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
2022
Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification
Beesley L, Mukherjee B. Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification. Statistics In Medicine 2022, 41: 5501-5516. PMID: 36131394, PMCID: PMC9826451, DOI: 10.1002/sim.9579.Peer-Reviewed Original ResearchConceptsElectronic health recordsElectronic health record data analysisElectronic health record settingsLeverages external data sourcesElectronic health record dataPopulation-based data sourcesEHR-based researchLongitudinal health informationUniversity of Michigan Health SystemHealth record dataSelection biasPopulation-based researchMichigan Health SystemMultiple sources of biasFactors related to selectionPatient-level dataHealth recordsHealth systemHealth informationPhenotype misclassificationSummary estimatesPhenotyping errorsCancer diagnosisSources of biasRecord data
2014
The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification
Stenzel S, Ahn J, Boonstra P, Gruber S, Mukherjee B. The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification. European Journal Of Epidemiology 2014, 30: 413-423. PMID: 24894824, PMCID: PMC4256150, DOI: 10.1007/s10654-014-9908-1.Peer-Reviewed Original ResearchConceptsG-E interactionsExposure informationDetection of gene-environment interactionsPrevalence of exposureGene-environment interactionsSampling designCase-control studyRandom selection of subjectsPerformance of sampling designsCase-onlyExposure prevalenceJoint testExposure misclassificationCase-controlRare exposuresMarginal associationSelection of subjectsType I errorEmpirical simulation studyIdeal sampling schemesJoint effectsPrevalenceRandom selectionG-EMisclassification