缅北强奸

Caleb Miles, PhD

Assistant Professor of Biostatistics
Columbia University Mailman School of Public Health

WHEN: Wednesday, January 24, 2024, from 3:30 to 4:30 p.m.

WHERE: hybrid | 2001 缅北强奸 College Avenue, room 1140;

NOTE: Dr. Miles will be presenting from New York

Abstract

As data sources have become more plentiful and readily accessible, the practice of data fusion has become increasingly ubiquitous. However, when the focus is on a causal effect on a particular outcome, a major limitation is that this outcome may not be available in all data sources. In fact, different randomized experiments or observational studies of a common exposure will often focus on potentially related, yet distinct outcomes. One such example is the Database of Cognitive Training and Remediation Studies (DoCTRS), which consists of several randomized trials of the effect of cognitive remediation therapy on various outcomes among patients with schizophrenia. We develop causally principled methodology for fusing data sets when multiple outcomes are observed across studies that leverages outcomes of secondary interest as informative proxies for the missing outcome of primary interest, thereby maximizing power and efficiency by making full use of the available data. As this methodology relies on a key transportability assumption, we also develop methods to assess the degree of sensitivity to violations of this assumption. We apply this methodology to data from the DoCTRS trials to make improved causal inferences about the effectiveness of cognitive remediation therapy on cognition among patients with schizophrenia.

Speaker bio

Dr. Miles is an assistant professor in the Department of Biostatistics at the Columbia University Mailman School of Public Health. He works on developing semiparametric methods for causal inference and applying them to problems in medicine and public health. His applied work is largely in HIV/AIDS, mental health, and anesthesiology. His current methodological research interests include causal inference, its intersection with machine learning, mediation analysis, interference, and measurement error.