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
2018
Improving estimation and prediction in linear regression incorporating external information from an established reduced model
Cheng W, Taylor J, Vokonas P, Park S, Mukherjee B. Improving estimation and prediction in linear regression incorporating external information from an established reduced model. Statistics In Medicine 2018, 37: 1515-1530. PMID: 29365342, PMCID: PMC5889759, DOI: 10.1002/sim.7600.Peer-Reviewed Original ResearchConceptsOutcome variable YEfficiency of estimationApproximate Bayesian inferenceBayes solutionVariable YNonlinear constraintsInferential frameworkVariable BE(Y|XImprove inferenceBayesian inferenceEffective computational methodParameter spaceReduced modelImproved estimatesLinear regression modelsTransformation approachStandard errorDunsonInferenceEstimationRegression modelsProblemCovariatesSpace
2014
A data-adaptive strategy for inverse weighted estimation of causal effects
Zhu Y, Ghosh D, Mitra N, Mukherjee B. A data-adaptive strategy for inverse weighted estimation of causal effects. Health Services And Outcomes Research Methodology 2014, 14: 69-91. DOI: 10.1007/s10742-014-0124-y.Peer-Reviewed Original ResearchEstimation of causal effectsData analysis examplesAverage treatment effectNonparametric modelSimulation studyTheoretical resultsPropensity scoreEffect of confoundersMeasured covariatesWeight estimationCausal effectsNonrandomized observational studyTreatment effectsLogistic regressionObservational studyAnalysis exampleRandomized trialsConfoundingExamplesScoresCovariatesInferenceEstimation
2012
On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case‐control study of colorectal cancer
Ghosh M, Song J, Forster J, Mitra R, Mukherjee B. On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case‐control study of colorectal cancer. Statistics In Medicine 2012, 31: 2196-2208. PMID: 22495822, DOI: 10.1002/sim.5358.Peer-Reviewed Original ResearchConceptsPosterior inferenceCase-control study of colorectal cancerOdds ratio parametersCategorical response dataBayesian analysis of dataStudy of colorectal cancerCase-control studyGeneral classProspective likelihoodSimulation studyCategorical responsesBayesian analysisColorectal cancerMatched case-control studyInferenceAnalysis of dataResponse dataPriorsRetrospective designRetrospective modelEquivalence
2010
Bayesian and likelihood-based inference for the bivariate normal correlation coefficient
Ghosh M, Mukherjee B, Santra U, Kim D. Bayesian and likelihood-based inference for the bivariate normal correlation coefficient. Journal Of Statistical Planning And Inference 2010, 140: 1410-1416. DOI: 10.1016/j.jspi.2009.11.013.Peer-Reviewed Original ResearchAsymptotic matching of coverage probabilities of Bayesian credible intervalsCoverage probabilities of Bayesian credible intervalsHighest posterior density matchingInversion of test statisticsCredible intervalsLikelihood-based inferenceBayesian credible intervalsBivariate normal distributionLikelihood-based methodsTest statisticsNormal distributionQuantile matchingSimulation studyAsymptotic matchingDensity matchingInferencePriorsIntervalMatching criterionCoefficientProbability
2009
Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer
Ahn J, Mukherjee B, Banerjee M, Cooney K. Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer. Statistics In Medicine 2009, 28: 3139-3157. PMID: 19731262, PMCID: PMC3103066, DOI: 10.1002/sim.3693.Peer-Reviewed Original ResearchConceptsStereotype regression modelProportional odds modelLog-odds-ratioStereotype modelMaximum likelihood estimationOdds modelBayesian inferenceAdjacent category logit modelCase-control study of prostate cancerLack of identifiabilityModel comparison procedureLikelihood estimationProduct representationValid inferenceFrequentist approachUnordered outcomesCategorical responsesOrdered outcomesCategory-specific scoresOdd structuresComparison procedureCategorical outcomesLatent variablesInferenceCase-control study
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