Current Bio3 Seminar Series

The Bio3 Seminar Series meets every second and fourth Friday of the month, during the academic year. Refreshments are served 15 minutes prior to the seminar’s starting time.

*Students are expected to attend the bi-weekly seminar series*

Seminar Schedule – Fall 2019:

Friday, September 13, 2019 at 10:00 am

Xiaochun Li, Ph.D.
Associate Professor, Department of Biostatistics, School of Medicine, Fairbanks School of Public Health, University of Indiana

Title: A Machine Learning Approach to Causal Inference in the Presence of Missing Data

Abstract: Observational medical databases increasingly find uses for comparative effectiveness and safety research. However, the lack of analytic methods that simultaneously handle the issues of missing data and confounding bias along with the onus of model specification, limit the use of these valuable data sources. We derived a novel machine-learning approach based on trees to estimate the average treatment effect. In order to evaluate causal estimation by model-free machine-learning methods in data with incomplete observations, we conducted a simulation study with data generated from known models of exposure, outcome and missing mechanisms. Thus the true causal effect was known and used as the benchmark for evaluations. Two settings were studied. We compared the bias and standard error of causal estimates from our method to a multiply robust parametric method, the complete case analysis (CC) and a regression analysis after multiple imputations (MI). The proposed methods were applied to a real observational data set of implantable cardioverter defibrillator use.

Location: Proctor Harvey Amphitheater, Med-Dent Building C104
3900 Reservoir Rd. NW, Washington, DC 20057

Friday, September 27, 2019 at 1:00 pm

Ming T. Tan, Ph.D.
Chair and Professor, Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University Medical Center

Title: Statistics and Data Science in Biomedicine I: Discovery, Design and Analysis of Multi-drug Combinations: from Experiments to Clinical Trials

Abstract: This seminar presents applications of statistics and data science in the design and analysis of biomedical research studies including drug combinations, adaptive and/or targeted clinical trials designs, statistical learning and predictive modeling in head and neck cancer with a series of validation in three large randomized studies, and search for patient subgroups in precision medicine. Part I will focus on drug combinations which are the hallmark of therapies for complex diseases such as cancer. A statistical approach for evaluating the joint effect of the combination is necessary because even in vitro experiments often demonstrate significant variation in dose-effect. I will present a novel methodology for efficient design and analysis of preclinical drug combination studies with applications in the combination of Vorinostat and Ara-C and etoposide. Further discussion will be on the utilization of the preclinical data to guide early phase clinical trial design and a novel adaptive phase I design for drug combinations.

Location: Proctor Harvey Amphitheater, Med-Dent Building C104
3900 Reservoir Rd. NW, Washington, DC 20057

Friday, October 11, 2019 at 10:00 am

Robert Makuch, Ph.D.
Professor, Department of Biostatistics, School of Public Health, Yale University

Title: Treatment Effects Assessment in Multi-Regional Clinical Trials (MRCTs) Using Bayesian Methods

Abstract:  Multi-regional clinical trials (MRCTs) help to synchronize drug development globally, whereby the time lag in marketing authorization among different countries is minimized. However, there are statistical concerns associated with analysis and interpretation of MRCTs. The results of certain countries/regions could vary significantly from the overall results. In this case, controversy exists regarding the extent to which country-specific result should be minimized/ignored and medical scientists/regulators should defer to the overall global treatment effect. Rather than analyzing data separately in each region, our discussion today focuses on developing a Bayesian framework for assessing heterogeneity of regional treatment effects that leverages data collected in other regions. The goal is to make scientifically valid judgments about the play of chance versus real regional differences when comparing results to the overall trial outcome.

Location: New Research Building Auditorium
3970 Reservoir Rd. NW, Washington, DC 20057

Friday, October 25, 2019 at 10:00 am

Kellie J. Archer, Ph.D.
Professor and Chair, Division of Biostatistics, College of Public Health, Ohio State University

Title: Algorithmic and Statistical Methods for High-Dimensional Variable Selection

Abstract: Pathological evaluations are frequently reported on an ordinal scale. Moreover, diseases may progress from less to more advanced stages. For example, HCV infection progresses from normal liver tissue to cirrhosis, dysplastic nodules, and potentially to hepatocellular carcinoma. To elucidate molecular mechanisms associated with disease progression, genomic characteristics from samples procured from these different tissue types are often assayed using a high-throughput platform. Such assays result in a high-dimensional feature space where the number of predictors (P) greatly exceeds the available sample size (N). In this talk, various approaches to modeling an ordinal response when P>N will be described including algorithmic and statistical methods.

Location: New Research Building Auditorium
3970 Reservoir Rd. NW, Washington, DC 20057

Friday, November 8, 2019 at 10:00 am

Sergeant Jannie M. Clipp
Georgetown University Police Department


Abstract: Based on best practices from federal law enforcement officials, this training program is designed to increase awareness of the “run, hide, fight” protocols in case of an active shooter or violent incident. The course, which uses the “See Something, Say Something” concept, as well as detailed steps in an active shooter incident, is taught by the Georgetown University Police Department (GUPD).

Location: New Research Building Auditorium
3970 Reservoir Rd. NW, Washington, DC 20057

Thursday, November 14, 2019 at 1:00 pm

Tai Xie, Ph.D.
CEO & Founder, Brightech International & CIMS Global

Title: Design and Monitor Clinical Trials with a Dynamic Adaptive System

Abstract: Modern clinical trials need to be designed with much flexibility and efficiency. Flexibility includes adaptation based on new information while trial is ongoing and being monitored dynamically with cumulative data. Efficiency includes adequate study power and timely decision making regarding the future course of the trial. Both aspects require the protection of trial integrity and validity. Adaptive sequential designs have been proposed for clinical trials for the last decades. However, traditional computing environment could not accommodate trial monitoring at any time in a timely fashion. In this high-speed, AI – (artificial intelligence) – everywhere era, we introduce Dynamic Data Monitoring concept in which the cumulative data are monitored continuously, and the treatment effect is examined timely over the trial duration. Changing frequency and schedules of the interim analyses can be monitored at real time. The accumulating data can be viewed whenever new data is available and the timing of efficacy and futility assessments and sample size adaptation is made very flexibly, and the type I error rate is always controlled. Numerical and real study examples and simulations are presented. (Collaborated with Drs. Gordon Lan, Joe Shih, and Peng Zhang)

Location: Pre-Clinical Science Building, LA4
3900 Reservoir Rd. NW, Washington, DC 20057

Friday, November 22, 2019 at 10:00 am

Junyong Park, Ph.D.
Associate Professor, Department of Mathematics & Statistics, University of Maryland, Baltimore County

Title: Estimation of Species Richness and Rarefaction Curve using Nonparametric Empirical Bayes by Quadratic Optimization

Abstract: We have proposed new algorithms to estimate the rarefaction curve and accordingly the number of species. The key idea in developing the algorithms is based on an interpolated rarefaction curve through quadratic optimization with linear constraints. Our proposed algorithms are easily implemented and show better performances than existing methods in terms of computational speed and accuracy. We also provide a criterion of model selection to choose tuning parameters and the idea of confidence interval. A broad range of numerical studies including simulations and real date examples are also conducted, and the gain that it produces has been compared to existing methods.

Location: Proctor Harvey Amphitheater, Med-Dent Building C104
3900 Reservoir Rd. NW, Washington, DC 20057

The Bio3 Seminar Series are for educational purposes and intended for the faculty, students and staff of Georgetown University.  The seminars are not recorded and closed to the public.