Ralitza Gueorguieva, PhD
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Research Summary
Professor Gueorguieva's interests are in development and application of statistical methods for the analysis of data from clinical trials and epidemiological studies. She is working on models for repeatedly measured observations, survival outcomes and risk assessment. Her main collaborations are in the area of psychiatry and clinical neuroscience.
Extensive Research Description
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
Coauthors
Research Interests
Psychiatry; Research Design; Models, Statistical; Risk Assessment
Public Health Interests
Epidemiology Methods; Maternal & Child Health; Mental Health; Modeling; Substance Use, Addiction
Selected Publications
- Statistical Methods in Psychiatry and Related Fields: Longitudinal, Clustered and Other Repeated Measures Data.Gueorguieva R. (2017). Chapman & Hall/CRC Interdisciplinary Statistics series. ISBN 9781498740760.
- 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.
- Data Visualization Tools of Tobacco Product Use Patterns, Transitions and Sex Differences in the PATH Youth Data.Gueorguieva R, Buta E, Simon P, Krishnan-Sarin S, O'Malley SS. Data Visualization Tools of Tobacco Product Use Patterns, Transitions and Sex Differences in the PATH Youth Data. Nicotine & Tobacco Research : Official Journal Of The Society For Research On Nicotine And Tobacco 2020, 22:1901-1908.
- Bayesian joint modelling of longitudinal data on abstinence, frequency and intensity of drinking in alcoholism trials.Buta E, O'Malley SS, 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) 2018, 181:869-888.
- Trajectories 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.
- A modified classification tree method for personalized medicine decisions.Tsai WM, Zhang H, Buta E, O'Malley S, Gueorguieva R. A modified classification tree method for personalized medicine decisions. Statistics And Its Interface 2016, 9:239-253.
- 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, 35:4202-25.
- Temporal 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, 38:2119-27.
- Joint 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) 2012, 175:417-433.
- Trajectories of depression severity in clinical trials of duloxetine: insights into antidepressant and placebo responses.Gueorguieva R, Mallinckrodt C, Krystal JH. Trajectories of depression severity in clinical trials of duloxetine: insights into antidepressant and placebo responses. Archives Of General Psychiatry 2011, 68:1227-37.
- Dirichlet Component Regression and its Applications to Psychiatric Data.Gueorguieva R, Rosenheck R, Zelterman D. Dirichlet Component Regression and its Applications to Psychiatric Data. Computational Statistics & Data Analysis 2008, 52:5344-5355.
- Joint 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, 25:1307-22.
- 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, 61:862-6; discussion 866-7.
- Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry.Gueorguieva R, Krystal JH. Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry. Archives Of General Psychiatry 2004, 61:310-7.
- Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Response.Gueorguieva, R.V. and Agresti, A. Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Response. Journal of the American Statistical Association 96: 1102-1112, 2001.
Clinical Trials
| Conditions | Study Title |
|---|---|
| Addictive Behaviors; Alcohol Addiction; Mental Health & Behavioral Research | Behavioral and Neurochemical Mechanisms Underlying Stress-Precipitated Drinking |