2022
Models for Zero-Inflated and Overdispersed Correlated Count Data: An Application to Cigarette Use
Pittman B, Buta E, Garrison K, Gueorguieva R. Models for Zero-Inflated and Overdispersed Correlated Count Data: An Application to Cigarette Use. Nicotine & Tobacco Research 2022, 25: 996-1003. PMID: 36318799, PMCID: PMC10077942, DOI: 10.1093/ntr/ntac253.Peer-Reviewed Original ResearchConceptsCorrelated count dataCount outcomesCount dataSubject-specific interpretationZero-InflatedIncorrect statistical inferenceStatistical inferenceCorrelated countsPoisson distributionOverdispersionModel assumptionsPoisson modelRandom effectsHurdle Poisson modelProper modelNegative binomial modelBinomial modelSuch dataAppropriate modelBest fitLarge varianceTobacco researchSuch outcomesModel fitTraining app
2020
Two‐part models for repeatedly measured ordinal data with “don't know” category
Gueorguieva R, Buta E, Morean M, Krishnan‐Sarin S. Two‐part models for repeatedly measured ordinal data with “don't know” category. Statistics In Medicine 2020, 39: 4574-4592. PMID: 32909252, PMCID: PMC8025667, DOI: 10.1002/sim.8739.Peer-Reviewed Original ResearchConceptsAdaptive Gaussian quadratureCorrelated random effectsSAS PROC NLMIXEDOrdinal dataMaximum likelihood estimationTerms of biasStatistical dependenceNominal modelGaussian quadraturePROC NLMIXEDLikelihood estimationPartial orderingEstimation algorithmTwo-part modelModel formulationSimulation studyRandom effectsPredictor effectsSubmodelsOrdinal natureFormulationNLMIXEDQuadratureModelOrdering
2017
Bayesian Joint Modelling of Longitudinal Data on Abstinence, Frequency and Intensity of Drinking in Alcoholism Trials
Buta E, O’Malley S, Gueorguieva R. Bayesian Joint Modelling of Longitudinal Data on Abstinence, Frequency and Intensity of Drinking in Alcoholism Trials. Journal Of The Royal Statistical Society Series A (Statistics In Society) 2017, 181: 869-888. PMID: 31123390, PMCID: PMC6527419, DOI: 10.1111/rssa.12334.Peer-Reviewed Original ResearchBayesian joint modellingParameter estimate biasStandard frequentist approachRandom effectsLog-normal modelJoint modelFrequentist approachBayesian approachMean squared errorJoint modellingEstimate biasIntensity of drinkingSimulation studyFrequency of drinkingSeparate modellingModellingLongitudinal outcomesClinical trialsSame subjectsSustained abstinenceModelLogistic partAbstinence
2005
Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit-Specific Outcomes
Gueorguieva RV. Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit-Specific Outcomes. Biometrics 2005, 61: 862-866. PMID: 16135040, DOI: 10.1111/j.1541-020x.2005.00409_1.x.Peer-Reviewed Original ResearchConceptsCluster sizeJoint modelingContinuous response variablesMaximum likelihood estimatesCluster-level random effectsMaximum likelihood approachData examplesPrior specificationBayesian approachLikelihood estimatesGeneral situationAlternative parameterizationsStandard softwareRandom effectsGeneral modelResponse variablesCluster-level factorsBest fitDunsonExtensive programmingData setsEstimatesModelingModelInference
2001
A multivariate generalized linear mixed model for joint modelling of clustered outcomes in the exponential family
Gueorguieva R. A multivariate generalized linear mixed model for joint modelling of clustered outcomes in the exponential family. Statistical Modelling 2001, 1: 177-193. DOI: 10.1177/1471082x0100100302.Peer-Reviewed Original ResearchExponential familyMonte Carlo EM algorithmJoint multivariate normal distributionGeneralized linear mixed modelMultivariate normal distributionMaximum likelihood estimation approachRandom effectsMultivariate caseData examplesMultivariate generalizationEM algorithmJoint modellingMultiple outcome variablesRestrictive assumptionsEstimation approachMeasures dataNormal distributionLinear mixed modelsMore variablesScore testMixed modelsSingle modelData setsAssumptionQuadrature