Current Bio3 Seminar Series

The Bio3 Seminar Series meets every second and fourth Friday of the month, during the academic year.

*MS and PhD biostatistics students are expected to attend the bi-weekly seminar series, as part of their academic curriculum.*

Seminar Schedule – Fall 2023

Friday, November 10, 2023 at 10:00 am

Molin Wang, Ph.D.
Associate Professor, Department of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health/ Harvard Medical School, Harvard University

Abstract: Exposure measurement error is a common occurrence in various epidemiological fields, with radiation epidemiology at the top of the list. Failure to properly assess and adjust for uncertainties in radiation dosimetry could lead to biased effect estimates. Moreover, characterizations of health impacts obtained without countering error in exposure levels could potentially misinform policy makers, when they are, for example, setting the radiation safety levels in occupational and residential settings referencing unadjusted dose-response relationships between error-prone radiation levels and observed adverse health outcomes. Therefore, from both the statistical advancement and public health policy perspectives, it is of great importance to develop and discuss statistical methods in countering the influences of such exposure measurement error and providing valid health outcome effects into the policy decision pipeline. In this talk, I will present statistical methods for estimating exposure-outcome associations adjusting for the exposure measurement errors, when the exposure takes the form of a cumulative total. The proposed methods will be illustrated using data from the field of radiational epidemiology. 

Location: Online via Zoom

Friday, October 27, 2023 at 10:00 am

Daniel Almirall, Ph.D.
Associate Professor, Institute for Social Research, Department of Statistics, University of Michigan

Abstract: Evidence-based practices often fail to be implemented or sustained due to barriers at multiple levels of an organization (e.g., system-level, practitioner-level). A growing cadre of implementation strategies can help mitigate challenges at these multiple levels, but significant heterogeneity exists in whether, and to what extent, organizations—and the practitioners who deliver treatment within them—respond to different strategies. However, it is impractical to provide all (or even most) of these strategies to all levels, at all times. This suggests the need for an approach that sequences and adapts the provision of implementation strategies to the changing context and needs of practitioners within the multiple levels of an organization. A multilevel adaptive implementation strategy (MAISY) offers a replicable, approach to precision implementation that guides implementers in how best to adapt and re-adapt (e.g., augment, intensify, switch) implementation strategies based on the changing context and changing needs at multiple levels.

Location: Online via Zoom

Friday, October 13, 2023 at 10:00 am

Ding-Geng (Din) Chen, Ph.D.
Executive Director and Professor in Biostatistics, College of Health Solutions, Arizona State University, SARCHI Research Professor in Biostatistics, Department of Statistics, University of Pretoria, South Africa

Abstract: Statistical meta-analysis (MA) is a common statistical approach in big data inference to combine meta-data from diverse studies to reach a more reliable and efficient conclusion. It can be performed by either synthesizing study-level summary statistics (MA-SS) or modeling individual participant-level data (MA-IPD), if available. However, it remains not fully understood whether the use of MA-IPD indeed gains additional efficiency over MA-SS. In this talk, we review the classical fixed-effects and random-effects meta-analyses, and further discuss the relative efficiency between MA-SS and MA-IPD under a general likelihood inference setting. We show theoretically that there is no gain of efficiency asymptotically by analyzing MA-IPD. Our findings are further confirmed by extensive Monte-Carlo simulation studies and real data analyses.  

*This talk is based on the joint publication: Chen, D.G, Liu, D., Min, X. and Zhang H. (2020).  Relative efficiency of using summary and individual information in random-effects meta-analysis. Biometrics, 76(4): 119-1329. (

Location: Online via Zoom

Friday, September 22, 2023 at 10:00 am

Bret Musser, Ph.D.
Executive Director, Head of Biostatistics, Biostatistics & Data Management, Regeneron

Abstract: The increasing complexity of clinical research objectives has fueled the demand for the next-generation clinical trials with more effective designs and analysis strategies. For example, medicines such as gene therapies have features vastly different from those of typical drugs, which presents a new challenge for statisticians in designing their dose-finding, proofs of concept, and confirmatory studies. In addition, regulatory agencies have been promoting complex and innovative designs for the industry, such as clinical trials for rare diseases, leveraging real-world data and real-world evidence in clinical trials, clinical trial with master protocols, and many more. These opportunities for developing next-generation clinical trials also come with unprecedented statistical difficulties. To conquer such difficulties and fully unlock the potential of next-generation clinical trials, a strong partnership between industry and academic stakeholders are needed. In this presentation, we will examine the potential of the Regeneron-Georgetown partnership in modernizing the clinical studies industry are conducting.

Location: Building D, Warwick Evans Conference Room

Friday, September 8, 2023 at 10:00 am

Shelby Haberman, Ph.D.
Educator, Statistician

Abstract: Measures of agreement are compared to measures of prediction accuracy. Differences in appropriate use are emphasized, and approaches are examined for both numerical and nominal variables. General estimation methods are developed, and their large-sample properties are compared.

Location: Online via Zoom

The Bio3 Seminar Series are for educational purposes and intended for members of the Georgetown University community.  The seminars are closed to the public.