YSPH Biostatistics Seminar: “Addressing the Replicability and Generalizability of Clinical Prediction Models”
Naim Rashid, PhD
Associate Professor, Department of Biostatistics
University of North Carolina at Chapel Hill
September 7, 2021
YSPH Biostatistics Virtual Seminar: "Telehealth Use to Support Management of Anxiety and Depression Among African American Women"
Terika McCall, PhD, MPH, MBA
Center for Medical Informatics
Yale School of Medicine
Jessica Young, Ph.D
Department of Population Medicine
Harvard Medical School
March 23, 2021
Matthew Stephens, PhD, Ralph W. Gerard Professor Department of Statistics, Human Genetics and the College
The University of Chicago
March 2, 2021
Xihong Lin, PhD
Professor, Department of Biostatistics
Harvard T.H. Chan School of Public Health
Tuesday, February 23, 2021
Jingshu Wang, Ph.D.
Department of Statistics and the College
The University of Chicago
Tuesday, February 9, 2021
Eugene Katsevich, Ph.D.
Assistant Professor in the Department of Statistics
The Wharton School at the University of Pennsylvania
YSPH Biostatistics Seminar: “Marginal Structural Models for Causal Inference with Continuous-Time Treatment and Censored Survival Outcomes"
Liangyuan Hu, PhD, Assistant Professor - Department of Population Health Science and Policy
Icahn School of Medicine at Mount Sinai
December 8, 2020
Biostatistics Seminar - November 17, 2020
Elizabeth Tipton, PhD
Associate Professor, Department of Statistics, Northwestern University
YSPH Biostatistics Virtual Seminar: “Optimal Doubly Robust Estimation of Heterogeneous Causal Effects"
Edward Kennedy, PhD
Assistant Professor, Department of Statistics & Data Science, Carnegie Mellon University
Abstract: Heterogeneous effect estimation plays a crucial role in causal inference, with applications across medicine and social science. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years, but there are important theoretical gaps in understanding if and when such methods are optimal. This is especially true when the CATE has nontrivial structure (e.g., smoothness or sparsity). Our work contributes in several main ways. First, we study a two-stage doubly robust CATE estimator and give a generic model-free error bound, which, despite its generality, yields sharper results than those in the current literature. We apply the bound to derive error rates in nonparametric models with smoothness or sparsity, and give sufficient conditions for oracle efficiency. Underlying our error bound is a general oracle inequality for regression with estimated or imputed outcomes, which is of independent interest; this is the second main contribution. The third contribution is aimed at understanding the fundamental statistical limits of CATE estimation. To that end, we propose and study a local polynomial adaptation of double-residual regression. We show that this estimator can be oracle efficient under even weaker conditions, if used with a specialized form of sample splitting and careful choices of tuning parameters. These are the weakest conditions currently found in the literature, and we conjecture that they are minimal in a minimax sense. We go on to give error bounds in the non-trivial regime where oracle rates cannot be achieved. Some finite-sample properties are explored with simulations.
David Benseker, PhD, MPH, Assistant Professor
Emory University Department of Biostatistics and Bioinformatics.
October 27, 2020
Abstract: One of the best hopes we have of returning life to normal is to bring a safe and effective preventive COVID-19 vaccine to market and make it available to just about everybody around the world. We are in the middle of an unprecedented effort to bring such a vaccine to market. After a recent pressure campaign led by academic scientists, vaccine developers have made public the protocols for their Phase III trials. The release of these protocols has ignited a fierce debate as to whether the designs are appropriate and sufficiently safe-guarded from political pressure for vaccine approval. In this talk I will discuss a few of the complex issues involved in designing Phase III COVID vaccine trials, touching on some key statistical aspects of the trials along the way.
Department of Mathematical and Statistical Sciences
University of Alberta
October 13, 2020
BIS Seminar: Dealing with observed and observed effect moderators wehn estimating population average treatment effects
Associate Dean for Education
Bloomberg PRofessor of American Health
September 22, 2020
Qingyuan Zhao, University of Cambridge
Biostatistics Seminar: BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
Qingyuan Zhao, Statistical Laboratory, University of Cambridge
May 5, 2020
BIS Seminar - 6.23.2020 - Model-averaged estimation of molecular evolution and natural selection in SARS-COV-1 and SARS-CoV-2 coronaviruses during zoonosis
Jeffrey Townsend, PhD
Elihu Professor of Biostatistics and Professor of Ecology and Evolutionary Biology
Biostatistics Seminar - 6.9.2020
Frank Harrel, Professor of Biostatistics
Vanderbilt University School of Medicine
Associate Professor of Biostatistics, Statistics & Data Science, Operation, and Ecology & Evolutionary Biology
5.25.2020 Biostatistics Seminar
Dan Weinberger, PhD, associate professor of Epidemiology (Microbial Diseases), Yale School of Public Health April 2020 Seminar