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Biostatistics Seminars

Department of Biostatistics Seminar Series

The Department of Biostatistics presents a regular speaker series each semester, generally on Thursday afternoon each week. Diverse experts lecture on their work in biostatistics.

Upcoming Biostats Seminars

Thu 9/28/2017 3:30PM - 5:00PM
Biostatistics Seminar: David Choi - Carnegie Mellon University
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, David Choi, Carnegie Mellon University, will present, "Estimation of Monotone Treatment Effects in Network Experiments".
Thu 10/5/2017 3:30PM - 4:30PM
Biostatistics Seminar: Hongtu Zhu, MD Anderson Cancer Center
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Hongtu Zhu, PhD, The University of Texas MD Anderson Cancer Center, will present, “Statistical Challenges, Opportunities, and Strategies in Large-Scale Medical Studies”.
Thu 10/26/2017 3:30PM - 5:00PM
Biostatistics Seminar: Ying Haung, Fred Hutchinson Cancer Research Center
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Ying Haung, Fred Hutchinson Cancer Research Center, will present.
Thu 11/2/2017 3:30PM - 5:00PM
Biostatistics Seminar: Dong-Yun Kim, NIH/NHLBI
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Dong-Yun Kim, NIH/NHLBI, will present.
Thu 11/9/2017 3:30PM - 5:00PM
Biostatistics Seminar: Wen-Chi Woo, Merck
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Wen-Chi Woo, Merck, will present.
Thu 11/16/2017 3:30PM - 5:00PM
Biostatistics Seminar: Michael Wallace, University of Waterloo
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Michael Wallace, University of Waterloo, will present.

Previous Biostats Seminars

Biostatistics Seminar Series

Martin Lindquist, Johns Hopkins - High-dimensional Multivariate Mediation...

Thursday 3/30 3:30PM - 4:30PM
Public Health Auditorium (G23)

Biostatistics Seminar speaker, Martin A. Lindquist, PhD, Professor, Biostatistics, Johns Hopkins University, will present, “High-dimensional Multivariate Mediation with Application to Neuroimaging Data”.

Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs). DMs linearly combine potential mediators into a smaller number of orthogonal components, with components ranked by the proportion of the LSEM likelihood (assuming normally distributed errors) each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of SNPs, or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging (fMRI) study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.

Last Updated On Friday, February 17, 2017 by Haydo, Amber LC
Created On Monday, December 19, 2016


For information on seminars and events in the department, contact:

Bernadette Kapko

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