2018
Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes
Yu Y, Xia L, Lee S, Zhou X, Stringham H, Boehnke M, Mukherjee B. Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes. Human Heredity 2018, 83: 283-314. PMID: 31132756, PMCID: PMC7034441, DOI: 10.1159/000496867.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesCholesterolCohort StudiesComputer SimulationC-Reactive ProteinFinlandGene FrequencyGene-Environment InteractionGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLipoproteins, LDLMeta-Analysis as TopicModels, GeneticPhenotypePolymorphism, Single NucleotideConceptsPresence of G-E interactionsGenetic associationHeterogeneity of genetic effectsDiscovery of genetic associationsGene-environment (G-EMarginal genetic effectsG-E interactionsGenome-wide association studiesGene-environment interactionsGenetic effectsData examplesSimulation studySingle nucleotide polymorphismsGene-environmentAssociation studiesAssociation analysisScreening toolMarginal associationNucleotide polymorphismsPresence of heterogeneityAssociationEnvironmental factorsIncreased powerMultiple studiesG-E
2013
Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons
Sun Z, Tao Y, Li S, Ferguson K, Meeker J, Park S, Batterman S, Mukherjee B. Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environmental Health 2013, 12: 85. PMID: 24093917, PMCID: PMC3857674, DOI: 10.1186/1476-069x-12-85.Peer-Reviewed Original ResearchConceptsMultipollutant modelsHealth impacts of environmental factorsEffect estimatesExposure-response associationsExposure to multiple pollutantsTime series designConsequence of environmental exposureSample sizeHealth impactsEnvironmental exposuresPresence of multicollinearityRisk predictionPotential interactive effectsInitial screeningPollutant mixturesImpact of environmental factorsSupervised principal component analysisModel dimensionsStatistical literatureData examplesTree-based methodsMultiple pollutantsVariable selectionSimulation studyReduce model dimensionPropensity score‐based diagnostics for categorical response regression models
Boonstra P, Bondarenko I, Park S, Vokonas P, Mukherjee B. Propensity score‐based diagnostics for categorical response regression models. Statistics In Medicine 2013, 33: 455-469. PMID: 23934948, PMCID: PMC3911784, DOI: 10.1002/sim.5940.Peer-Reviewed Original ResearchConceptsRetrospective sampling designsChi-square distributionCategorical response modelsGoodness-of-fit statisticsPredicted response probabilitiesResponse regression modelsConditional distributionProportional odds modelAssess model adequacyData examplesSimulation studyVA Normative Aging StudyNormative Aging StudyPropensity scoreCumulative lead exposureOdds modelModel diagnosticsCase-control studyAssociated with diabetesBalance scoresResponse probabilityModel adequacyCohort studyAging StudyNumerical summaries
2008
Fitting stratified proportional odds models by amalgamating conditional likelihoods
Mukherjee B, Ahn J, Liu I, Rathouz P, Sánchez B. Fitting stratified proportional odds models by amalgamating conditional likelihoods. Statistics In Medicine 2008, 27: 4950-4971. PMID: 18618428, PMCID: PMC3085191, DOI: 10.1002/sim.3325.Peer-Reviewed Original ResearchConceptsNuisance parametersConditional likelihoodProportional odds modelStratum-specific nuisance parametersCumulative logit modelStratum-specific interceptsGeneral regression frameworkMultiple ordered categoriesOdds modelContinuous covariatesSandwich estimatorData examplesBinary exposureRobust sandwich estimatorLikelihood principleProportional oddsStandard softwareRegression frameworkNatural choiceOutcome modelEstimationClassical methodsStratified dataLogistic regression modelsRandom-effects model