Biostatistics Seminars and Working Groups

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.

 

Purpose

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. Lunch will be served.

Time and Location

The seminars are held from 12:45 to 1:45pm at 801 Massachusetts Avenue, Crosstown Building, Room 305.

Upcoming Seminars

Interested speakers should contact Chanmin Kim, PhD. Please include abstract(s) in your correspondence. You can also email Dr. Kim to be added to our seminar mailing list.

Date
Speaker
Seminar Title
Description
Thursday, Sep. 14, 2017
Bethany Hedt-Gauthier, Assistant Professor of Global Health and Social Medicine at Harvard Medical School
Biostatistics in Global Health: Approaches, Challenges, and Methodological Needs
Dr. Bethany Hedt-Gauthier is a biostatistician and Assistant Professor in the Department of Global Health and Social Medicine at Harvard Medical School. Her work over the last 15 years has focused on health disparities, particularly in sub-Saharan Africa, with eight years resident in Namibia, Malawi and Rwanda. In her talk, she will present  methods and results for her most recent work with a focus on surgical care in rural sub-Saharan Africa. She will discuss some of the challenges to the use of rigorous statistical methods in global health research and opportunities for individuals and departments to address these gaps.
Thursday, Oct. 5, 2017
Laura White, Associate Professor of Biostatistics at Boston University School of Public Health
Estimating Tuberculosis Transmission and Incidence
Tuberculosis (TB) is the leading cause of infectious disease death globally, yet our limited ability to track TB transmission and incidence is hindering our response. In this talk, I will discuss methods that are used to monitor and track infectious diseases. These approaches make use of routinely collected surveillance data and are intended to identify groups and locations that are most responsible for transmission. I will then discuss how these approaches might be applied to TB and our current work and future plans in this area.
Thursday, Nov. 9, 2017
Joseph Hogan, Professor of Biostatistics and Director of the Biostatistics Graduate Program at Brown University
Using electronic health records to model engagement and retention in HIV care
The HIV care cascade is a conceptual model describing the stages of care leading to long-term viral suppression of those with HIV. Distinct stages include case identification, linkage to care, initiation of antiviral treatment, and eventual viral suppression. After entering care, individuals are subject to disengagement from care, dropout, and mortality. Owing to the complexity of the cascade, evaluation of efficacy and cost effectiveness of specific policies has primarily relied on simulation-based approaches of mathematical models, where model parameters may be informed by multiple data sources that come from different populations or samples.  The growing availability of electronic health records and large-scale cohort data on HIV-infected individuals presents an opportunity for a more unified, data-driven approach using statistical models. We describe a statistical framework based on multi-state models that can be used for regression analysis, prediction and causal inferences. We illustrate using data from a large HIV care program in Kenya, focusing on comparisons between statistical and mathematical modeling approaches for inferring causal effects about treatment policies.
Thursday, Feb. 8, 2018
Hyonho Chun, Associate Professor of Mathematics and Statistics at Boston University
Nonlinearity and outliers in high-dimensional complex biological data

Modern high-throughput technologies provide information about high dimensional features in biomedical research.  These biological entities are often related to each other.   When characterized well, an inferred network can lead to useful insights to researchers.  However, a graph/network estimation problem becomes challenging since dependence can be nonlinear as well as data contains outliers.  In this talk, I will introduce recent network estimation approaches that address these challenges by modeling conditional medians via various non-parametric approaches.

Thursday, Mar. 8, 2018
Chris Gill, Associate Professor of Global Health, BUSPH
Resurgence of pertussis in the era of acellular pertussis vaccines
The incidence of Whooping Cough in the US has been rising slowly since the 1970s, but the pace of this has accelerated sharply since newer acellular pertussis vaccines replaced the earlier whole cell vaccines.   A similar trend occurred in many other countries following the switch to acellular vaccine, including the UK, Canada, Australia, Ireland and Spain.  The key question is why?  Two leading theories (short duration of persistence and evolutionary shifts in the pathogen to evade the vaccine) explain some but not all of these shifts, suggesting that other factors may also be important.  In this synthesis, we argue that sterilizing mucosal immunity that blocks nasopharyngeal carriage of B. pertussis and impedes person-to-person transmission (including between asymptomatically infected individuals) is a critical factor in this dynamic.  Moreover, we argue that the ability to induce such mucosal immunity is fundamentally what distinguishes whole cell and acellular pertussis vaccines, and may be pivotal to understanding much of the resurgence of this disease in countries using acellular vaccines.  Additionally, we offer the hypothesis that herd effects generated by acellular vaccines may be due to modification of disease presentation, leading to reduced infectiousness by those already infected, as opposed to resistance to infection among those who were exposed.
Thursday, Apr. 12, 2018
Soe Soe Thwin, Manager of SIS/Biostatistics and Data Management Group at the World Health Organization’s Department of Reproductive Health and Research
Research Profile of Reproductive Health and Research Department at World Health Organization, Geneva
Department of Reproductive Health and Research (RHR) within the Family Women’s and Children’s health cluster of the World Health Organization includes the Special Program of Research, Development and Research Training in Human Reproduction (HRP), which serves as the main instrument within the United Nations system for research in human reproduction. RHR and HRP provide leadership on matters critical to sexual and reproductive health through shaping the research agenda, and coordinating high-impact research; setting norms and standards; articulating an ethical and human-rights-based approach; and supporting research capacity in low-income settings, by bringing together policy-makers, scientists, health care providers, clinicians, consumers and community representatives. My talk will focus on research topics supported by the RHR and HRP and highlight study design and bio-statistical considerations of select studies.
Thursday, May 10, 2018
Elena Losina, Robert W. Lovett Professor of Orthopedic Surgery at Harvard Medical School, Brigham and Women’s Hospital’s Department of Orthopedic Surgery
Using Value of Information Analysis to Inform Clinical Trials
TBD
Thursday, Jun. 14, 2018
Sebastien Haneuse, Associate Professor of Biostatistics at Harvard T.H. Chan School of Public Health
Adjusting for selection bias due to missing data in electronic health records-based research
Electronic Health Records (EHR) data provide unique opportunities for research. Since EHR data are typically not collected for research purposes, however, numerous methodological issues must be considered and possibly addressed. While substantial research has been devoted to the control of confounding bias in the EHR context, selection bias due to incomplete/missing data has received little-to-no attention. Unfortunately, existing approaches for missing data (e.g. inverse-probability weighting (IPW) and multiple imputation) generally fail to acknowledge the complex interplay of heterogeneous decisions, made by patients, providers, and health systems, that govern whether specific data elements in the EHR are observed. In the clinical literature, this collection of decisions is referred to as the “data provenance”. Building on a recently-proposed framework for modularizing the data provenance, we develop a general and scalable framework for estimation and inference with respect to regression models based on IPW that allows for a hierarchy of missingness mechanisms to better align with the complex nature of EHR data. We show that the proposed estimator is consistent and asymptotically Normal. We also derive the form of the asymptotic variance and propose a consistent estimator. Extensive simulations show that naive application of standard methods may yield biased point estimates; that the proposed estimators have good small-sample properties; and, that researchers may have to contend with a bias-variance trade-off as they consider how to handle missing data. The proposed methods are motivated by, and illustrated with, data from an on-going, multi-site EHR-based study of the effect of bariatric surgery on BMI.

This methods group is co-organized with faculty in the Departments of Biostatistics; Epidemiology; Health Law, Policy & Management; and Global Health. The group will meet weekly for 2 hours during the Fall semester. Over the course of 10 meetings, we will discuss frontier topics in applied econometrics and their relevance to population health science and health services research. As an organizing text, we will cover Susan Athey and Guido Imbens’ 2016 review paper “The State of Applied Econometrics – Causality and Policy Evaluation”. Each session will cover a different topic and student participants will be asked to present on the topic and place it into context of the prior literature. The discussion will focus on understanding the methods, identifying questions for further inquiry, identifying population health and health services applications, and discussing how the methods might be implemented. As a final product, students and post-docs taking the course will prepare a brief research proposal to implement one of the discussed methods in a future research project, with potential for future mentorship.

Please contact Dr. Yorghos Tripodis (Biostatistics) or Dr. Jacob Bor (Global Health) for more information.

Click here for more information.

Interested speakers should contact Gheorghe Doros or Sandeep Menon. Please include abstract(s) in your correspondence. You may also email Dr. Doros or Dr. Menon to be added to the Working Group mailing list.

Lead Faculty: Gheorghe Doros

The Statistical Genetics Working Group meets regularly from 9:30 to 11 a.m. every other Friday at 801 Massachusetts Avenue, Crosstown Building, Room 305. The goal is to get to know each other, learn cutting-edge research, foster collaboration, and get help. 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: Ching-Ti Liu.

The Genetic Analysis Workshops (GAWs) are a collaborative effort among genetic epidemiologists and statistical geneticists to develop, evaluate, and compare statistical genetic methods. They are coordinated by the Southwest Foundation for Biomedical Research.

Monthly student luncheon followed by seminar, which features innovative speakers in the area of statistical genetics. More information. Contact: Haldan Smith