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 – Spring 2023

Friday, April 28, 2023 at 10:00 am

David L. Rosen, BS Pharm., JD
Partner, Foley & Lardner LLP

Abstract: This session will provide you with an understanding of what a biosimilar product is and how it compares to the reference biologic product.  The session will include a review of how a company establishes that the proposed biosimilar is highly similar to the reference product.  In addition, the session will provide an overview of how FDA has handled interchangeability of biosimilar insulin products for the reference product.  The session will also review how someone can find information about biosimilar and reference products in FDA’s Purple Book as well as information on small molecules in FDA’s Orange Book.

Location: Online via Zoom

Friday, April 14, 2023 at 10:00 am

Ronald L. Wasserstein, Ph.D.
Executive Director, American Statistical Association (ASA)

Abstract: For nearly a hundred years, the concept of “statistical significance” has been fundamental to statistics and to science. And for nearly that long, it has been controversial and misused as well. In a completely non-technical (and generally humorous) way, ASA Executive Director Ron Wasserstein will explain this controversy, and say why he and others have called for an end to the use of statistical significance as means of determining the worth of scientific results. He will talk about why this change is so hard for the scientific community to make, but why it is good for science and for statistics, and will point to alternate approaches.

Location: Warwick Evans Conference Room, Building D

Friday, March 24, 2023 at 10:00 am

James O’Malley, Ph.D.
Peggy Y. Thomson Professorship in the Evaluative Clinical Sciences, Department of Biomedical Data Science, The Dartmouth Institute of Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth

Abstract: Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of “referral paths” — sequences of patient-specific temporally linked physician visits — in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this talk we first introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals and association with a hospital-level outcome. In the second part of the talk, we use the findings from the first part to construct a shared-patient network of 10,661 physicians who delivered care to Medicare patients in Ohio that we examine for associations with the physicians’ risky prescribing behaviors. Risky-prescribing is the excessive or inappropriate prescription of drugs (e.g., Opioids) that singly or in combination pose significant risks of adverse health outcomes. This enables the novel decomposition of peer-effects of risky-prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer-effects for contagion in risky-prescribing behavior. Estimated peer-associations were strongest when the patient-sharing relationship was mutual as opposed to directional. Using simulations, we confirmed that our modeling and estimation strategies accurately and precisely estimate each type of peer-effect (mutual and directional). We also show that failing to account for these distinct mechanisms, a form of model misspecification, produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared-patient networks. 

Location: Online via Zoom

Friday, February 10, 2023 at 10:00 am

Yi Zhao, Ph.D.
Assistant Professor, Department of Biostatistics and Health Data Science, Indiana University School of Medicine

Abstract: In this study, we consider the problem of regressing covariance matrices on covariates of interest. The goal is to use covariates to explain variation in covariance matrices across units. Building upon our previous work, the Covariate Assisted Principal (CAP) regression, an optimization-based method for identifying components associated with the covariates using a generalized linear model, two approaches for high-dimensional covariance matrix outcomes will be discussed.Our studies are motivated by resting-state functional magnetic resonance imaging (fMRI) studies. In the studies, resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. Our work introduces modeling approaches that regress whole-brain functional connectivity on covariates and enable identification of brain sub-networks. The first approach identifies subnetworks that are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the CAP regression. The second approach directly performs generalized linear regression by introducing a well-conditioned linear shrinkage estimator of the high-dimensional covariance matrix outcomes, where the shrinkage coefficients are proposed to be common across matrices. The superior performance of the proposed approaches over existing methods are illustrated through simulation studies and resting-state fMRI data applications.

Location: Online via Zoom

Friday, January 27, 2023 at 10:00 am

Gary Cline, Ph.D. & Lan-Feng Tsai, M.S.
Early Biometrics & Statistical Innovation​ Data Science & Artificial Intelligence, R&D​ AstraZeneca, Gaithersburg, US

Abstract: Drug development is a complex and expensive scientific endeavor. Statisticians play a unique role in the pharmaceutical industry with their quantitative training. Their work impacts business decisions that drive the success of a drug development. They are involved in all stages of a clinical trial, from trial design, protocol writing, data collection, to data analysis and results interpretation. Their contributions do not stop there. They play a pivotal role in the entire clinical development program and its lifecycle management. They have the opportunities to innovate clinical trials, to advance science, and to create new drugs that benefit millions of patients. In this presentation, we will provide an overview of how statisticians contribute to the drug development and what they do in their daily life from an early phase biometrics point of view. Particularly, we will cover some of the exciting innovations that we are working on at AstraZeneca.

Location: Building D, Warwick Evans Conference Room

Friday, January 13, 2023 at 10:00 am

Li C. Cheung, Ph.D.
Stadtman Investigator, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NCI

Abstract: To address the appropriate management of individuals under an ever-changing screening landscape (i.e., the introduction of new screening technology, electronic health records providing greater access to patient histories, and HPV vaccination), representatives from 19 professional organizations agreed to change from issuing recommendations based on test results to recommendations based on precancer risk and a pre-agreed set of clinical action risk thresholds. Using electronic-health records from nearly 2 million women undergoing routine screening from 2003 to 2017, we estimated precancer risk for combinations of screening test results and relevant past histories. Because there can be precancers prevalent at the initial screen and precancer status is intermittently observed, resulting in left-, interval-, and right-censored time of precancer onset, we fit the data using prevalence-incidence mixture models (i.e., jointly estimated logistic regression and proportional hazards models). To inform the consensus risk thresholds, we provided to the working groups estimates of delayed diagnosis vs. colposcopic efficiency trade-offs. The new risk-based management recommendations were then externally validated using data from 2 trials, the New Mexico HPV-precancer registry, and a CDC program that provided screening for underinsured and uninsured individuals.

Location: Online via Zoom

Seminar Schedule – Fall 2022

Friday, November 11, 2022 at 10:00 am

Robert Lund, Ph.D.
Professor and Chair, Department of Statistics, University of California, Santa Cruz

Abstract: This talk introduces changepoint issues in time-ordered data sequences and discusses their uses in resolving climate problems.  An asymptotic description of the single mean shift changepoint case is first given.  Next, a penalized likelihood method is developed for the multiple changepoint case from minimum description length information theory principles.  Optimizing the objective function yields estimates of the changepoint numbers and location time(s). The audience is then walked through an example of a climate precipitation homogenization. The talk closes by addressing the climate hurricane controversy: are North Atlantic Basin hurricanes becoming more numerous and/or stronger? 

Location: Online via Zoom

Friday, October 28, 2022 at 10:00 am

Aaron C. Courville, Ph.D.
Associate Professor, Department of Computer Science and Operations Research, Université de Montréal, Canada

Abstract: In this presentation, I’ll talk about recent work in my group that looked into a strange finding:  when training neural networks, periodically resetting some or all parameters can be helpful in promoting better solutions.  Beginning with a discussion of our findings in supervised learning where we relate this strategy to Iterated Learning, a method for promoting compositionality in emergent languages. We then show how parameter resets appear to offset a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. We apply this reset mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains and observe consistently improving performance. I conclude with some recent finding and speculation about the underlying causes behind the observed effects of parameter resets. 

Location: Online via Zoom

Friday, October 14, 2022 at 10:00 am

Deepak Parashar, Ph.D.
Associate Professor, University of Warwick, England, UK

Abstract: Current precision medicine of cancer matches therapies to patients based on average molecular properties of the tumour, resulting in significant patient benefit. However, despite the success of this approach, resistance to drugs develops leading to variability in the duration of response. The approach is based on static molecular patterns observed at diagnosis whereas cancers are constantly evolving. We, therefore, focus on Dynamic Precision Medicine, an evolutionary-guided precision medicine strategy that explicitly considers intra-tumour heterogeneity and subclonal evolution and plans ahead in order to delay or prevent resistance. Clinical validation of such an evolutionary strategy poses challenges and requires bespoke development of clinical trial designs. In this talk, I will present preliminary results on the construction of such trial designs. The work is a joint collaboration with Georgetown (Robert Beckman, Matthew McCoy).

Location: Online via Zoom

Friday, September 23, 2022 at 10:00 am

Junrui Di, Ph.D. 
Associate Director, Early Clinical Development Digital Sciences & Translational Imaging Quantitative Sciences, Pfizer Inc. 

Abstract: In recent years, wearable and digital devices are gaining popularity in clinical trials and public health research due to technology advances, thanks to their reduced size, prolonged battery life, larger storage, and faster data transmission. In public health, countless research has been conducted to better understand the complex signals collected by wearable and digital devices and their relationship with human health. In clinical trials, emphasis has been given to the derivation and validation of novel digital endpoints and proper statistical methods to model such digital endpoints to objectively quantify disease progression and treatment effects. Such devices provide us a unique opportunity to reconsider the way data can be collected, from snapshot in-clinic measurements to continuous recording of behaviors of daily life. This presentation will highlight some representative examples of statistical and machine learning methodologies and applications of wearable and digital devices in health research.

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.