2017
Exposure enriched outcome dependent designs for longitudinal studies of gene–environment interaction
Sun Z, Mukherjee B, Estes J, Vokonas P, Park S. Exposure enriched outcome dependent designs for longitudinal studies of gene–environment interaction. Statistics In Medicine 2017, 36: 2947-2960. PMID: 28497531, PMCID: PMC5523112, DOI: 10.1002/sim.7332.Peer-Reviewed Original ResearchConceptsLongitudinal cohort studyCohort studyCase-only designLongitudinal studyG x E interactionNormative Aging StudyComplete-case analysisGene-environmentSampling designCase-controlVeterans AdministrationComplex human diseasesE interactionExposure informationAging StudyOutcome trajectoriesStratified samplingRetrospective genotypingIndividual exposureCovariate dataExposure effectsJoint effectsOutcomesTime-varying outcomeEnvironmental factors
2014
The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification
Stenzel S, Ahn J, Boonstra P, Gruber S, Mukherjee B. The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification. European Journal Of Epidemiology 2014, 30: 413-423. PMID: 24894824, PMCID: PMC4256150, DOI: 10.1007/s10654-014-9908-1.Peer-Reviewed Original ResearchConceptsG-E interactionsExposure informationDetection of gene-environment interactionsPrevalence of exposureGene-environment interactionsSampling designCase-control studyRandom selection of subjectsPerformance of sampling designsCase-onlyExposure prevalenceJoint testExposure misclassificationCase-controlRare exposuresMarginal associationSelection of subjectsType I errorEmpirical simulation studyIdeal sampling schemesJoint effectsPrevalenceRandom selectionG-EMisclassificationA space-time point process model for analyzing and predicting case patterns of diarrheal disease in northwestern Ecuador
Ahn J, Johnson T, Bhavnani D, Eisenberg J, Mukherjee B. A space-time point process model for analyzing and predicting case patterns of diarrheal disease in northwestern Ecuador. Spatial And Spatio-temporal Epidemiology 2014, 9: 23-35. PMID: 24889991, PMCID: PMC4044631, DOI: 10.1016/j.sste.2014.02.001.Peer-Reviewed Original ResearchConceptsSampled communitiesNorthwestern EcuadorLog Gaussian Cox processRiver BasinRisk-related parametersTemporal variationSpace-time modelDiarrheal diseaseLongitudinal sampling designSampling designPoint process modelNatural environmentSampling regionSpatial clusteringSampling cycleCase eventsPoint patterns
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
2006
A NOTE ON SAMPLING DESIGNS FOR RANDOM PROCESSES WITH NO QUADRATIC MEAN DERIVATIVE
Mukherjee B. A NOTE ON SAMPLING DESIGNS FOR RANDOM PROCESSES WITH NO QUADRATIC MEAN DERIVATIVE. Australian & New Zealand Journal Of Statistics 2006, 48: 305-319. DOI: 10.1111/j.1467-842x.2006.00442.x.Peer-Reviewed Original Research
2003
Exactly optimal sampling designs for processes with a product covariance structure
Mukherjee B. Exactly optimal sampling designs for processes with a product covariance structure. Canadian Journal Of Statistics 2003, 31: 69-87. DOI: 10.2307/3315904.Peer-Reviewed Original ResearchOn Sampling Designs for Integral Estimation of a Random Process
Mukherjee B. On Sampling Designs for Integral Estimation of a Random Process. Communication In Statistics- Theory And Methods 2003, 32: 1647-1663. DOI: 10.1081/sta-120022249.Peer-Reviewed Original Research