Applied Biostatistics Seminar Series on Thu Dec 1: Using stochastic-interventional causal effects to evaluate treatment efficacy in clinical trials (Nima Hejazi, PhD)

Please join us for the next installment of the MGH Biostatistics Applied Biostatistics Seminar Series, a 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.

Using stochastic-interventional causal effects to evaluate treatment efficacy in clinical trials

Thursday December 1, 2022, 12:00-1:00pm

Hybrid event: https://partners.zoom.us/j/89410632413 and in-person (50 Staniford St, Ste 560, Large Conference Room)

Speaker: Nima Hejazi, PhD, Assistant Professor, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Abstract: In clinical trials randomizing participants to active vs. control conditions and following study units until the occurrence of a primary clinical endpoint, evaluating the efficacy of a quantitative exposure or mediator (e.g., drug dosage, drug- or vaccine-induced biomarker activity) is challenging. This is due, in part, to the fact that statistical innovations in causal inference have historically focused on defining interpretable estimands compatible only with categorical (or binary) treatments. We will introduce stochastic-interventional causal effects, which provide a measure of the effect attributable to perturbing a treatment’s natural (i.e., observed or induced) value, focusing primarily on how these effect definitions provide a scientifically informative solution when working with quantitative (continuous-valued) intervention variables. Unfortunately, the estimation of these, and other, estimands in treatment or vaccine efficacy clinical trials often requires significant additional care, for such trials measure immunologic biomarkers — critical to understanding the mechanisms by which vaccines confer protection or as surrogate endpoints in future clinical trials — via outcome-dependent two-phase sampling (e.g., case-cohort) designs. These biased sampling designs have earned their popularity: they circumvent the administrative burden of collecting potentially expensive biomarker measurements on all study units without limiting opportunities to detect biomarkers mechanistically informative of the disease or infection process. To address this, we outline a semiparametric correction procedure that recovers population-level estimates (in spite of two-phase sampling of the intervention variable), with guarantees of asymptotically efficient inference (i.e., minimal variance within a suitable regularity class), of a causally informed vaccine efficacy measure defined by contrasting assignments of study units to active vs. control conditions while simultaneously hypothetically shifting biomarker expression in the active condition. This results in a _descriptive_ causal dose-response analysis informative of next-generation vaccine efficacy and useful for bridging vaccine efficacy from a source pathogen strain (e.g., SARS-CoV-2 at outbreak, i.e., D614G) to reasonably similar variants of concern (e.g., Delta). We present the results of applying this approach in an analysis of the joint U.S. Government and COVID-19 Prevention Network’s COVE COVID-19 vaccine efficacy clinical trial of Moderna’s two-dose mRNA-1273 vaccine.

Please contact tthaweethai@mgh.harvard.edu with any questions.

MGH Biostatistics Launches Faculty Search 2022

The Massachusetts General Hospital (MGH) Division of Clinical Research is seeking qualified applicants with doctoral degrees in Biostatistics or a related field for a faculty-level position in Biostatistics. Academic appointments at the rank of Instructor, Assistant, or Associate Professor at Harvard Medical School will be within the Department of Medicine. For more information, please visit our Career Openings page.

Interested candidates should send a cover letter, research statement, three potential referees and Curriculum Vitae to biostat@mgh.harvard.edu. Review of applications will begin December 1, 2022 and continue until the position is filled.

Dr. Andrea Foulkes to Receive 2022 Lagakos Distinguished Alumni Award

Andrea S. Foulkes, ScD, director of Biostatistics at MGH, will be the recipient of the 2022 Lagakos Distinguished Alumni Award from the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. This award recognizes Department alumni whose research in statistical theory and application, leadership in biomedical research, and commitment to teaching have had a major impact on the theory and practice of statistical science.

For more information: https://www.hsph.harvard.edu/biostatistics/2022/09/dr-andrea-foulkes-to-receive-2022-lagakos-distinguished-alumni-award/

Mass General Brigham investigators approved for large studies on reversing acute suicidal depressed state, bipolar depression

Research teams at Mass General Brigham’s founding members, Brigham and Women’s Hospital (BWH) and Massachusetts General Hospital (MGH), have been approved for more than $50 million in funding awards by the Patient-Centered Outcomes Research Institute (PCORI) for studies focused on treating two important mental health conditions. Both projects are large-scale, high-impact research trials that will invite patients to participate at sites throughout the country, including at MGH, Brigham and Women’s Faulkner Hospital (BWFH), and McLean Hospital, all members of the Mass General Brigham system.

The team at MGH, led by Andrew A. Nierenberg, MD, director of the Dauten Family Center for Bipolar Treatment Innovation, and Andrea S. Foulkes, ScD, director of Biostatistics, will use their nearly $25 million in funding to compare the effectiveness of four treatments for bipolar depression.

For more information, see: https://www.massgeneralbrigham.org/en/about/newsroom/press-releases/investigators-approved-large-studies-reversing-acute-depression

 

Applied Biostatistics Seminar Series on Wed Jul 27: Opportunities and Challenges in Using Digital Biomarkers as Trial Outcomes and Enrichment (Hiroko Dodge, 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.

Opportunities and Challenges in Using Digital Biomarkers as Trial Outcomes and Enrichment

Wednesday July 27, 2022, 1:00-2:00pm

Virtual event: https://partners.zoom.us/j/84562510027

Speaker: Hiroko Dodge, PhD. Professor of Neurology, Co-Associate Director, Biostatistics and Data Management Core Lead, NIH-Layton Aging and Alzheimer’s Disease Center, Oregon Center for Aging and Technology, Department of Neurology, Oregon Health & Science University, Portland, OR.

Abstract: The two major challenges in clinical trials in the dementia field are large intra- and inter-individual variabilities. For example, cognitive test results can fluctuate depending on the assessment time of the day, sleep quality the night before, and other health conditions at the time of assessments (intra-individual fluctuations) which can override or obscure longitudinal changes and trial effects. Also, subjects show different clinical symptoms given the same levels of pathological burdens due to individual differences in the level of cognitive reserve and resilience. This large inter-individual variability makes it challenging to identify accurately those destined to have disease progression. Highly frequently monitored digital biomarkers are able to overcome some of these challenges by generating person-specific distributions of biomarkers within a short time duration. These person-specific distributions can be used to monitor deviations from their own normative or pre-trial distributions and thereby provide more sensitive measures of changes induced by disease progression or interventions. In my presentation, I will highlight some of the advantages of digital biomarkers as trial outcomes and enrichment as well as challenges that inherently exist in digital biomarkers. I will conclude with suggestions for future research.

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

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.