A Biostatistics Seminar Series hosted by the NYU GPH Department of Biostatistics
Featuring: Linda Valeri, PhD, Assistant Professor in Biostatistics, Columbia University Mailman School of Public Health; Adjunct Assistant Professor of Epidemiology, Harvard T.H. Chan School of Public Health
Abstract: The adoption of digital technologies in Psychiatry holds promise for the evaluation of personalized causal effects to better inform behavioral treatment decisions in a patient population that displays substantial diversity in symptomatology even within the same diagnostic category. In this presentation I will discuss challenges in estimating the individual causal effect of mobile communication social network size on negative mood of bipolar and schizophrenia patients enrolled in a cohort study part of the Intensive Longitudinal Health Behavior Network. The first challenge is missing data, potentially dependent on participant health status, and the second challenge is non-stationarity of the time series, when the treatment effect may change over time. To address these challenges, we propose a Monte Carlo EM (MCEM) algorithm of the state space model to properly address missing data in non-stationary multivariate time series. We also propose a set of novel causal estimands for (potentially non-stationary) multivariate time series in N-of-1 studies to systematically summarize how time-varying exposures affect outcomes in the short and long term and derive their identification via the g-formula in the presence of exposure- and outcome-covariates feedbacks.
About the speaker: Linda Valeri, Assistant Professor of Biostatistics at Columbia University and Adjunct Assistant Professor of Epidemiology at Harvard University, is an expert biostatistician specializing in causal inference, with a focus on biostatistical methodology and statistical learning. She received her doctorate degree in Biostatistics from Harvard University. Her research encompasses causal mediation analysis, measurement error, missing data, and the integration of data from multiple sources, such as smartphone and wearable devices, life-course cohort studies, and electronic medical records, in diverse populations. Dr. Valeri has developed widely utilized open-access computational tools for causal inference, benefiting scientists across biomedical and social sciences. As PI of a career development award from the National Institute of Mental Health, of an R01 research grant from the National Institute of Aging and of an R21 from the National Institute of Environmental Health Sciences she collaborates with interdisciplinary teams to advance our understanding of mental health across the life-course, environmental determinants of health, and health disparities, contributing to informed policy-making. Finally, she serves as co-editor of the Data Science section of the journal Current Environmental Health Reports, Associate Editor for the International Journal of Biostatistics, as Statistical Editor for JAMA Psychiatry.