Biostatistics Seminars, Working Groups & Workshops.
Throughout the academic year in the department, faculty, students, and collaborators meet to discuss various research projects in progress. These meetings and working groups are great opportunities for students to sit in and participate in research discussions in the areas of clinical trials, statistical genetics, and observational studies. Students gain firsthand experience in developing research with faculty and master- and PhD-level students. Additionally, there are opportunities for students to present on a developing research area. Students are encouraged to attend and take advantage of these opportunities.
The Biostatistics Seminar Series is designed to engage faculty and students in research projects happening within our department and outside of Boston University. The purpose of the seminars is to widen and deepen participants’ knowledge of research in the field of Biostatistics and encourage collaboration in the field. We invite speakers from diverse research backgrounds to present their latest findings. Attendees are encouraged to participate in discussions and provide feedback. The seminars are currently held being held from 12:45 to 1:45pm remotely on Zoom. Interested speakers should contact Shariq Mohammed or Prasad Patil. Please include abstract(s) in your correspondence. You can also email Dr. Mohammed or Dr. Patil to be added to our seminar mailing list. (Passcode: $z?w2dfm) Click here for more information. Interested speakers should contact Gheorghe Doros or Robert Lew. Please include abstract(s) in your correspondence. You may also email Dr. Doros or Dr. Lew to be added to the Working Group mailing list. The Statistical Genetics Seminar Series is held on the 1st and 3rd Fridays of the month at 9:30am from September through May. The goal of this seminar series is to create a community of researchers doing statistical genetics research, learn cutting edge research methods, and foster collaboration and exchange of ideas. The group brainstorms together in September to lay out the yearlong topics of interest for discussion. At each session, one person, group of participants, or invited outside speaker presents (formally/informally) the material, usually pertaining to his or her area of expertise, interest, or research, and leads the discussion. Our participants include a mix of Biostatistics and Bioinformatics students in addition to faculty members involved in genetics research. Please click here for more information. Lead Faculty: Gina M. Peloso The Causal Inference Seminar Series is a collaboration between the Department of Biostatistics and the Department of Mathematics and Statistics. It meets at 1:00-2:00pm on the first Monday of each month, alternating between the Charles River Campus and Medical Campus. Guest speakers will present on different topics in causal inference. Lead Faculty: Sara Lodi and Judith Lok. PhD student dissertation presentations are held regularly throughout the academic year. They are open to the public and all students and faculty are encouraged to attend. All presentations will be in Crosstown 4th Floor, 801 Massachusetts Avenue Boston, 02118. Presentation sessions last 1 hour with two students presenting at each session. Here is the schedule. These seminars are held by other departments but may be of interest to our students. Notably they count for PhD students’ required seminar attendance. Goal & Activities Current committee members Previous committee members Future Workshops Past WorkshopsLunchtime Seminar Series
Purpose
Time and Location
Upcoming Seminars
Date
Speaker
Seminar Title
Description
Thursday May 5, 2022
Dr. Jonathan Huggins, PhD, Assistant Professor, Department of Mathematics and Statistics Boston University
A generalized Bayesian approach to robustly discovering mutational signatures in human cancer
Somatic mutations in cancer genomes are caused by mutational processes such as ultraviolet light and disrupted DNA repair mechanisms. Each mutational process causes a distinctive pattern of mutations (a “mutational signature”), which can be inferred from whole genome and whole exome sequencing data. However, as we show via simulation studies, the methods used to infer the mutational signatures are not robust: even slight perturbations to the assumed model for how mutations are generated lead to substantially incorrect inferences. Since we know the assumed model is incorrect, our results suggest current methods are likely inferring spurious processes and missing bone fide processes. We offer an alternative, more reliable method for inferring mutational signatures using generalized Bayesian inference with a data-adaptive power likelihood. We validate our approach in simulation studies and apply it to PCAWG whole-genome data. We discuss some possible biological implications of our findings.
Thursday, October 13th, 2022
Dr. Youjin Lee, PhD, Manning Assistant Professor of Biostatistics, Brown University
Policy effect evaluation under counterfactual neighborhood intervention in the presence of spillover
Policy interventions can spill over to the portions of population who are not directly exposed to the policy, but that are nonetheless close to the units directly affected. Such spillover effects of a policy intervention on neighboring regions have been recently acknowledged, where one of the target estimands is the average treatment effect on the particular observed population. Our research question moves a step further by asking what policy consequences would the treated units experience if their surrounding neighbors were also directly affected by the policy intervention. When we only observe the treated unit(s) surrounded by the controls — as is common when a policy intervention is only effective for a single city or state — this effect inquires about the policy effects under a counterfactual neighborhood policy status that we do not, in actuality, observe. In this work, we extend difference-in-differences (DiD) approaches to spillover settings and develop the identification conditions required to evaluate policy effects in counterfactual treatment scenarios. These causal quantities are policy-relevant for designing effective policies for populations subject to various neighborhood statuses. Through extensive numerical experiments, we examine the performance of our doubly-robust estimator under heterogeneous spillover effects. We finally apply our proposed method to the Philadelphia beverage tax data to investigate the effect of the tax implementation on Philadelphia.
Thursday, November 10th, 2022
Dr. Peter James, Associate Professor, Harvard Medical School and Harvard Pilgrim Health Care Institute, Harvard TN Chan School of Public Health
Novel Approaches to Spatial Factors and Health: Incorporating Smartphone Applications and Google Street View into Epidemiology
The places in which we live, work and play influence our health behaviors, our mental health, and our chronic disease risk. However, the majority of research on spatial factors and health has relied on residential addresses to assign exposure, questionnaire data to measure health behaviors and health outcomes, and coarse and nonspecific indices to estimate exposure to spatial factors. Recent technological advances have provided opportunities to overcome these limitations. Mobile health technologies—including GPS-enabled smartphones and consumer wearables like Fitbit—have opened new doorways to track personalized exposure and granular data on mental health and health behaviors from minute to minute. Deep learning algorithms applied to Google Street View images enable us to estimate exposure-specific features of the built and natural environment that might impact health. In this talk, Dr. James will speak about his experience integrating mobile health technology and Google Street View imagery into several cohorts based at Harvard, including the Nurses’ Health Study 3 (NHS3) and Project Viva.
Clinical Trials Working Group
Statistical Genetics Seminar Series
Date
Speaker
Seminar Title
Friday, Oct. 01, 2021
Xiaoling Zhang, BU Genetics, Molecular Medicine
Mapping the Genetic Architecture of Alzheimer’s Disease and Integration with Functional Genomics
Friday, Nov. 05, 2021
Frank Wendt, Yale/VA (in-person)
Modeling the longitudinal changes of ancestry diversity in the MVP cohort: systemic comparison of methods
Friday, Nov. 19, 2021
Xianbang Sun, BUSPH Biostatistics
Analysis of mtDNA heteroplastic mutation: methods and application
Friday, Dec. 03, 2021
Ningyuan Wang, BUSPH Biostatistics
Discussion of colocalization methods
Friday, Dec. 17, 2021
Sophie Gunn, BUSPH Biostatistics
Discussion of Behavioral Genetics
Friday, Jan. 21, 2022
Stephen Burgess, University of Cambridge (virtual)
Mendelian randomization: genetic epidemiology for causal inference and drug development
Friday, Feb. 04, 2022
TBD
TBD
Friday, Feb. 18, 2022
Gyungah Jun, BU Biomedical Genetics
Big data driven drug discovery
Friday, Mar. 04, 2022
Anubha Mahajan, Genentech (virtual)
TBD
Friday, Mar. 18, 2022
Sarah Urbut, BWH
Bayesian multivariate GWAS implications
Friday, Apr. 01, 2022
TBD
TBD
Friday, Apr. 15, 2022
TBD
TBD
Friday, May 06, 2022
TBD
TBD
Causal Inference Seminar Series
Doctoral Student Presentations
Other Seminars of Interest
Biostatistics Career Development Workshop