2012
Point source modeling of matched case–control data with multiple disease subtypes
Li S, Mukherjee B, Batterman S. Point source modeling of matched case–control data with multiple disease subtypes. Statistics In Medicine 2012, 31: 3617-3637. PMID: 22826092, PMCID: PMC4331356, DOI: 10.1002/sim.5388.Peer-Reviewed Original ResearchConceptsAdjacent-category logit modelMarkov chain Monte Carlo techniquesEvaluate maximum likelihoodExtensive simulation studyProfile likelihoodHierarchical Bayesian approachCase-control dataSimulation studyBayesian approachMonte Carlo techniqueBayesian methodsMaximum likelihoodMultiple disease subtypesCategorical outcomesCovariate adjustmentNonlinear modelEstimation stabilityMedicaid claims dataCase-control designPediatric asthma populationAsthma populationElevated oddsMarkovLogit modelCovariates
2010
Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification
Ahn J, Mukherjee B, Gruber S, Sinha S. Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification. Biometrics 2010, 67: 546-558. PMID: 20560931, PMCID: PMC3119773, DOI: 10.1111/j.1541-0420.2010.01453.x.Peer-Reviewed Original ResearchConceptsStereotype regression modelSubtypes of casesDeletion of observationsExpectation/conditional maximization algorithmBaseline category logit modelEstimation of model parametersMissingness mechanismData mechanismCase-control dataProportional oddsBayesian approachCategorical responsesCase-control studyCase-control study of colorectal cancerMissingnessMaximization algorithmCategorical outcomesMonte CarloModel assumptionsRegression modelsStudy of colorectal cancerModel parametersNonidentifiabilityDisease subclassificationMultinomial logit model
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