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
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
2011
Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems
Hess KR, Wei C, Qi Y, Iwamoto T, Symmans WF, Pusztai L. Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems. BMC Bioinformatics 2011, 12: 463. PMID: 22132775, PMCID: PMC3245512, DOI: 10.1186/1471-2105-12-463.Peer-Reviewed Original ResearchConceptsPrediction problemCurrent statistical methodsClinical prediction problemsReal data setsMonte Carlo cross validationStatistical methodsData setsAccurate modelPerturbedInformative featuresPrediction modelCancer data setsPredictor performanceGene expression dataProblemBreast cancer data setsClassification problemSuch featuresMean expression valuesSet
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