Ralitza Gueorguieva, PhD
Senior Research Scientist in Biostatistics; Director of Biostatistics in PsychiatryCards
About
Titles
Senior Research Scientist in Biostatistics; Director of Biostatistics in Psychiatry
Biography
Dr. Gueorguieva obtained her Ph.D. in Statistics from University of Florida in 1999 and has been a faculty member of the Department of Biostatistics at Yale University School of Public Health since 2000. Her research interests are in methodological development of models for longitudinal data and outcomes of different types, assessment of risk and the application of innovative statistical approaches for clinical trials data. She has developed statistical techniques for simultaneous analysis of repeatedly measured categorical and continuous outcomes, risk assessment screening algorithms, models for joint analysis of longitudinal and survival outcomes, and has applied trajectory-based approaches and recursive partitioning methods to the assessment of treatment effects. Dr. Gueorguieva is collaborating extensively with psychiatric researchers on clinical trials and observational studies in tobacco research, alcoholism, substance abuse, depression and anxiety disorders, eating disorders, women's behavioral health, schizophrenia, bipolar disorder, obsessive-compulsive disorder and posttraumatic stress disorder. She advocates the use of modern statistical methods such as mixed and mixture models, tree-based methods and nonparametric alternatives to address the complexity of psychiatric data.
Appointments
Biostatistics
Senior Research ScientistPrimary
Other Departments & Organizations
- ADVANCE Study
- Biostatistics
- Center for Biomedical Data Science
- Center for the Translational Neuroscience of Alcohol
- Neuroscience Research Training Program (NRTP)
- Statistical Methods in Psychiatry and Related Fields: Longitudinal, Clustered and Other Repeated Measures Data
- Yale School of Public Health
- Yale Tobacco Center of Regulatory Science
Education & Training
- PhD
- University of Florida, Statistics (1999)
- MSc
- University of Florida, Statistics (1996)
- MSc
- Sofia University, Informatics (1994)
Research
Overview
Dr. Gueorguieva's research interests are in methodological development of models for longitudinal data and outcomes of different types, assessment of risk and the application of innovative statistical approaches for the analyses of clinical trials data. She has developed statistical techniques for simultaneous analysis of repeatedly measured categorical and continuous outcomes, and of longitudinal and survival outcomes, has developed risk assessment screening algorithms, and has applied trajectory-based approaches and recursive partitioning methods to the assessment of treatment effects. Dr. Gueorguieva is collaborating extensively with psychiatric researchers on clinical trials and observational studies in tobacco research, alcoholism, substance abuse, depression and anxiety disorders, women's behavioral health, schizophrenia, bipolar disorder, obsessive-compulsive disorder and posttraumatic stress disorder. She advocates the use of modern statistical methods such as mixed and mixture models, tree-based methods and nonparametric alternatives to address the complexity of psychiatric data.
- Predictors and Moderators of Response to Treatment
- Growth Mixture Modeling of Longitudinal Data
- Joint Analysis of Repeatedly Measured Outcomes and Competing Risks
- Mixture Models for Semi-Ordinal Data
- Mixture Models for Simultaneous Analysis of Abstinence, Frequency and Intensity of Drinking
- Center for the Translational Neuroscience of Alcoholism
- Yale Center for the Study of Tobacco Product Use and Addiction: Flavors, Nicotine and Other Constituents
- YALE-SCORE on Sex Differences in Alcohol Use Disorder
- The STRONG STAR Consortium to Alleviate PTSD
Medical Research Interests
Public Health Interests
Publications
Featured Publications
Illusory generalizability of clinical prediction models
Chekroud A, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, Kambeitz J, Corlett P, Koutsouleris N, Krumholz H, Krystal J, Paulus M. Illusory generalizability of clinical prediction models. Science 2024, 383: 164-167. PMID: 38207039, DOI: 10.1126/science.adg8538.Peer-Reviewed Original ResearchLongitudinal Trajectories of Multiple Nicotine Product Use Among Youths in the Population Assessment of Tobacco and Health Study
Simon P, Jiang Y, Buta E, Sartor CE, Krishnan-Sarin S, Gueorguieva R. Longitudinal Trajectories of Multiple Nicotine Product Use Among Youths in the Population Assessment of Tobacco and Health Study. JAMA Network Open 2022, 5: e223549. PMID: 35319763, PMCID: PMC8943628, DOI: 10.1001/jamanetworkopen.2022.3549.Peer-Reviewed Original ResearchConceptsNicotine product useCigar useTobacco usersTobacco useHealth StudyCigarette useProduct useWave 1Significant public health concernLongitudinal trajectoriesPublic health concernNon-Hispanic whitesTobacco product useLogistic regression modelsTobacco use behaviorsComplex survey designMultinomial logistic regression modelsDays of useSocioecological factorsMAIN OUTCOMESmokeless tobaccoLower oddsNicotine productsLatent class growth analysisSociodemographic factorsTrajectories of relapse in randomised, placebo-controlled trials of treatment discontinuation in major depressive disorder: an individual patient-level data meta-analysis
Gueorguieva R, Chekroud AM, Krystal JH. Trajectories of relapse in randomised, placebo-controlled trials of treatment discontinuation in major depressive disorder: an individual patient-level data meta-analysis. The Lancet Psychiatry 2017, 4: 230-237. PMID: 28189575, PMCID: PMC5340978, DOI: 10.1016/s2215-0366(17)30038-x.Peer-Reviewed Original ResearchConceptsActive medicationActive treatmentClinical trialsDepression severityHamilton Depression Rating Scale scoresDepression Rating Scale scoresClinical Global Impression scoresIndividual patient-level dataDouble-blind treatmentPlacebo-controlled trialPatterns of relapseGlobal Impression scoresIndividual patient dataPrevention of relapseTrajectory class membershipTreatment of depressionMajor depressive disorderRating Scale scoresPatient-level dataPost-traumatic stress disorderTreatment discontinuationAntidepressant treatmentClinical responseAlcohol Research CenterAntidepressant medicationTemporal patterns of adherence to medications and behavioral treatment and their relationship to patient characteristics and treatment response
Gueorguieva R, Wu R, Krystal JH, Donovan D, O'Malley SS. Temporal patterns of adherence to medications and behavioral treatment and their relationship to patient characteristics and treatment response. Addictive Behaviors 2013, 38: 2119-2127. PMID: 23435273, PMCID: PMC3595348, DOI: 10.1016/j.addbeh.2013.01.024.Peer-Reviewed Original ResearchConceptsPercent heavy drinking daysAdherence trajectoriesExcellent adherersPercent days abstinentPatient characteristicsMedication adherenceTreatment outcomesMedication adherence trajectoriesPatterns of treatmentHeavy drinking daysPatterns of adherenceExcellent medication adherenceLack of benefitTrajectories of adherenceIntervention main effectsActive medicationAdverse eventsPharmacologic treatmentHigher percent days abstinentTreatment adherenceTreatment modalitiesWorse outcomesTreatment responseDays abstinentDrinking daysJoint Modelling of Longitudinal Outcome and Interval-Censored Competing Risk Dropout in a Schizophrenia Clinical Trial
Gueorguieva R, Rosenheck R, Lin H. Joint Modelling of Longitudinal Outcome and Interval-Censored Competing Risk Dropout in a Schizophrenia Clinical Trial. Journal Of The Royal Statistical Society Series A (Statistics In Society) 2011, 175: 417-433. PMID: 22468033, PMCID: PMC3315284, DOI: 10.1111/j.1467-985x.2011.00719.x.Peer-Reviewed Original ResearchLongitudinal outcomesClinical Antipsychotic TrialsQuality of lifeSchizophrenia clinical trialsStudy medicationAntipsychotic medicationClinical trialsAntipsychotic TrialsStudy participantsPhase IIntervention effectivenessMedicationsSignificant differencesSelection biasTrialsOutcomesJoint modelling approachCauses of dropoutLarge percentageParticipantsTolerabilityPatientsOutcome valuesEffect of dropoutStudy scheduleJoint analysis of repeatedly observed continuous and ordinal measures of disease severity
Gueorguieva RV, Sanacora G. Joint analysis of repeatedly observed continuous and ordinal measures of disease severity. Statistics In Medicine 2006, 25: 1307-1322. PMID: 16217846, DOI: 10.1002/sim.2270.Peer-Reviewed Original ResearchMove Over ANOVA: Progress in Analyzing Repeated-Measures Data andIts Reflection in Papers Published in the Archives of General Psychiatry
Gueorguieva R, Krystal JH. Move Over ANOVA: Progress in Analyzing Repeated-Measures Data andIts Reflection in Papers Published in the Archives of General Psychiatry. JAMA Psychiatry 2004, 61: 310-317. PMID: 14993119, DOI: 10.1001/archpsyc.61.3.310.Peer-Reviewed Original ResearchA Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses
Gueorguieva R, Agresti A. A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses. Journal Of The American Statistical Association 2001, 96: 1102-1112. DOI: 10.1198/016214501753208762.Peer-Reviewed Original ResearchStatistical Methods in Psychiatry and Related Fields: Longitudinal, Clustered and Other Repeated Measures Data.
Gueorguieva R. (2017). Chapman & Hall/CRC Interdisciplinary Statistics series. ISBN 9781498740760.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
Clinical Trials
Current Trials
Behavioral and Neurochemical Mechanisms Underlying Stress-Precipitated Drinking
HIC ID2000021685RoleSub InvestigatorPrimary Completion Date09/30/2022Recruiting ParticipantsGenderBothAge21 years - 55 years
Academic Achievements & Community Involvement
News & Links
News
- December 11, 2024Source: The American Journal of Psychiatry
Cognitive Behavioral Therapy and Lisdexamfetamine, Alone and Combined, for Binge-Eating Disorder With Obesity: A Randomized Controlled Trial
- October 24, 2024Source: Cambridge University Press
Lisdexamfetamine Maintenance Treatment for Binge-Eating Disorder Following Successful Treatments
- May 01, 2024Source: Yale News
Understanding Youth Nicotine Use to Prevent Initiation and Escalation
- January 12, 2024Source: Yale News
Quest for Personalized Medicine Hits a Snag
Get In Touch
Contacts
Locations
300 George Street
Academic Office
Ste 501
New Haven, CT 06511