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
2011
Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons
Mukherjee B, Ahn J, Gruber S, Chatterjee N. Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons. American Journal Of Epidemiology 2011, 175: 177-190. PMID: 22199027, PMCID: PMC3286201, DOI: 10.1093/aje/kwr367.Peer-Reviewed Original ResearchConceptsGene-environment independenceGene-environment interactionsCase-only methodTesting gene-environment interactionsCase-control testsExposure under studyCase-control association studyUnderlying populationCase-control methodCase-control analysisFraction of markersType I error propertiesGenome-wide scanClass of proceduresAssociation studiesData-adaptive wayComparative simulation studyLarge-scale studiesEmpirical-BayesIndependence assumptionFalse positivesPopulationReplication strategyHybrid methodIndependence
2009
Shrinkage estimation for robust and efficient screening of single‐SNP association from case‐control genome‐wide association studies
Luo S, Mukherjee B, Chen J, Chatterjee N. Shrinkage estimation for robust and efficient screening of single‐SNP association from case‐control genome‐wide association studies. Genetic Epidemiology 2009, 33: 740-750. PMID: 19434716, PMCID: PMC3103068, DOI: 10.1002/gepi.20428.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesComputational BiologyComputer SimulationData Interpretation, StatisticalFalse Positive ReactionsGenetic MarkersGenomeGenome, HumanGenome-Wide Association StudyGenotypeHumansLikelihood FunctionsModels, StatisticalPolymorphism, Single NucleotideReproducibility of ResultsConceptsHardy-Weinberg equilibriumAssociation TestPopulation-based case-control designGenome-wide association scanGenome-wide association studiesSingle-SNP associationsCase-control designCase-control studyAssociation scansAssociation studiesGenetic markersSusceptibility SNPsRecessive effectUnderlying populationAssociationFalse-positive resultsEfficient screeningSNPsRare diseaseShrinkage estimatorsSimulation studyStudyTestTwo-degrees-of-freedomPopulation
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
Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure
Zhang L, Mukherjee B, Ghosh M, Wu R. Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure. Statistical Modelling 2006, 6: 352-372. DOI: 10.1177/1471082006071841.Peer-Reviewed Original ResearchPopulation substructureCase-control studyGenetic association studiesLog odds ratio parametersOdds ratio parametersAssociation studiesAllele frequenciesGenetic associationParametric Bayesian methodsArgentinean populationBayesian modelCredible intervalsGenetic factorsBayesian methodsStatistical propertiesNumerical integration techniquesPosterior probabilityAssociation modelPopulationAllelesGenesAssociationIntegration techniqueMarkovObesity