Applied Biostatistics Seminar Series on Feb. 18: Neural Networks (Zoe Guan, PhD)

Please join us for the next installment of the MGH Biostatistics Applied Biostatistics Seminar Series, a new seminar series designed to introduce researchers to intermediate topics that are highly relevant to clinical biostatistical research. Presenters will join us on every 3rd Friday of each month to introduce us to their area of expertise and motivate the use of these methods with concrete clinical examples.

Using Neural Networks to Predict Breast Cancer Risk

Friday February 18, 2022, 1:00-2:00pm

https://partners.zoom.us/j/89183981161

Speaker: Zoe Guan, PhD, Postdoctoral Research Fellow, Memorial Sloan Kettering Cancer Center

Abstract: Neural networks are a flexible machine learning method inspired by the structure of the brain. They are able to capture complex nonlinear relationships between inputs and outputs and have achieved remarkable accuracy in many prediction and classification problems, including natural language processing, image recognition, and prediction of health outcomes. Recently, there has been growing interest in applying neural networks to clinical problems, especially with the acceleration of large-scale data collection efforts in healthcare. In this talk, I will give an overview of two types of neural networks, fully-connected and convolutional, and discuss an application of these approaches to breast cancer risk prediction using family history data.

This will be a virtual event. Please contact tthaweethai@mgh.harvard.edu with any questions.

Upcoming seminars:

Friday March 18, 2022 (1-2pm): Kaitlyn Cook, PhD, Postdoctoral Research Fellow, Harvard Medical School and Harvard Pilgrim Healthcare Institute. Topic: Interval-censored data and HIV prevention trials

Brian Healy photo

Course Announcement: Basic Biostatistics for Clinical Research (Brian Healy, PhD)

Brian Healy, PhD will be teaching the course “Basic Biostatistics for Clinical Research” between Friday January 7 and Friday February 4, 2022. The course is sponsored by the MGH Division of Clinical Research and MGH Biostatistics.

This course will provide clinical researchers with a solid foundation in biostatistical concepts. Intended for those with minimal statistical experience, these five lectures will serve as an introduction to biostatistical issues in clinical investigation and will prepare students for more advanced courses on clinical trial design and biostatistics offered through the DCR’s Education Unit.

The course is open to learners at MGB. Click here to register.

Applied Biostatistics Seminar Series on Nov. 19: Semi-competing risks (Harrison Reeder)

Please join us for the inaugural seminar of the MGH Biostatistics Applied Biostatistics Seminar Series, a new seminar series designed to introduce researchers to intermediate topics that are highly relevant to clinical biostatistical research. Presenters will join us on every 3rd Friday of each month to introduce us to their area of expertise and motivate the use of these methods with concrete clinical examples.

Semi-competing risks: modeling and joint prediction of dependent non-terminal and terminal events

Friday November 19, 2021, 1:00-2:00pm

Hybrid event: In-person and on Zoom (see details below)

MGH Biostatistics Conference Room, 50 Staniford St Ste 560

or https://partners.zoom.us/j/86551137602

Speaker: Harrison T. Reeder, PhD Candidate, Department of Biostatistics, Harvard TH Chan School of Public Health

Abstract: Semi-competing risks refers to the survival analysis setting where the occurrence of a non-terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi-competing risks arise across a broad range of clinical contexts, but are not always recognized as such, leading researchers to pursue analyses that ignore the dependence between events, or focus solely on a single or composite outcome. In particular, unlike standard competing risks, semi-competing risks provide an opportunity to learn about the joint risk of the two events, enabling individualized risk prediction of patients’ entire outcome trajectories across time. In this talk we will build on familiar survival analysis tools to introduce the frailty-based illness-death model for semi-competing risks. This framework captures the complex interplay between risk factors and the joint outcomes, and aligns with the needs of clinical decision makers. We illustrate this with recent work on joint prediction in two clinical settings: preeclampsia and delivery during pregnancy, and shock and death among heart failure patients receiving implantable cardioverter defibrillators.

This will be a hybrid in-person/virtual event. Harrison will be joining us in-person and MGH employees who would like to attend in-person are welcome to do so. The option to attend virtually will be made available to all. Due to COVID protocols, non-MGH employees are not permitted to attend in-person at this time. Please contact tthaweethai@mgh.harvard.edu with any questions.

Upcoming seminars:

Kaitlyn Cook, PhD, Postdoctoral Research Fellow, Harvard Medical School and Harvard Pilgrim Healthcare Institute. Topic: Interval-censored data and HIV prevention trials

Zoe Guan, PhD. Postdoctoral Research Fellow, Memorial Sloan Kettering Cancer Center. Topic: Neural networks and prediction of hereditary cancers

A rendering of the SARS-Cov-2 virus

Mass General Brigham Researchers Selected by NIH to Study Long Term Effects of COVID-19 Infection

Mass General Brigham Researchers Selected by NIH to Study Long Term Effects of COVID-19 Infection

Researchers from Mass General Brigham have been selected by the National Institutes of Health (NIH) for an important research opportunity to help the country rapidly improve understanding of recovery after COVID-19 infection and to prevent and treat the long-term complications, collectively referred to as Post-Acute Sequelae of SARS-CoV-2 infection (PASC).

Mass General Brigham assembled a team to respond to the research opportunities that were announced by the NIH in February as part of the new PASC initiative to characterize the prevalence and risk factors for long-term outcomes of COVID-19 infection and to develop ways to treat or prevent these conditions. The PASC Initiative aims to assemble a nationwide multi-cohort study to help researchers learn more about how COVID-19 may lead to such widespread and lasting symptoms.

“Our hospitals have been on the frontlines of this devastating pandemic and we have mobilized every resource available, but we still don’t know for certain what the long-term health impacts will be for the tens of thousands of patients we cared for or how widespread the long-term public health consequences will be. Through research and discovery, Mass General Brigham is committed to being at the forefront of the public health response so that we can better understand this complicated illness for our patients and others who have been impacted – locally, nationally and throughout the world.”

Anne Klibanski, MD
President and Chief Executive Officer
Mass General Brigham

In a statement announcing the initiative, Francis Collins, MD, PhD, Director of the NIH said, “We believe that the insight we gain from this research will enhance our knowledge of the basic biology of how humans recover from infection, and improve our understanding of other post-viral syndromes and autoimmune diseases, among others.”Mass General Brigham researchers were selected to serve as the PASC Data Resource Core to support and contribute to the collection, coordination, and analysis of data collected on PASC patients, including COVID-19 “long-haulers,” throughout the nation. The PASC Data Resource Core will provide expertise on study design and facilitate the collection and analysis of standardized data across different cohort studies. The team will be led by Andrea Foulkes, ScD, Chief of Biostatistics at Massachusetts General Hospital, Elizabeth Karlson, MD, MS Director of Rheumatic Disease Epidemiology at Brigham and Women’s Hospital, and Shawn Murphy, MD, PhD, Chief Research Information Officer at Mass General Brigham, and will include complementary teams from Harvard Medical School and Harvard T.H. Chan School of Public Health.

“We are so proud of our talented research leaders at Mass General Brigham who immediately and skillfully responded to the request of the NIH for the patient, medical, and scientific communities to come together. In partnership with the NIH, and most importantly for the benefit of our patients, we look forward to better understanding, managing, and hopefully preventing and treating the long-term medical consequences of this trying infection,” said Ravi Thadhani, MD, MPH, Chief Academic Officer of Mass General Brigham.

The PASC Data Resource Core is a four-year, multimillion dollar project that will begin immediately.

Media Contact

Mass General Brigham:
Bridget Perry bperry7@partners.org