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Ralitza Gueorguieva, PhD

Senior Research Scientist in Biostatistics; Director of Biostatistics in Psychiatry
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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

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 Subject Headings (MeSH)

Biostatistics; Models, Statistical; Psychiatry; Research Design; Risk Assessment

Research at a Glance

Yale Co-Authors

Frequent collaborators of Ralitza Gueorguieva's published research.

Publications

2024

Clinical Trials

Current Trials

Academic Achievements & Community Involvement

  • activity

    Biological Psychiatry

  • activity

    NIMH

  • honor

    Certificate of Appreciation for dedicated service and valuable contributions as one of the Top 10 Reviewers for Biological Psychiatry in 2011.

  • activity

    Journal of the Royal Statistical Society, Series A

  • activity

    National Science Foundation

Get In Touch

Contacts

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