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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 10/26/2017 3:30PM - 5:00PM
Biostatistics Seminar: Ying Huang, Fred Hutchinson Cancer Research Center
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Ying Huang, Fred Hutchinson Cancer Research Center, will present, "Inferential Procedures for the Use of Biomarkers in Treatment Selection".

Thu 11/2/2017 3:30PM - 5:00PM
Biostatistics Seminar: Dong-Yun Kim, Georgetown University
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Dong-Yun Kim, PhD, Adjunct Associate Professor, Georgetown University, will present, "Continuous Monitoring of Patient Accrual in Multi-center Clinical Trials".
Thu 11/9/2017 3:30PM - 5:00PM
Biostatistics Seminar: Wen-Chi Wu, Merck
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Wen-Chi Wu 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

Biostatistics Seminar: David Choi - Carnegie Mellon University

Thursday 9/28 3:30PM - 5:00PM
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, David Choi, Carnegie Mellon University, will present, "Estimation of Monotone Treatment Effects in Network Experiments".

Randomized experiments on social networks pose statistical challenges, due to the possibility of interference (such as peer influence) between units. To find rigorous confidence intervals on the average treatment effect in such settings, one typically must model the underlying social network --  "who can influence whom", how such effects might combine, and whether they can cascade over long distances. In many settings, this may be an unreasonable modeling burden. As an alternative, we propose new methods for finding confidence intervals on the attributable treatment effect. These methods do not make assumptions on the structure of the underlying social network, but instead require an identifying assumption that is similar to requiring nonnegative treatment effects; for example, assuming that a vaccine does not increase the risk of catching a disease, either directly or indirectly through vaccinated peers. Network or spatial information can be used to customize the test statistic; in principle, this can increase power to detect spillovers without making further assumptions on the data generating process.

Last Updated On Tuesday, August 29, 2017 by Haydo, Amber LC
Created On Wednesday, August 09, 2017


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

Bernadette Kapko

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