As part of the International Day of Women in Statistics and Data Science Conference, delivered a talk titled “Causal Inference and Evidence on Drug Outcomes Among Elderly Schizophrenia Patients.” The session, chaired and organized by Arinjita Bhattacharyya of Merck, focused on cutting-edge applications of AI and statistical methods in mental health research with a spotlight on schizophrenia. It also highlighted the power of statistical science in tackling some of the most pressing challenges in mental health and offered insights into the use of data to improve outcomes for vulnerable populations.
Normand’s presentation underscored the challenges and opportunities of using large observational databases to study antipsychotic treatment outcomes among elderly individuals with schizophrenia. Addressing the complexities of comparing outcomes across diverse treatment patterns, Normand showcased approaches such as targeted minimum loss-based estimation and transfer learning to enhance the robustness of findings. The session also explored broader issues in mental health, including disparities in treatment access, the role of social determinants of health, available US government income support, and the promise of AI in advancing diagnostic and therapeutic strategies.
Normand is the S. James Adelstein Professor of Health Care Policy (Biostatistics) at Harvard Medical School and a professor in the department of biostatistics at the Harvard School of Public Health. Her research spans health services and outcomes, emphasizing Bayesian methods, causal inference, provider profiling, and medical technology assessment. She also serves as director of the Medical Device Epidemiology Network’s Methodology Center.
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