2013
Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference
Li S, Mukherjee B, Batterman S, Ghosh M. Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference. Biometrics 2013, 69: 925-936. PMID: 24289144, PMCID: PMC4108592, DOI: 10.1111/biom.12102.Peer-Reviewed Original ResearchConceptsSemi-parametric Bayesian approachLikelihood-based approachRandom nuisance parametersTime series analysisFrequentist literatureNuisance parametersDirichlet processInferential issuesConditional likelihoodPosterior distributionRisk functionTime seriesBayesian workFrequentist approachCase-crossover designSimulation studyRestrictive assumptionsBayesian approachTime Series DataLikelihood formulationBayesian methodsEquivalent resultsBayesian analysisCase-crossoverBayesian frameworkBayesian semiparametric analysis for two-phase studies of gene-environment interaction
Ahn J, Mukherjee B, Gruber S, Ghosh M. Bayesian semiparametric analysis for two-phase studies of gene-environment interaction. The Annals Of Applied Statistics 2013, 7: 543-569. PMID: 24587840, PMCID: PMC3935248, DOI: 10.1214/12-aoas599.Peer-Reviewed Original ResearchBayesian variable selection algorithmTwo-phase sampling designGene-environment independencePseudo-likelihood methodJoint effects of genotypeGene-environment interactionsHigh-dimensional modelsWeighted likelihoodCase-control study of colorectal cancerJoint distributionHierarchical priorsSemiparametric analysisRetrospective likelihoodGenetic markersCovariate informationLikelihood methodSimulation studyStudy of gene-environment interactionsStudy of colorectal cancerVariable selection algorithmBayesian approachPhase I dataSub-sample of casesBayesian methodsBayesian analysis
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
2006
Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure
Zhang L, Mukherjee B, Ghosh M, Wu R. Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure. Statistical Modelling 2006, 6: 352-372. DOI: 10.1177/1471082006071841.Peer-Reviewed Original ResearchPopulation substructureCase-control studyGenetic association studiesLog odds ratio parametersOdds ratio parametersAssociation studiesAllele frequenciesGenetic associationParametric Bayesian methodsArgentinean populationBayesian modelCredible intervalsGenetic factorsBayesian methodsStatistical propertiesNumerical integration techniquesPosterior probabilityAssociation modelPopulationAllelesGenesAssociationIntegration techniqueMarkovObesity