Hanga Galfalvy, PhD
Associate Professor of Biostatistics (in Psychiatry) at Columbia University Medical Center
Dr. Galfalvy's areas of expertise include statistical methodology in psychiatric research, with a special focus on the prediction models for suicidal behavior from high-dimensional data, censored regression models, statistical genetics, and longitudinal data analysis in observational studies.
Dr. Galfalvy received her Ph.D. in Statistics from the University of Illinois at Urbana-Champaign in 2000. She has completed three years as a postdoctoral research scientist at the New York State Psychiatric Institute before her appointment in the Department of Psychiatry. Dr. Galfalvy recently completed successfully an NIMH-funded Mentored Quantitative Research Scientist Career Development Award (K-25 award) “Statistical Methods in Suicide Research”.
While her primary appointment is in the Division of Biostatistics at the Department of Psychiatry at Columbia University, she has an interdisciplinary appointment in the Department of Biostatistics in the Mailman School of Public Health at Columbia.
My main research interest lies in developing and validating statistical methods for predicting suicides and suicide attempts, analysis of observational studies and clinical trials for mood disorder subjects at high risk for suicidal behavior; analysis of genome-wide association studies of suicidal behavior.
Statistical Methods in Suicide Research: The aim of this study is to develop and evaluate efficient and flexible statistical methodology for the prediction of suicide attempts and completed suicides in patients with mood disorders based on demographic, clinical and biological data for the use of health professionals so that patients at risk for suicide can be identified and treated in advance.Suicide attempt is defined as a self-destructive act with at least some intent to end one's life. The focus will be on developing and evaluating predictive models based on a large number of retrospective and longitudinal measurements on each subject, as opposed to identification of individual risk factors. Another goal is the classification of post-mortem brain samples from suicide victims and normal controls as a method of identifying putative risk factors for suicidal behavior. This will include large-scale gene expression analysis as well as the analysis of receptor bindings and other biochemical information in suicide victims and normal controls.