Inaugural MGH Biostatistics Student Research Symposium

This semester, we were very lucky to have the following students from the biostatistics department at Harvard T.H. Chan School of Public Health join us in several different collaborative and methodological research projects:

Carolin Schulte, Daniel Nolte, Devika Godbole, Jie Sun, Lauren Mock, Paul Licht, Ta-Chou (Vincent) Ng, Tingyi Cao, Yidan Ma, and Zainab Soetan.

Last week, at the inaugural MGH Biostatistics Student Research Symposium, six students gave presentations on their research projects:

  • Devika Godbole: Modeling the relationship between infant birthweight and gestational glucose intolerance using quantile regression
  • Zainab Soetan: Longitudinal changes in insulin secretion and sensitivity in women with gestational diabetes risk factors
  • Dan Nolte: Examining mindfulness, wellbeing, and their relationship following Mindfulness-Based Cognitive Therapy (MBCT)
  • Lauren Mock: Causal inference for temperature and mortality time series
  • Paul Licht: Identifying predictors of sustained weight loss in a weight loss intervention trial among individuals with type 2 diabetes
  • Ta-Chou (Vincent) Ng: Clustering and Risk Analysis of COVID Hospitalization with Time-varying Biomarkers

If you are a biostatistics student in the Boston area interested in learning about future research opportunities, click here.

Applied Biostatistics Seminar Series on Thu May 19: The essential role of early phase clinical trials in oncology (Alexia Iasonos, 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 introduce us to their area of expertise and motivate the use of these methods with concrete clinical examples.

The essential role of early phase clinical trials in oncology: design and interpretation

Thursday May 19, 2022, 12:00-1:00pm

Hybrid event: In-person at 50 Staniford St, Ste 560 & on Zoom (https://partners.zoom.us/j/82088584012 )

Speaker: Alexia Iasonos, PhD, Attending Biostatistician, Director, Clinical Research Development, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York.

Abstract: The specific aims and overall purpose of Phase I clinical trials in oncology have undergone substantial changes over the last several years. The trials originally had a single objective of showing adequate tolerance of an investigational agent or regimen in a heterogeneous patient population. The last several years have seen this single objective evolve into an ambitious set of objectives: to identify the most effective dose of a single agent or combination of agents, the right treatment schedule, and the right patient population in terms of the likelihood of antitumor activity. In this talk, I will provide a broad overview of how the objectives, design, and interpretation of early phase trials have evolved and how the inclusion of dose-expansion cohorts has changed the landscape of early drug development. I will present two recent methodological advancements in this field that aim to correct the bias that can result from errors in toxicity attribution and the bias that can result when the administered dose differs from the assigned dose, which can occur in Phase I trials of CAR T therapy.

This will be a hybrid in-person and virtual event. Dr. Iasonos will be joining us in person for this presentation. Please contact tthaweethai@mgh.harvard.edu with any questions.

Applied Biostatistics Seminar Series on Apr. 15: Early Detection of Ovarian Cancer through Statistical Modeling of Longitudinal Biomarkers (Steven J. Skates, 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.

Early Detection of Ovarian Cancer through Statistical Modeling of Longitudinal Biomarkers

Friday April 15, 2022, 1:00-2:00pm

https://partners.zoom.us/j/82231967249

Speaker: Steven J. Skates, PhD, Associate Biostatistician at Massachusetts General Hospital and Associate Professor of Medicine at Harvard Medical School.

Abstract: Ovarian cancer (OC) is a prime target for early detection due to a high proportion of cases detected in late-stage disease and a large difference in prognosis between patients detected in early stage compared to late-stage detection. However, the low annual incidence of OC presents a serious medical and statistical challenge – how to detect the cases as early as possible in time without generating too many false positives potentially resulting in surgical interventions. A pilot trial of 1,000 women detected one ovarian cancer using a then recently discovered ovarian cancer serum biomarker CA125 with a single threshold and referral to ultrasound if positive. The CA125 profile over time in the case was striking with an exponential growth in distinct contrast to the relatively stable CA125 profile in all other subjects. Subsequent larger pilot trials showed a similar phenomenon. The statistical challenge was how to use the extra information in the CA125 profile to increase sensitivity in early stage while maintaining the same low and acceptable false positive rate. We developed a calculation for the probability of having undetected OC given a series of CA125 values built on a longitudinal change-point model for OC cases and from a longitudinal flat model for controls. This calculation replaced the single CA125 threshold for screening decisions. We implemented the calculation in five prospective early detection trials in the US and UK, including a 20-year RCT of over 200,000 women. Results from the five trials were mixed, with a negative result in the largest trial but encouraging results in the other trials. We reflect on the current state of the field and describe a parallel biomarker discovery and validation program to identify novel biomarkers and approaches that may improve on the longitudinal serum CA125 approach to early detection of OC.

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

Upcoming seminars:

Thursday May 19, 2022 (12-1pm): Alexia Iasonos, Attending Biostatistician, Memorial Sloan Kettering Cancer Center

Applied Biostatistics Seminar Series on Mar. 18: Interval-Censored Failure Time Data (Kaitlyn Cook, 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.

An Introduction to Interval-Censored Failure Time Data, with Applications to HIV Prevention Trials

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

https://partners.zoom.us/j/86995026265

Speaker: Kaitlyn Cook, PhD, Postdoctoral Research Fellow, Harvard Medical School and Harvard Pilgrim Healthcare Institute

Abstract: Interval-censored failure time data naturally arise in biomedical and epidemiological studies in which the event of interest is subject to periodic follow-up (as in electronic health records research) or otherwise not directly observable (as in infectious disease prevention studies). Since questionnaires, physical exams, blood cultures, and serological tests are conducted on an intermittent basis, the exact timing of this event is known only up to the interval between the last negative and first positive tests. When the subjects in these studies also belong to existing, non-investigator determined groups—such as hospitals, communities, transmission networks, or insurance networks—the resulting data may be both interval-censored and clustered. The presence of these two features leads to a loss of statistical information and power and presents complications for both estimation and inference. In this talk, I will provide an introduction to interval-censored failure time data and discuss some common methods for analyzing this data in the independent data setting. I will then introduce a novel extension of the proportional hazards framework to the clustered data context, and will illustrate its use by re-analyzing a large-scale cluster-randomized trial of combination HIV prevention methods.

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

Upcoming seminars:

Friday April 15, 2022 (1-2pm): Steven J. Skates, PhD, Associate Investigator at MGH Biostatistics and Associate Professor of Medicine at Harvard Medical School. Topic: Longitudinal Biomarkers for Early Detection of Cancer

Harvard Catalyst Journal Club on Mar. 2: Missing Data Challenges in EHR-Based Studies of COVID & Long COVID (Tanayott Thaweethai, PhD)

Tanayott Thaweethai, PhD will lead a journal club with Harvard Catalyst on Wed 3/2 from 1-2pm.

Title: Missing Data Challenges in Electronic Health Records-Based Studies of COVID and Long COVID

Abstract: Since the beginning of the COVID-19 pandemic, researchers have repeatedly turned to electronic health records (EHR) to rapidly answer complex questions about the short and long-term consequences of SARS-CoV-2 infection. However, because EHR are not collected for research purposes, observational studies using EHR are subject to various challenges and biases, including bias due to missing data. Standard missing data methods generally fail to address the complex nature of EHR data, particularly the interplay of numerous decisions by patients, physicians, and insurers that collectively determine whether “complete” data is observed. Tanayott Thaweethai, PhD, Massachusetts General Hospital, will discuss some statistical methods for handling bias due to missing data in the EHR setting, and conclude with an introduction to a semi-supervised learning technique for handling the “positive unlabeled” problem of phenotyping individuals based on the presence or absence of clinical codes.

The talk will occur on Wednesday March 2, 2022, 1-2pm.

Register here.

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