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 sourcesSet-Based Tests for the Gene–Environment Interaction in Longitudinal Studies
He Z, Zhang M, Lee S, Smith J, Kardia S, Roux V, Mukherjee B. Set-Based Tests for the Gene–Environment Interaction in Longitudinal Studies. Journal Of The American Statistical Association 2017, 112: 966-978. PMID: 29780190, PMCID: PMC5954413, DOI: 10.1080/01621459.2016.1252266.Peer-Reviewed Original ResearchGene-environment interactionsMulti-Ethnic Study of AtherosclerosisSet-based testMeasures of neighborhood environmentMarginal genetic associationsEnvironmental exposuresMulti-Ethnic StudyStudy of AtherosclerosisNeighborhood environmentMeasurement of blood pressureGene-environmentMain-effects modelScore type testsMethod of sievesLongitudinal measures of blood pressureRobust to misspecificationGenetic associationGenetic variantsLongitudinal studyMain effectStudy periodEffects modelContinuous environmental exposurePotential biasIndependent conditions