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 sourcesExploiting Gene-Environment Independence for Analysis of Case–Control Studies: An Empirical Bayes-Type Shrinkage Estimator to Trade-Off Between Bias and Efficiency
Mukherjee B, Chatterjee N. Exploiting Gene-Environment Independence for Analysis of Case–Control Studies: An Empirical Bayes-Type Shrinkage Estimator to Trade-Off Between Bias and Efficiency. Biometrics 2007, 64: 685-694. PMID: 18162111, DOI: 10.1111/j.1541-0420.2007.00953.x.Peer-Reviewed Original ResearchConceptsGene-environment independenceShrinkage estimatorsLog odds ratio parametersCase-control dataGene-environment independence assumptionOdds ratio parametersCase-control estimatorsData-adaptive fashionData exampleProspective logistic regression analysisBinary exposureGene-environment associationsIndependence assumptionLogistic regression analysisCase-onlyMaximum likelihood frameworkEstimationSample sizeBinary genesRegression analysisChatterjeeExamplesWeighted averageAssumptions
2008
A note on bias due to fitting prospective multivariate generalized linear models to categorical outcomes ignoring retrospective sampling schemes
Mukherjee B, Liu I. A note on bias due to fitting prospective multivariate generalized linear models to categorical outcomes ignoring retrospective sampling schemes. Journal Of Multivariate Analysis 2008, 100: 459-472. PMID: 34194120, PMCID: PMC8240662, DOI: 10.1016/j.jmva.2008.05.011.Peer-Reviewed Original ResearchOutcome dependent samplingCase-control sampling designData exampleBias approximationCategorical outcomesSampling designOngoing ProstateDisease sub-classificationLogit linkDependent samplesGeneralized linear modelLinear modelEquivalenceResponse fallApproximate expressionExamplesApproximationCancer Screening TrialInferenceCase-control study