Ralitza Gueorguieva, PhD

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