Featured Publications
Exploiting Gene-Environment Independence for Analysis of Case–Control Studies: An Empirical Bayes-Type Shrinkage Estimator to Trade-Off Between Bias and Efficiency
Mukherjee B, Chatterjee N. Exploiting Gene-Environment Independence for Analysis of Case–Control Studies: An Empirical Bayes-Type Shrinkage Estimator to Trade-Off Between Bias and Efficiency. Biometrics 2007, 64: 685-694. PMID: 18162111, DOI: 10.1111/j.1541-0420.2007.00953.x.Peer-Reviewed Original ResearchConceptsGene-environment independenceShrinkage estimatorsLog odds ratio parametersCase-control dataGene-environment independence assumptionOdds ratio parametersCase-control estimatorsData-adaptive fashionData exampleProspective logistic regression analysisBinary exposureGene-environment associationsIndependence assumptionLogistic regression analysisCase-onlyMaximum likelihood frameworkEstimationSample sizeBinary genesRegression analysisChatterjeeExamplesWeighted averageAssumptions
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
2017
Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies
Estes J, Rice J, Li S, Stringham H, Boehnke M, Mukherjee B. Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies. Statistics In Medicine 2017, 36: 3895-3909. PMID: 28744888, PMCID: PMC5624850, DOI: 10.1002/sim.7398.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAlpha-Ketoglutarate-Dependent Dioxygenase FTOBayes TheoremBiasBiometryBody Mass IndexCase-Control StudiesComputer SimulationDiabetes Mellitus, Type 2Gene-Environment InteractionHumansLogistic ModelsMeta-Analysis as TopicModels, GeneticModels, StatisticalPolymorphism, Single NucleotideRetrospective StudiesConceptsGene-environment independenceGene-environmentEmpirical Bayes estimatorsGene-environment interactionsCase-control studyMeta-analysis settingBayes estimatorsRetrospective likelihood frameworkShrinkage estimatorsMeta-analysisTesting gene-environment interactionsCombination of estimatesFactors body mass indexSimulation studyBody mass indexUnconstrained modelLikelihood frameworkInverse varianceMeta-analysis frameworkFTO geneMass indexGenetic markersEstimationStandard alternativeChatterjee
2012
A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case–Control Studies
Bhadra D, Daniels M, Kim S, Ghosh M, Mukherjee B. A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case–Control Studies. Biometrics 2012, 68: 361-370. PMID: 22313248, PMCID: PMC3935236, DOI: 10.1111/j.1541-0420.2011.01686.x.Peer-Reviewed Original ResearchConceptsBayesian semiparametric approachSemiparametric approachCase-control studyReversible jump Markov chain Monte Carlo algorithmMarkov chain Monte Carlo algorithmMeasures of cumulative exposureLongitudinal biomarker informationMonte Carlo algorithmClinically meaningful estimatesSmooth functionsCase-control study of prostate cancerWeighted integralsCumulative exposureInfluence functionJoint likelihoodLikelihood formulationExposure historyStudy of prostate cancerDisease risk modelsHierarchical Bayesian frameworkDisease statusBayesian frameworkCase-controlRisk modelCohort study
2011
A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures
Sánchez B, Kang S, Mukherjee B. A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures. Biometrics 2011, 68: 466-476. PMID: 21955029, PMCID: PMC4405908, DOI: 10.1111/j.1541-0420.2011.01677.x.Peer-Reviewed Original ResearchMeSH KeywordsAnalysis of VarianceBiasBiometryBirth WeightCase-Control StudiesComputer SimulationEnvironmental ExposureEpidemiologic FactorsFemaleGene-Environment InteractionHumansInfant, NewbornIronLead PoisoningModels, StatisticalPregnancyPrenatal Exposure Delayed EffectsPrincipal Component AnalysisConceptsGene-environment interactionsGene-environmentEnvironmental epidemiologyCohort studyGene-environment dependenceBurden of multiple testingStudy gene-environment interactionsEnvironmental exposuresExposure dataEarly life exposuresLV frameworkG x E effectsHealth StudyCorrelated exposuresG x EDisease riskLife exposureMultiple testingFunction of environmental exposureE studyGenotype categoriesStudy of lead exposureBirth weightIron metabolism genesAdaptive trade-off
2010
Missing 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
2008
Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior
Dorazio R, Mukherjee B, Zhang L, Ghosh M, Jelks H, Jordan F. Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior. Biometrics 2008, 64: 635-644. PMID: 17680831, DOI: 10.1111/j.1541-0420.2007.00873.x.Peer-Reviewed Original ResearchConceptsSampling locationsSampling protocolNatural populations of animalsPredictions of abundanceAbundance of animalsDistribution of abundanceEndangered fish speciesInduce spatial heterogeneityAnimal abundanceOkaloosa DartersPopulations of animalsUnsampled locationsFish speciesRemoval samplingSpatial heterogeneityAnalysis of countsAbundanceDirichlet processData-adaptive wayModel specificationSources of heterogeneitySpeciesParametric alternativesDartersParametric model
2007
Semiparametric Bayesian Analysis of Case–Control Data under Conditional Gene-Environment Independence
Mukherjee B, Zhang L, Ghosh M, Sinha S. Semiparametric Bayesian Analysis of Case–Control Data under Conditional Gene-Environment Independence. Biometrics 2007, 63: 834-844. PMID: 17489972, DOI: 10.1111/j.1541-0420.2007.00750.x.Peer-Reviewed Original ResearchConceptsGene-environment independenceSemiparametric Bayesian approachTraditional logistic regression analysisParametric model assumptionsSemiparametric Bayesian modelCase-control studyPopulation-based case-control studySimulation studyBayesian approachRobust alternativeLogistic regression analysisUnderlying populationEfficient estimation techniqueBayesian modelEnvironmental exposuresModel assumptionsScientific evidenceRegression analysisAssociated with diseaseEstimation techniquesOvarian cancerControl populationPopulationIndependenceCovariates
2006
A Score Test for Determining Sample Size in Matched Case‐Control Studies with Categorical Exposure
Sinha S, Mukherjee B. A Score Test for Determining Sample Size in Matched Case‐Control Studies with Categorical Exposure. Biometrical Journal 2006, 48: 35-53. PMID: 16544811, DOI: 10.1002/bimj.200510200.Peer-Reviewed Original ResearchConceptsCase-control studyCategorical exposureMatched case-control studyScore testDichotomous exposureNull hypothesisExposure variablesOdds ratioNatural orderDisease-gene associationsMatched setsDisease riskColorectal cancerPower functionSample sizeAssociationOddsGeneralizationDiseaseSetsScoresEstimationExposureStudyRisk
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