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
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