Featured Publications
A framework for understanding selection bias in real-world healthcare data
Kundu R, Shi X, Morrison J, Barrett J, Mukherjee B. A framework for understanding selection bias in real-world healthcare data. Journal Of The Royal Statistical Society Series A (Statistics In Society) 2024, 187: 606-635. PMID: 39281782, PMCID: PMC11393555, DOI: 10.1093/jrsssa/qnae039.Peer-Reviewed Original ResearchElectronic health recordsSelection biasAssociation of cancerMultiple sources of biasHealth recordsHealthcare systemSources of biasReal-world healthcare dataBinary outcomesEstimation of associated parametersHealthcare dataReal-world dataPotential biasSample sizeStandard errorData exampleVariance formulaAnalysis of real-world dataAssociationSimulation studyWeighting approachBiological sexAssociated parametersBiasMultiple sources
2022
Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review
Chen C, Haupert S, Zimmermann L, Shi X, Fritsche L, Mukherjee B. Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review. The Journal Of Infectious Diseases 2022, 226: 1593-1607. PMID: 35429399, PMCID: PMC9047189, DOI: 10.1093/infdis/jiac136.Peer-Reviewed Original ResearchConceptsPost-COVID-19 conditionCondition prevalenceMeta-analysisGlobal prevalenceHealth effects of COVID-19Prevalence of post-COVID-19 conditionRegional prevalence estimationHealthcare systemPrevalence estimatesPooled prevalencePost-COVID-19Systematic reviewDerSimonian-Laird estimatorMeta-analyzedMemory problemsHealth effectsPrevalenceEffects of COVID-19Post-coronavirus disease 2019Long COVIDCOVID-19COVID-19 conditionsNonhospitalized patientsUnited States