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
2016
Correlated probit analysis of repeatedly measured ordinal and continuous outcomes with application to the Health and Retirement Study
Grigorova D, Gueorguieva R. Correlated probit analysis of repeatedly measured ordinal and continuous outcomes with application to the Health and Retirement Study. Statistics In Medicine 2016, 35: 4202-4225. PMID: 27222058, DOI: 10.1002/sim.6982.Peer-Reviewed Original ResearchCross-trial prediction of treatment outcome in depression: a machine learning approach
Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, Cannon TD, Krystal JH, Corlett PR. Cross-trial prediction of treatment outcome in depression: a machine learning approach. The Lancet Psychiatry 2016, 3: 243-250. PMID: 26803397, DOI: 10.1016/s2215-0366(15)00471-x.Peer-Reviewed Original ResearchConceptsTreatment outcomesTreatment groupsEscitalopram treatment groupSpecific antidepressantsPatient-reported dataSequenced Treatment AlternativesClinical trial dataIndependent clinical trialsClinical remissionSymptomatic remissionClinical trialsTreatment efficacyPatientsProspective identificationTreatment alternativesTrial dataDepressionRemissionAntidepressantsOutcomesGroupLevel 1CitalopramCohortClinicians
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