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Please join us for the tenth 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.

Designing a Longitudinal Clinical Trial Based on a Composite Endpoint: Sample Size, Monitoring, and Adaptation

Thursday September 14, 2023, 12:00-1:00pm

Hybrid event: In-person, 50 Staniford St. Ste 560 Large Conference Room and on Zoom (https://partners.zoom.us/j/81785601606) 

Speaker: David A. Schoenfeld, PhD, Massachusetts General Hospital

Abstract:Hierarchical composite endpoints are often used in regulatory trials but there aren’t many guidelines about how to design such trials. I focus on the design issues that these trials face. Namely how to determine the sample size of such a trial, how to use group sequential monitoring and how to develop an adaptive design. We assume that the analysis will be based on a ranking method where each endpoint is compared in an order specified by the importance of the endpoint and the first endpoint that can be compared at the minimum of the two patients follow up times is used to determine the ranking. if each patient has a different follow up time, the ranking will not be transitive and the simple Wilcoxon variance cannot be used. In addition, the probability that one treatment is better than another is often not an intuitively useful parameter for determining sample size. We. suggest simulation approaches on how to deal with these issues. Both group sequential and adaptive methods suffer from the issue that sequential looks at the data do not satisfy independent increments. We show that the usual methods need to be modified to use the correlation coefficient that relates the data of the current look with previous ones. We describe this modification and how to estimate this correlation.

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

Tanayott Thaweethai, PhD, and Andrea S. Foulkes, ScD, led a manuscript titled, “Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection,” published online in the Journal of the American Medical Association (JAMA) on May 25, 2023. This high-impact work, which has already drawn the attention of several national news outlets, proposes a preliminary symptom-based rule that can be used to identify cases of postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Their paper leverages data collected and analyzed from 9764 participants in the RECOVER adult cohort, a prospective longitudinal cohort study. Several other MGH Biostatistics members were major contributors to this publication including co-authors Caitlin A. Selvaggi, MS, Daniel J. Shinnick, MS, and Carolin Schulte, PhD. This is a significant launching point for future research into understanding risk factors, mechanisms of disease, and potential therapies for long COVID.

For more information: https://www.massgeneralbrigham.org/en/about/newsroom/press-releases/new-long-covid-framework-highlights-12-signature-symptoms

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.

Does Real-World Evidence have a role in Precision Oncology? 

Tuesday May 9, 2023, 3:00-4:00pm

Hybrid event: In-person, 50 Staniford St. Ste 560 Large Conference Room and on Zoom (https://partners.zoom.us/j/84315824841) 

Speaker: Brian Hobbs, PhD, Associate Professor, Dell Medical School, The University of Texas

Abstract: The FDA instituted a program for Accelerated Approval in 1992, which allowed for approvals on the basis of surrogate endpoints for drugs treating serious conditions that filled an unmet medical need. The Food and Drug Administration Safety Innovations Act, passed in 2012, allows accelerated approvals for appropriate drugs and indications by evaluating the effects of drugs on surrogate markers. More recently, the FDA has established three additional pathways to speed the review process for emerging therapies. These changes prompted innovations in trial design with master protocol and seamless designs. Immune checkpoint inhibitors (ICIs) have yielded promising therapies for patients experiencing refractory cancers. Trials evaluating ICIs made extensive use of phase Ib, enrolling hundreds and even more than one thousand patients into dose expansion cohorts following dose-escalation spanning multiple tumor types. This represents a departure from conventional drug development strategies, for which dose expansion cohorts were used in roughly 25% of phase trials. Moreover, in 2021 two drugs, Atezolizumab and Durvalumab, were voluntarily withdrawn from accelerated approvals for PD-L1 inhibition in advanced or metastatic bladder cancer. This presentation considers the statistical implications of expansive, uncontrolled early phase trials and discusses the potential role for real-world evidence in this setting.  

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

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.

Moving to a World Beyond p<0.05

Thursday April 20, 2023, 12:00-1:00pm

Hybrid event: In-person, Simches Research Building Room 3110 (185 Cambridge St, Boston) and on Zoom (https://partners.zoom.us/j/89829344107)

Speaker: Ronald L. Wasserstein, PhD, Executive Director, American Statistical Association

Abstract: For nearly a hundred years, the concept of “statistical significance” has been fundamental to statistics and to science. And for nearly that long, it has been controversial and misused as well. In a completely non-technical (and generally humorous) way, ASA Executive Director Ron Wasserstein will explain this controversy, and say why he and others have called for an end to the use of statistical significance as means of determining the worth of scientific results. He will talk about why this change is so hard for the scientific community to make, but why it is good for science and for statistics and will point to alternate approaches.

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

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: In-person (Simches Research Building, Room 3110, 185 Cambridge St., Boston) and on Zoom https://partners.zoom.us/j/89829344107 

Speaker: Ronald L. Wasserstein, Executive Director, American Statistical Association

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.

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/

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

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.