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
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
2018
Improving 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
2015
Data-Adaptive Shrinkage via the Hyperpenalized EM Algorithm
Boonstra P, Taylor J, Mukherjee B. Data-Adaptive Shrinkage via the Hyperpenalized EM Algorithm. Statistics In Biosciences 2015, 7: 417-431. PMID: 26834856, PMCID: PMC4728141, DOI: 10.1007/s12561-015-9132-x.Peer-Reviewed Original Research
2014
A data-adaptive strategy for inverse weighted estimation of causal effects
Zhu Y, Ghosh D, Mitra N, Mukherjee B. A data-adaptive strategy for inverse weighted estimation of causal effects. Health Services And Outcomes Research Methodology 2014, 14: 69-91. DOI: 10.1007/s10742-014-0124-y.Peer-Reviewed Original ResearchEstimation of causal effectsData analysis examplesAverage treatment effectNonparametric modelSimulation studyTheoretical resultsPropensity scoreEffect of confoundersMeasured covariatesWeight estimationCausal effectsNonrandomized observational studyTreatment effectsLogistic regressionObservational studyAnalysis exampleRandomized trialsConfoundingExamplesScoresCovariatesInferenceEstimation
2013
Bayesian shrinkage methods for partially observed data with many predictors
Boonstra P, Mukherjee B, Taylor J. Bayesian shrinkage methods for partially observed data with many predictors. The Annals Of Applied Statistics 2013, 7: 2272-2292. PMID: 24436727, PMCID: PMC3891514, DOI: 10.1214/13-aoas668.Peer-Reviewed Original ResearchFraction of missing informationOptimal bias-variance tradeoffBayesian shrinkage methodsEmpirical Bayes algorithmComprehensive simulation studyBias-variance tradeoffSurrogate covariatesSimulation studyShrinkage methodCovariatesPrediction problemState-of-the-artModel parametersProblemMissing dataLung cancer datasetBayes algorithmState-of-the-art technologiesArray technologyCancer datasetsQRT-PCR
2012
Point source modeling of matched case–control data with multiple disease subtypes
Li S, Mukherjee B, Batterman S. Point source modeling of matched case–control data with multiple disease subtypes. Statistics In Medicine 2012, 31: 3617-3637. PMID: 22826092, PMCID: PMC4331356, DOI: 10.1002/sim.5388.Peer-Reviewed Original ResearchConceptsAdjacent-category logit modelMarkov chain Monte Carlo techniquesEvaluate maximum likelihoodExtensive simulation studyProfile likelihoodHierarchical Bayesian approachCase-control dataSimulation studyBayesian approachMonte Carlo techniqueBayesian methodsMaximum likelihoodMultiple disease subtypesCategorical outcomesCovariate adjustmentNonlinear modelEstimation stabilityMedicaid claims dataCase-control designPediatric asthma populationAsthma populationElevated oddsMarkovLogit modelCovariates
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
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 NOTE ON SAMPLING DESIGNS FOR RANDOM PROCESSES WITH NO QUADRATIC MEAN DERIVATIVE
Mukherjee B. A NOTE ON SAMPLING DESIGNS FOR RANDOM PROCESSES WITH NO QUADRATIC MEAN DERIVATIVE. Australian & New Zealand Journal Of Statistics 2006, 48: 305-319. DOI: 10.1111/j.1467-842x.2006.00442.x.Peer-Reviewed Original Research
2005
Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure
Sinha S, Mukherjee B, Ghosh M, Mallick B, Carroll R. Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure. Journal Of The American Statistical Association 2005, 100: 591-601. DOI: 10.1198/016214504000001411.Peer-Reviewed Original Research