Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
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