Featured Publications
A meta-inference framework to integrate multiple external models into a current study.
Gu T, Taylor J, Mukherjee B. A meta-inference framework to integrate multiple external models into a current study. Biostatistics 2021, 24: 406-424. PMID: 34269371, PMCID: PMC10102901, DOI: 10.1093/biostatistics/kxab017.Peer-Reviewed Original ResearchConceptsAccuracy of statistical inferenceEmpirical Bayes estimatorsSummary-level informationBias-variance trade-offRelevant external informationBayes estimatorsStatistical inferenceExternal informationExternal estimatesNaive analysisNaive combinationInternational dataWeight estimationExternal modelMeta-analysis frameworkIndividual-level dataEfficiency gainsEstimationInfluence of informationTrade-offsInformationFramework
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
2023
A Synthetic Data Integration Framework to Leverage External Summary-Level Information from Heterogeneous Populations
Gu T, Taylor J, Mukherjee B. A Synthetic Data Integration Framework to Leverage External Summary-Level Information from Heterogeneous Populations. Biometrics 2023, 79: 3831-3845. PMID: 36876883, PMCID: PMC10480346, DOI: 10.1111/biom.13852.Peer-Reviewed Original ResearchConceptsCovariate effectsStatistical inferenceHeterogeneity of covariate effectsRegression coefficient estimatesSummary-level informationImprove statistical inferenceInternational studiesOutcome YCovariate informationData integration frameworkStatistical efficiencyCoefficient estimatesPartial informationExternal populationGeneral frameworkIndividual-level dataRisk prediction modelExternal modelPrediction problemInternational study populationMultiple imputation
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
Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability
Estes J, Mukherjee B, Taylor J. Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability. Statistics In Biosciences 2018, 10: 568-586. PMID: 31123532, PMCID: PMC6529204, DOI: 10.1007/s12561-018-9217-4.Peer-Reviewed Original ResearchEmpirical Bayes estimatorsSummary-level informationConstrained maximum likelihoodBayes estimatorsEmpirical Bayes shrinkage estimatorsSimulation studyBayes shrinkage estimatorShrinkage estimatorsLikelihood estimationCovariate distributionsConditional probability distributionData applicationsTrade biasMaximum likelihoodProbability distributionLoss of efficiencyCancer Prevention TrialIndividual-level dataEstimationProstate Cancer Prevention TrialPrevention trialsInternational population