2022
Assessing the added value of linking electronic health records to improve the prediction of self-reported COVID-19 testing and diagnosis
Clark-Boucher D, Boss J, Salvatore M, Smith J, Fritsche L, Mukherjee B. Assessing the added value of linking electronic health records to improve the prediction of self-reported COVID-19 testing and diagnosis. PLOS ONE 2022, 17: e0269017. PMID: 35877617, PMCID: PMC9312965, DOI: 10.1371/journal.pone.0269017.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsCOVID-19-related outcomesCOVID-19 testingSurvey respondentsSelf-reported outcomesSelf-reported dataCOVID-19 outcomesElectronic recordsSurvey dataCOVID-19Prediction modelModel contextSurveyCOVID-19 diagnosisOutcomesPredictor variablesDigital surveyData sourcesCoronavirus disease 2019RespondentsPredictorsCOVID-19 casesDiagnosisRecords
2020
An analytic framework for exploring sampling and observation process biases in genome and phenome‐wide association studies using electronic health records
Beesley L, Fritsche L, Mukherjee B. An analytic framework for exploring sampling and observation process biases in genome and phenome‐wide association studies using electronic health records. Statistics In Medicine 2020, 39: 1965-1979. PMID: 32198773, DOI: 10.1002/sim.8524.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsAssociation studiesObservational health care databasesElectronic health record dataLongitudinal biorepository effortPhenome-wide association studyMichigan Genomics InitiativeHealth record dataHealth care databasesDisease-gene association studiesMichigan Health SystemCare databaseHealth systemPhenotype misclassificationStudy biasRecord dataNonprobability samplingAssociation analysisData sourcesGenome InitiativeMisclassificationAnalysis approachRecordsSensitivity analysis