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 – Spring 2020:

Friday, January 10, 2020 at 10:00 am

Edsel Pena, Ph.D.
Professor, Department of Statistics, University of South Carolina

Title: The Search for Truth through Data: Fisher, Neyman & Pearson, Bayes, P-Values, and Knowledge Updating

Abstract:  The talk will concern the role of statistical thinking in the Search for Truth using data. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and Biostatistics and has become a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, a psychology journal has gone to the extreme of banning the use of P-values in articles appearing in its journal, and a special issue of The American Statistician this year was fully devoted to this issue. The concerns about P-values has also been in relation to important issues of reproducibility and replicability in scientific research. This issue goes to the core of inductive inference and the different schools of thought (Fisher’s null hypothesis significance testing (NHST) approach, Neyman-Pearson paradigm, Bayesian approach, etc.) on how inductive inference (that is, the process of discovering the Truth through sample data) should be done. Some new perspectives on the use of P-values and on the search for truth through data will be discussed. In particular, we will discuss the representation of knowledge and its updating based on observations, and ask the question: “When given the P-value, what does it provide in the context of the updated knowledge of the phenomenon under consideration, and what additional information should accompany it?” This talk is based on the preprint in the Math ArXiV. (http://arxiv.org/abs/1910.05486 (new window))

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, January 24, 2020 at 10:00 am

Tiemen Woutersen, Ph.D.
Professor, Department of Economics, Eller College of Management, University of Arizona

Title: Confidence Sets for Continuous and Discontinuous Functions of Parameters

Abstract: Applied researchers often need to construct confidence sets for policy effects based on functions of estimated parameters, and often these functions are such that the delta method cannot be used. In this case, researchers currently use one of two bootstrap procedures, but we show that these procedures can produce confidence sets that are too small. We provide two alternatives, both of which produce consistent confidence sets under reasonable assumptions that are likely to be met in empirical work. Our second, more efficient method produces consistent confidence sets in cases when the delta method cannot be used, but is asymptotically equivalent to the delta method if the use of the latter is valid. Finally, we show that our second procedure works well when we conduct counterfactual policy analyses using a dynamic model for employment.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, February 14, 2019 at 10:00 am

Chris Amos, Ph.D.
Associate Director of Quantitative Science; Director for the Institute of Clinical & Translational Medicine, Baylor College of Medicine

Title: Understanding Complex Etiology of Diseases by Application of Machine Learning Tools

Abstract: Genetic analyses have identified etiological factors for several complex disease but understanding how genetic factors interact to increase risk is challenging. In this talk I describe the development of novel methods for large-scale identification of population-level attributes that may affect genome-wide association studies. Because studies we conduct are planned for hundreds of thousands of samples, we had to develop machine-learning based procedures that could be generalized for identifying ethnic variability. We also evaluated tools for jointly estimating the effects of multiple genetic factors by using machine learning tools including random forests and classification trees. These particular analytical schemes identified novel interactions among alleles at multiple loci influencing risk for a rare autoimmune disease. We are now studying the efficacy of modeling with classification tree based analysis versus more traditional approaches such as logistic regression with lasso for high dimensional SNP studies.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, February 28, 2020 at 10:00 am

Chen Hu, Ph.D.
Associate Professor, Division of Oncology, Biostatistics & Bioinformatics, School of Medicine, Johns Hopkins University

Title: Utility of Restricted Mean Survival Time Related Methods in Oncology Clinical Trials

Abstract: For oncology clinical trials, time-to-event endpoints, such as overall survival or progression-free survival, are used widely as key endpoints and of great interest. While the classic log-rank test and Cox proportional hazards model have been considered as the “default” analysis methods for statistical inference and for quantifying treatment benefit, we have witnessed many challenges and issues when they cannot be readily or properly applied. Furthermore, in cancer treatment trials we also concurrently observe disease-related longitudinal processes or multiple outcomes that are dependently censored by some “terminal” event like death, and there is increasing need and interest in developing and applying alternative and statistically valid metrics and inference tools to assess the overall disease prognosis and benefit-risk profile more efficiently and in a timely fashion. In recent years, restricted mean survival time (RMST) has gained growing interests as an important alternative metric to evaluate survival data. In this talk we will review and discuss these recent methodological developments, as well as their potential use and implications in oncology clinical trials and drug development.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, March 27, 2020 at 10:00 am

Laura Balzer, Ph.D.
Associate Professor, Department of Biostatistics & Epidemiology, School of Public Health & Health Sciences, University of Massachusetts Amherst

Title: Improving Community Health in East Africa with Causal Inference and Machine Learning

Abstract: In this talk, we highlight the use of causal inference and machine learning methods to reduce bias when assessing disease burden and to improve precision when estimating intervention effects in randomized trials. We illustrate with data from the SEARCH Study, a large (>320,000-person) cluster randomized trial for HIV prevention and improved community health in rural Kenya and Uganda (NCT01864603).

Location: Zoom only

Friday, April, 24, 2020 at 10:00 am – *Cancelled*

Location: Warwick Evans Conference Room, Building D
4000 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.