2023
Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 Infection (PASC) in a Large Academic Medical Center in the US
Fritsche L, Jin W, Admon A, Mukherjee B. Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 Infection (PASC) in a Large Academic Medical Center in the US. Journal Of Clinical Medicine 2023, 12: 1328. PMID: 36835863, PMCID: PMC9967320, DOI: 10.3390/jcm12041328.Peer-Reviewed Original ResearchPost-Acute Sequelae of SARS CoV-2 infectionElectronic health record dataPhenotype risk scoreHealth record dataCase-control study designPhenome-wide scanAcademic medical centerRisk prediction modelPost-COVID-19Risk stratification approachStudy designRecord dataRisk scoreHistory of COVID-19Medical CenterCOVID-19Increased riskPre-COVID-19Post-acute sequelaePre-COVID-19 periodRiskPost-COVID-19 periodCohortStratification approachSARS CoV-2 infection
2022
A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns
Haupert S, Shi X, Chen C, Fritsche L, Mukherjee B. A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns. Journal Of Biomedical Informatics 2022, 136: 104237. PMID: 36283580, PMCID: PMC9595430, DOI: 10.1016/j.jbi.2022.104237.Peer-Reviewed Original ResearchConceptsPhenome-wide association studyPost-COVID-19 conditionCOVID-19 survivorsCohort of COVID-19 survivorsAssociation studiesMental health disordersConditional logistic regressionWithin-person confoundingSARS-CoV-2 infectionRobust study designsProportion of COVID-19 survivorsPost-COVID-19Healthcare needsMental healthSARS-CoV-2Circulatory diseasesPhenotype codesHealth disordersSARS-CoV-2 positivityStudy designSARS-CoV-2 positive patientsLogistic regressionPheWASPost-COVID-19 infectionCOVID-19Estimating COVID-19 Vaccination and Booster Effectiveness Using Electronic Health Records From an Academic Medical Center in Michigan
Roberts E, Gu T, Wagner A, Mukherjee B, Fritsche L. Estimating COVID-19 Vaccination and Booster Effectiveness Using Electronic Health Records From an Academic Medical Center in Michigan. AJPM Focus 2022, 1: 100015. PMID: 36942016, PMCID: PMC9323299, DOI: 10.1016/j.focus.2022.100015.Peer-Reviewed Original ResearchIntensive care unit admissionElectronic health record dataHealth record dataElectronic health recordsMedical CenterUnit admissionAcademic medical centerOdds of vaccinationHealth recordsSevere COVID-19 outcomesAffluent areasHealthcare workersStudy designRecord dataCalendar quarterCOVID-19COVID-19 outcomesDisease overallUniversity of Michigan Medical CenterObservational studySevere COVID-19SARS-CoV-2 infectionVaccine effectivenessBooster statusOngoing surveillance
2021
Performance of urine, blood, and integrated metal biomarkers in relation to birth outcomes in a mixture setting
Ashrap P, Watkins D, Mukherjee B, Rosario-Pabón Z, Vélez-Vega C, Alshawabkeh A, Cordero J, Meeker J. Performance of urine, blood, and integrated metal biomarkers in relation to birth outcomes in a mixture setting. Environmental Research 2021, 200: 111435. PMID: 34097892, PMCID: PMC8403638, DOI: 10.1016/j.envres.2021.111435.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreIntraclass correlation coefficientBirth outcomesBody mass indexWeighted quantile sumOdds ratio of preterm birthSecond-hand smoke exposurePre-pregnancy body mass indexOdds of preterm birthAssociated with birth outcomesIncreased odds of preterm birthPractice study designHealth effectsPreterm birthMaternal educationIncreased oddsOdds ratioSmoke exposureStudy designMaternal ageMass indexArea under the curveRisk scoreLogistic regressionConfidence intervals
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
2017
Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases
McAllister K, Mechanic L, Amos C, Aschard H, Blair I, Chatterjee N, Conti D, Gauderman W, Hsu L, Hutter C, Jankowska M, Kerr J, Kraft P, Montgomery S, Mukherjee B, Papanicolaou G, Patel C, Ritchie M, Ritz B, Thomas D, Wei P, Witte J, participants O. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. American Journal Of Epidemiology 2017, 186: 753-761. PMID: 28978193, PMCID: PMC5860428, DOI: 10.1093/aje/kwx227.Peer-Reviewed Original ResearchConceptsGene-environment interaction studiesStudies of complex diseasesGene-environmentAmerican Society of Human Genetics meetingMeasures of environmental exposureGene-environment interactionsComplex diseasesNational Institute of Environmental Health SciencesNational Cancer InstituteEnvironmental Health SciencesStudy designHealth SciencesCancer InstituteEnvironmental exposuresEnvironmental exposure assessmentNational InstituteLarge-scale studiesExposure assessmentNext-generation sequencing dataDisease outcomeNationalSequence dataThemesStudies of human populationsParticipants
2012
Likelihood‐based methods for regression analysis with binary exposure status assessed by pooling
Lyles R, Tang L, Lin J, Zhang Z, Mukherjee B. Likelihood‐based methods for regression analysis with binary exposure status assessed by pooling. Statistics In Medicine 2012, 31: 2485-2497. PMID: 22415630, PMCID: PMC3528351, DOI: 10.1002/sim.4426.Peer-Reviewed Original ResearchConceptsPopulation-based case-control study of colorectal cancerCase-control study of colorectal cancerPopulation-based case-control studyStudy of colorectal cancerExposure statusBinary outcomesRegression modelsCase-control sampleLogistic regression modelsGene-disease associationsObserved binary outcomeStudy designEpidemiological studiesColorectal cancerAssess exposureMaximum likelihood analysisRegression analysisLikelihood-based methodsExposure assessmentMaximum likelihood approachLikelihood approachCross-sectionSimulation studyOutcomesLikelihood analysis
2011
Comparative Performance of Comorbidity Indices in Discriminating Health-related Behaviors and Outcomes
Ou H, Mukherjee B, Erickson S, Piette J, Bagozzi R, Balkrishnan R. Comparative Performance of Comorbidity Indices in Discriminating Health-related Behaviors and Outcomes. Health Outcomes Research In Medicine 2011, 2: e91-e104. DOI: 10.1016/j.ehrm.2011.06.002.Peer-Reviewed Original ResearchChronic disease scoreHealth care utilizationElixhauser indexCare utilizationHealth-related quality of life comorbidity indexHealth care outcomesStandard adherenceHealth-related behaviorsHealth-related qualityPatient medication adherenceHealth care behaviorComorbidity indexPredicting hospital admissionMedical resource useCare outcomesMedication adherenceCaring behaviorsOutpatient visitsType 2 diabetesComorbidity indicatorsHospital admissionMedicaid patientsStudy designLogistic regressionHealth
2008
Inference of the Haplotype Effect in a Matched Case-Control Study Using Unphased Genotype Data
Sinha S, Gruber S, Mukherjee B, Rennert G. Inference of the Haplotype Effect in a Matched Case-Control Study Using Unphased Genotype Data. The International Journal Of Biostatistics 2008, 4: article 6. PMID: 20231916, PMCID: PMC2835450, DOI: 10.2202/1557-4679.1079.Peer-Reviewed Original ResearchConceptsCase-control studyUnphased genotype dataHardy-Weinberg equilibriumLocus-specific genotype dataGenotype dataBeta-Carotene Cancer Prevention StudyCancer Prevention StudyCase-control study designStudy of breast cancer patientsMatched case-control studyCase-control designPhasing of haplotypesDisease risk modelsBreast cancer patientsPrevention StudyHaplotype effectsStudy designGametic phasePolymorphic lociHaplotype frequenciesCancer patientsLociConditional likelihood approachAssociationHaplotypes
2007
Accounting for error due to misclassification of exposures in case–control studies of gene–environment interaction
Zhang L, Mukherjee B, Ghosh M, Gruber S, Moreno V. Accounting for error due to misclassification of exposures in case–control studies of gene–environment interaction. Statistics In Medicine 2007, 27: 2756-2783. PMID: 17879261, DOI: 10.1002/sim.3044.Peer-Reviewed Original ResearchConceptsCase-control studyCase-control study of colorectal cancerGene-environment independence assumptionStudy of gene-environment interactionsStudy of colorectal cancerCase-control study designEnvironmental exposuresDisease-exposure associationsCase-control dataMisclassification of exposureGene-environment interactionsDegree of misclassificationStudy designConfidence intervalsGenotyping errorsValidation subsampleColorectal cancerAnalysis of dataMisclassification error rateGenetic factorsIndependence assumptionMisclassificationMisclassified dataAnalytical formEstimation strategy