Featured Publications
Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care
Wiens J, Spector-Bagdady K, Mukherjee B. Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care. Annual Review Of Genomics And Human Genetics 2024, 25: 141-159. PMID: 38724019, DOI: 10.1146/annurev-genom-010323-010230.Peer-Reviewed Original ResearchChronic care managementSpectrum of careArtificial intelligenceClinical care decisionsAcademic medical centerEthical challengesClinical decision-makingImprove careCare decisionsPreventive careCare managementPrecision healthTertiary careLeveraging patient dataReduce inequalitiesCareMedical CenterInconsistent useSelection biasAI solutionsPatient dataMissing dataDecision-makingDesign imperfections
2019
The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities
Beesley L, Salvatore M, Fritsche L, Pandit A, Rao A, Brummett C, Willer C, Lisabeth L, Mukherjee B. The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities. Statistics In Medicine 2019, 39: 773-800. PMID: 31859414, PMCID: PMC7983809, DOI: 10.1002/sim.8445.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsMichigan Genomics InitiativeBiobank-based studiesHealth-related researchUK BiobankHealth researchDisease-gene associationsStudy designAgnostic searchBiobankDisease-treatmentInformatics infrastructureHypothesis-generating studyPhenotypic identificationGenome InitiativeMissing dataResource catalogExploratory questionsCurrent bodyBiobank researchData typesMedical researchRecruitment mechanismsPractical guidance
2018
Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE)
Ferguson K, Yu Y, Cantonwine D, McElrath T, Meeker J, Mukherjee B. Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE). Paediatric And Perinatal Epidemiology 2018, 32: 469-473. PMID: 30016545, PMCID: PMC6939297, DOI: 10.1111/ppe.12486.Peer-Reviewed Original ResearchConceptsLinear mixed modelsComplete-case analysisMultiple imputationEpidemiological studies of risk factorsImputed datasetsComplete-caseDemographic factorsStudy of risk factorsLIFECODES birth cohortUltrasound measurementsCalculate associationsBirth cohortCross-sectionEpidemiological studiesRisk factorsStudy visitsLongitudinal analysisParametric linear mixed modelImputationMissing dataMixed modelsLongitudinal measurementsSample sizeCovariate dataGrowth restriction
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
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