Featured Publications
Statistical Inference for Association Studies Using Electronic Health Records: Handling Both Selection Bias and Outcome Misclassification
Beesley L, Mukherjee B. Statistical Inference for Association Studies Using Electronic Health Records: Handling Both Selection Bias and Outcome Misclassification. Biometrics 2020, 78: 214-226. PMID: 33179768, DOI: 10.1111/biom.13400.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsElectronic health record data analysisElectronic health record settingsSelection biasMichigan Genomics InitiativeAssociation studiesEHR-linkedHealth researchInverse probability weighting methodStudy sampleEffect estimatesProbability weighting methodLack of representativenessType I errorSurvey sampling literatureStandard error estimatesGold standard labelsDisease statusError estimatesStatistical inferenceMisclassificationInference strategySampling literatureStandard labels
2012
A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case–Control Studies
Bhadra D, Daniels M, Kim S, Ghosh M, Mukherjee B. A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case–Control Studies. Biometrics 2012, 68: 361-370. PMID: 22313248, PMCID: PMC3935236, DOI: 10.1111/j.1541-0420.2011.01686.x.Peer-Reviewed Original ResearchConceptsBayesian semiparametric approachSemiparametric approachCase-control studyReversible jump Markov chain Monte Carlo algorithmMarkov chain Monte Carlo algorithmMeasures of cumulative exposureLongitudinal biomarker informationMonte Carlo algorithmClinically meaningful estimatesSmooth functionsCase-control study of prostate cancerWeighted integralsCumulative exposureInfluence functionJoint likelihoodLikelihood formulationExposure historyStudy of prostate cancerDisease risk modelsHierarchical Bayesian frameworkDisease statusBayesian frameworkCase-controlRisk modelCohort study
2011
Logistic regression analysis of biomarker data subject to pooling and dichotomization
Zhang Z, Liu A, Lyles R, Mukherjee B. Logistic regression analysis of biomarker data subject to pooling and dichotomization. Statistics In Medicine 2011, 31: 2473-2484. PMID: 21953741, DOI: 10.1002/sim.4367.Peer-Reviewed Original ResearchConceptsPopulation-based case-control study of colorectal cancerCase-control study of colorectal cancerProspective logistic regression modelPopulation-based case-control studyStudy of colorectal cancerEpidemiological studiesLogistic regression modelsAnalysis of epidemiological dataLogistic regression analysisBinary exposurePooled measureColorectal cancerRegression modelsEpidemiological dataRegression analysisAnalysis of biomarker dataDisease statusExposed subjectsBiomarker dataChoice of designSubjectsEstimated parametersStatusRecommendations