michigan-based case-control study. Amyotrophic Lateral Sclerosis And Frontotemporal Degeneration 2024, 25: 543-553. PMID: 38557405, PMCID: PMC11269018, DOI: 10.1080/21678421.2024.2336110.Peer-Reviewed Original ResearchAmyotrophic lateral sclerosis riskAssociated with ALS riskALS riskCase-only analysisResidential settingsControl participantsCox proportional hazards modelsLogistic regression modelsCase-control studyMultinomial logistic regressionMultiple testing correctionProportional hazards modelLatent profile analysisResidential exposureExposure variablesPolytomous outcomesExposure associationsDecrease disease burdenALS susceptibilityLogistic regressionDisease burdenTesting correctionHazards modelRisk factorsRegression models
2022
Integrating information from existing risk prediction models with no model details
Han P, Taylor J, Mukherjee B. Integrating information from existing risk prediction models with no model details. Canadian Journal Of Statistics 2022, 51: 355-374. PMID: 37346757, PMCID: PMC10281716, DOI: 10.1002/cjs.11701.Peer-Reviewed Original Research
2020
A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank
Bi W, Fritsche L, Mukherjee B, Kim S, Lee S. A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank. American Journal Of Human Genetics 2020, 107: 222-233. PMID: 32589924, PMCID: PMC7413891, DOI: 10.1016/j.ajhg.2020.06.003.Peer-Reviewed Original ResearchConceptsControlled type I error ratesTime-to-event data analysisType I error rateGenetic studies of human diseasesGenome-wide significance levelTime-to-event phenotypesSaddlepoint approximationGenome-wide analysisEuropean ancestry samplesMinor allele frequencyStudy of human diseaseElectronic health recordsCox PH regression modelRegression modelsStandard Wald testProportional hazardsBinary phenotypesData analysisAncestry samplesGenetic studiesHealth recordsUK BiobankAllele frequenciesInpatient dataCox proportional hazardsAn efficient and computationally robust statistical method for analyzing case-control mother–offspring pair genetic association studies
Zhang H, Mukherjee B, Arthur V, Hu G, Hochner H, Chen J. An efficient and computationally robust statistical method for analyzing case-control mother–offspring pair genetic association studies. The Annals Of Applied Statistics 2020, 14: 560-584. DOI: 10.1214/19-aoas1298.Peer-Reviewed Original ResearchEnvironmental risk factorsRisk factorsMaternal environmental risk factorsOffspring genetic effectsPerinatal environmental risk factorsGenetic association studiesFinite sample performancePregnancy healthGenetic risk factorsAssessment of pre-Extensive simulation studyGestational diabetes mellitusIncreased statistical efficiencyLogistic regressionAssociation studiesMaternal genotypeSample performanceMendelian transmissionProfile likelihoodRegression modelsOffspring genotypesEarly-lifeInference proceduresLagrange multiplier methodLikelihood method
2019
Interactions between chemicals and non-chemical stressors: The modifying effect of life events on the association between triclocarban, phenols and parabens with gestational length in a Puerto Rican cohort
Aker A, McConnell R, Loch-Caruso R, Park S, Mukherjee B, Rosario Z, Vélez-Vega C, Huerta-Montanez G, Alshawabkeh A, Cordero J, Meeker J. Interactions between chemicals and non-chemical stressors: The modifying effect of life events on the association between triclocarban, phenols and parabens with gestational length in a Puerto Rican cohort. The Science Of The Total Environment 2019, 708: 134719. PMID: 31785910, PMCID: PMC6957748, DOI: 10.1016/j.scitotenv.2019.134719.Peer-Reviewed Original ResearchConceptsLife eventsLinear regression modelsMultiple linear regression modelNegative life eventsPresence of negative life eventsPsychosocial stress measuresAdverse birth outcomesPregnancy cohort studyRegression modelsEffects of psychosocial stressPsychosocial factorsNon-chemical stressorsBirth outcomesEffects of life eventsCohort studyPsychosocial stressPositive LEsPositive life eventsExposure biomarkersSignificant modifiersPrenatal exposureSignificant interactionWomenAssociationRate of events
2018
Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information
Cheng W, Taylor J, Gu T, Tomlins S, Mukherjee B. Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2018, 68: 121-139. PMID: 31105344, PMCID: PMC6519970, DOI: 10.1111/rssc.12306.Peer-Reviewed Original ResearchOutcome variable YEfficiency of estimationMeasurement error literatureDistribution of B.Regression coefficientsVariable YRegression modelsBinary outcomesVariable BLogistic regression modelsRisk prediction modelAlternative expressionBinary BImproved estimatesGaussian distributionProstate Cancer Prevention Trial Risk CalculatorProstate cancer antigen 3Risk calculatorStandard errorEstimationPredictive powerAntigen 3RegressionHistorical dataImproving 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 ResearchConceptsOutcome 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
Perceptions of measles, pneumonia, and meningitis vaccines among caregivers in Shanghai, China, and the health belief model: a cross-sectional study
Wagner A, Boulton M, Sun X, Mukherjee B, Huang Z, Harmsen I, Ren J, Zikmund-Fisher B. Perceptions of measles, pneumonia, and meningitis vaccines among caregivers in Shanghai, China, and the health belief model: a cross-sectional study. BMC Pediatrics 2017, 17: 143. PMID: 28606106, PMCID: PMC5468991, DOI: 10.1186/s12887-017-0900-2.Peer-Reviewed Original ResearchConceptsPneumococcal vaccine uptakeHealth Belief ModelBelief ModelHealth Belief Model constructsVaccine uptakeModels of health behaviorVaccine necessityHealth care workersCross-sectional studyLogistic regression modelsChinese caregiversCaregiver perceptionsHealth behaviorsCaregiversCare workersYears of agePneumococcal vaccineWritten surveyBackgroundIn ChinaHealthPerceived safetyRegression modelsYoung childrenChildrenMeasles vaccine
2016
Association of Environmental Toxins With Amyotrophic Lateral Sclerosis
Su F, Goutman S, Chernyak S, Mukherjee B, Callaghan B, Batterman S, Feldman E. Association of Environmental Toxins With Amyotrophic Lateral Sclerosis. JAMA Neurology 2016, 73: 803-11. PMID: 27159543, PMCID: PMC5032145, DOI: 10.1001/jamaneurol.2016.0594.Peer-Reviewed Original ResearchMeSH KeywordsAgedAmyotrophic Lateral SclerosisCase-Control StudiesEnvironmental ExposureEnvironmental PollutantsFemaleGas Chromatography-Mass SpectrometryHumansMaleMichiganMiddle AgedMultivariate AnalysisOccupational ExposureOdds RatioOutcome Assessment, Health CareRetrospective StudiesRisk FactorsSurveys and QuestionnairesConceptsBrominated flame retardantsPersistent environmental pollutantsResidential exposureOrganochlorine pesticidesPolychlorinated biphenylsOdds of ALSAssociation of occupational exposureDisease risk factorsExposure time windowsEnvironmental pollutionRisk factorsModifiable risk factorsMultivariate modelOccupational exposureLogistic regression modelsCase-control studySurvey dataFamily history of amyotrophic lateral sclerosisExposure windowsIncreased oddsFamily historyAssociated with amyotrophic lateral sclerosisHistory of amyotrophic lateral sclerosisRegression modelsMilitary serviceMediation 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
Extreme Precipitation and Beach Closures in the Great Lakes Region: Evaluating Risk among the Elderly
Bush K, Fossani C, Li S, Mukherjee B, Gronlund C, O'Neill M. Extreme Precipitation and Beach Closures in the Great Lakes Region: Evaluating Risk among the Elderly. International Journal Of Environmental Research And Public Health 2014, 11: 2014-2032. PMID: 24534768, PMCID: PMC3945582, DOI: 10.3390/ijerph110202014.Peer-Reviewed Original ResearchConceptsExtreme precipitationBeach closuresWater qualityHospital admissionGreat Lakes regionPrecipitation eventsLong-term time trendsCity-specific estimatesLakes regionPoisson regression modelsBeach closingsPrecipitationClimate changeBeachRegional risk estimationRecreational water qualityLake CityTime trendsRisk estimatesRegression modelsGreatStudy period
2012
Likelihood‐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
2010
A new comorbidity index: the health-related quality of life comorbidity index
Mukherjee B, Ou H, Wang F, Erickson S. A new comorbidity index: the health-related quality of life comorbidity index. Journal Of Clinical Epidemiology 2010, 64: 309-319. PMID: 21147517, DOI: 10.1016/j.jclinepi.2010.01.025.Peer-Reviewed Original ResearchConceptsClinical classification codesPhysical component summaryMedical Expenditure Panel SurveyMental component summaryHealth-related qualityComponent summaryAssociated with physical component summaryHealth-related quality of life comorbidity indexShort Form-12 Physical Component SummaryMEPS databaseMeasures of HRQLComorbidity indexRisk adjustment indexChronic conditionsPanel SurveyRegression modelsInternal validityPatient populationAdjustment indicesClassification codesHRQLSummaryValidation testsIndexModel R2A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome
Boonstra P, Gruber S, Raymond V, Huang S, Timshel S, Nilbert M, Mukherjee B. A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome. Genetic Epidemiology 2010, 34: 756-768. PMID: 20878717, PMCID: PMC3894615, DOI: 10.1002/gepi.20534.Peer-Reviewed Original ResearchConceptsAffected parent-child pairsDanish HNPCC registerParent-child pairsLynch syndromePaired t-testGenetic anticipationLynch syndrome cohortCancer genetics clinicsT-testEvidence of genetic anticipationFamily membersClinic-based populationRandom-effects modelGenetics clinicAffected pairsMismatch repairUnaffected family membersFamilial correlationsAffected parentType I errorSyndrome cohortRegression modelsPedigree dataDecreasing ageAscertainmentMissing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification
Ahn J, Mukherjee B, Gruber S, Sinha S. Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification. Biometrics 2010, 67: 546-558. PMID: 20560931, PMCID: PMC3119773, DOI: 10.1111/j.1541-0420.2010.01453.x.Peer-Reviewed Original ResearchConceptsStereotype regression modelSubtypes of casesDeletion of observationsExpectation/conditional maximization algorithmBaseline category logit modelEstimation of model parametersMissingness mechanismData mechanismCase-control dataProportional oddsBayesian approachCategorical responsesCase-control studyCase-control study of colorectal cancerMissingnessMaximization algorithmCategorical outcomesMonte CarloModel assumptionsRegression modelsStudy of colorectal cancerModel parametersNonidentifiabilityDisease subclassificationMultinomial logit model
2009
Graphical diagnostics to check model misspecification for the proportional odds regression model
Liu I, Mukherjee B, Suesse T, Sparrow D, Park S. Graphical diagnostics to check model misspecification for the proportional odds regression model. Statistics In Medicine 2009, 28: 412-429. PMID: 18693299, DOI: 10.1002/sim.3386.Peer-Reviewed Original ResearchConceptsCovariate effectsOrdinal responsesModel misspecificationProportional odds regression modelStudy covariate effectsGoodness-of-fit statisticsClass of modelsNumerical methodFunctional misspecificationBinary responsesGraphical diagnosticsSimulation studyCumulative logitsMisspecificationCumulative sumRegression modelsGraphical methodSumArbogastVA Normative Aging StudyCovariatesProportional odds regressionClass
2004
Bayesian Semiparametric Modeling for Matched Case–Control Studies with Multiple Disease States
Sinha S, Mukherjee B, Ghosh M. Bayesian Semiparametric Modeling for Matched Case–Control Studies with Multiple Disease States. Biometrics 2004, 60: 41-49. PMID: 15032772, DOI: 10.1111/j.0006-341x.2004.00169.x.Peer-Reviewed Original ResearchConceptsSemiparametric Bayesian frameworkBayesian semiparametric modelSemiparametric modelDirichlet processStratum effectsConditional likelihoodProbability of disease developmentBayesian approachNumerical integration schemeBayesian frameworkSample sizeDirichletActual estimationMLEMissingnessMarkovIntegration schemeExposure distributionBayesianEstimationRegression modelsMultiple disease statesDistributionProbabilityDisease states