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
2023
An inverse probability weighted regression method that accounts for right‐censoring for causal inference with multiple treatments and a binary outcome
Yu Y, Zhang M, Mukherjee B. An inverse probability weighted regression method that accounts for right‐censoring for causal inference with multiple treatments and a binary outcome. Statistics In Medicine 2023, 42: 3699-3715. PMID: 37392070, DOI: 10.1002/sim.9826.Peer-Reviewed Original ResearchConceptsRight censoringWeighted score functionCausal treatment effectsAverage treatment effectAsymptotic propertiesCensored componentPre-specified time windowEstimation consistencyRobustness propertiesSimulation studyBinary outcomesPresence of confoundersCensoringScoring functionInverse probabilityTreatment effectsEstimationSources of biasInferenceLetter CComparative effectiveness researchTreatment switchRegression methodLogistic regression modelsInsurance claims database
2021
A comparison of parametric propensity score‐based methods for causal inference with multiple treatments and a binary outcome
Yu Y, Zhang M, Shi X, Caram M, Little R, Mukherjee B. A comparison of parametric propensity score‐based methods for causal inference with multiple treatments and a binary outcome. Statistics In Medicine 2021, 40: 1653-1677. PMID: 33462862, DOI: 10.1002/sim.8862.Peer-Reviewed Original ResearchConceptsComparative effectiveness researchEstimation of causal effectsPropensity score-based methodsBinary outcomesInsurance networksCausal effectsPropensity score methodsPropensity-based methodsConfounding biasContinuous outcomesPharmacy claimsEffectiveness researchObservational studySimulation studyAdverse outcomesPropensity scoreEmergency room
2020
Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions
Zhang M, Yu Y, Wang S, Salvatore M, Fritsche L, He Z, Mukherjee B. Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions. Statistics In Medicine 2020, 39: 1675-1694. PMID: 32101638, DOI: 10.1002/sim.8505.Peer-Reviewed Original ResearchConceptsType I error rateType I error inflationIndependence assumptionWald and score testsCorrect type I error ratesSandwich variance estimatorSandwich estimatorScore testVariance estimationSimulation studyMisspecificationMichigan Genomics InitiativeStatistical practiceBinary outcomesTested interactionsEmpirical factsFlexible modelData modelTest of interactionBiobank studyInflationAssumptionsContinuous outcomesEpidemiological literatureLinear regression models
2018
Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information
Cheng W, Taylor J, Gu T, Tomlins S, Mukherjee B. Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2018, 68: 121-139. PMID: 31105344, PMCID: PMC6519970, DOI: 10.1111/rssc.12306.Peer-Reviewed Original ResearchOutcome variable YEfficiency of estimationMeasurement error literatureDistribution of B.Regression coefficientsVariable YRegression modelsBinary outcomesVariable BLogistic regression modelsRisk prediction modelAlternative expressionBinary BImproved estimatesGaussian distributionProstate Cancer Prevention Trial Risk CalculatorProstate cancer antigen 3Risk calculatorStandard errorEstimationPredictive powerAntigen 3RegressionHistorical data
2016
Mediation Formula for a Binary Outcome and a Time-Varying Exposure and Mediator, Accounting for Possible Exposure-Mediator Interaction
Chen Y, Mukherjee B, Ferguson K, Meeker J, VanderWeele T. Mediation Formula for a Binary Outcome and a Time-Varying Exposure and Mediator, Accounting for Possible Exposure-Mediator Interaction. American Journal Of Epidemiology 2016, 184: 157-159. PMID: 27325886, PMCID: PMC4945703, DOI: 10.1093/aje/kww045.Peer-Reviewed Original Research
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