Events Calendar

Authentic partnerships for relevant, actionable research

This 90-minute live training will give academic researchers interested in doing engaged research in the Chinatown neighborhood of Boston a basic grounding in authentic and relevant best practices for engaging in this type of work with community partners. The topics to be covered are the benefits and challenges in this research and how to build trusting, authentic relationships with community partners. Through this training, participants will hear some of the voices from the community, and be given time to reflect on your role in and perspective on community engaged research and how to make it action-oriented and relevant in today’s world.

By the end of this training, you should be able to:

• Define community engaged research as has been practiced in Boston Chinatown.
• Describe methods for building trust with community partners and research participants.
• List qualities required for successful community engaged research practice
• Provide a case study of problem gambling as an example of how community engaged research can be mutually beneficial for researchers and community members.

Faculty

This training will be taught by Carolyn Rubin, EdD, MA, Director of Addressing Disparities in Asian Populations through Translational Research (ADAPT).

Who should attend

Investigators, research study team members, and graduate students are encouraged to attend.

Details

Thursday, June 3
10:00-11:30AM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

Are you a clinical research team member obtaining informed consent from human research volunteers?

Could you or your team use information on best practices and strategies for working with interpreters?

Join Tufts CTSI’s Stakeholder and Community Engagement Program’s quarterly training focused on Including Non-English-Speaking Participants in Research. This is a blended synchronous/asynchronous workshop that includes completion of a required, self-paced tutorial on Tufts CTSI I LEARN followed by an interactive live session hosted on Zoom. The live session will provide an opportunity to practice working with professional interpreters to obtain informed consent with non-English-speaking members of Tufts CTSI’s Stakeholder Expert Panel. Panel members are former research participants and individuals with experience as simulated patients. This is a learning and skill-building opportunity for you and will not be evaluated in any way.

Participants must complete the online pre-work in order to receive the Zoom link for the live training. While in the live session, your active participation is required, including participating in the roleplay, providing feedback to others, and engaging in group discussion. This will ensure that you get the most out of what this training has to offer.

By the end of this training, you should be able to:

• Identify when a participant needs an interpreter in order to obtain informed consent.
• Demonstrate the correct procedure for using an interpreter to obtain informed consent.
• Demonstrate three communication techniques to use while obtaining informed consent with an interpreter.

Faculty

This training will be taught by Robert Sege, MD, PhD, Tufts CTSI Lead Navigator and Co-Director of Stakeholder and Community Engagement.

Who should attend

Clinical research coordinators, investigators, research fellows, research nurses, and anyone involved in obtaining informed consent are encouraged to attend.

Details

Tuesday, June 8
1:30-3:30PM
Online via Zoom (a link will be sent to those who register).

*Pre-work via Tufts CTSI I LEARN must be completed prior to the training.

Registration

To attend, please register here.

What is the long-term effect of direct antiviral agents for Hepatitis C?

The first June seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, June 9, 2:00-3:00PM via Zoom. The topic of this month’s webinar is What is the Long-term Effect of Direct Antiviral Agents for Hepatitis C? A Causal Inference Approach Using ‘Big Data,’ presented by Sara Lodi, PhD.

The advent of direct-acting antiviral agents (DAAs) in 2011 revolutionized hepatitis C virus (HCV) treatment: based on clinical trials and real world data, approximately 95% of patients treated with DAA achieved a sustained virological response equivalent to cure. However, even after cure is achieved, the risk of hepatic and extra-hepatic disease remains. Our understanding of post-DAA clinical outcomes is based on clinical trials with relatively short follow-up and selected participants. However, the extent to which DAA impacts extra-hepatic morbidity in the long-term and in heterogenous populations is unknown. Electronic health records collected in routine clinical practice provide a unique opportunity to estimate the long-term benefits of DAA treatment and to assess the need for post-DAA clinical management.

In this talk, Dr. Sara Lodi will discuss how to design an observational study to estimate the effect of DAA on kidney function using the target trial approach. She will also describe how to apply and interpret the results of the parametric g-formula, a causal inference method that provides consistent estimates in the presence of treatment-confounding feedback. Dr. Lodi will present preliminary results using electronic health records from Boston Medical Center and the HepCAUSAL collaboration.

Faculty

Sara Lodi, PhD is an Assistant Professor in Biostatistics at Boston University School of Public Health. She obtained her PhD in Medical Statistics at the London School of Hygiene and Tropical Medicine in 2009. Her research focuses on clinical trials, clinical epidemiology and comparative effectiveness research using routinely collected heath data, particularly in the area of infectious disease and substance use. Methodologically, she focuses on statistical techniques for causal inference to estimate effects of interventions along the HIV continuum of care. She has published many articles on behalf of large international collaborations of HIV cohorts such as CASCADE, COHERE, URBAN ARCH and the HIV-CAUSAL Collaboration.

Details

Wednesday, June 9, 2:00-3:00PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

Are you a clinical research team member obtaining informed consent from human research volunteers?

Could you or your team use information on best practices and strategies for working with interpreters?

Join Tufts CTSI’s Stakeholder and Community Engagement Program’s quarterly training focused on Including Non-English-Speaking Participants in Research. This is a blended synchronous/asynchronous workshop that includes completion of a required, self-paced tutorial on Tufts CTSI I LEARN followed by an interactive live session hosted on Zoom. The live session will provide an opportunity to practice working with professional interpreters to obtain informed consent with non-English-speaking members of Tufts CTSI’s Stakeholder Expert Panel. Panel members are former research participants and individuals with experience as simulated patients. This is a learning and skill-building opportunity for you and will not be evaluated in any way.

Participants must complete the online pre-work in order to receive the Zoom link for the live training. While in the live session, your active participation is required, including participating in the roleplay, providing feedback to others, and engaging in group discussion. This will ensure that you get the most out of what this training has to offer.

By the end of this training, you should be able to:

• Identify when a participant needs an interpreter in order to obtain informed consent.
• Demonstrate the correct procedure for using an interpreter to obtain informed consent.
• Demonstrate three communication techniques to use while obtaining informed consent with an interpreter.

Faculty

This training will be taught by Robert Sege, MD, PhD, Tufts CTSI Lead Navigator and Co-Director of Stakeholder and Community Engagement.

Who should attend

Clinical research coordinators, investigators, research fellows, research nurses, and anyone involved in obtaining informed consent are encouraged to attend.

Details

Thursday, June 10
9:00-11:00AM
Online via Zoom (a link will be sent to those who register).

*Pre-work via Tufts CTSI I LEARN must be completed prior to the training.

Registration

To attend, please register here.

Want a framework for flexible joint Bayesian modeling of multiple time series where inference is fast, easy, and scalable?

The second June seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, June 16, 2:00-3:00PM via Zoom. The topic is Scalable Bayesian Flexible Joint Time Series Modeling, presented by Michael Wojnowicz, PhD.

We frequently obtain datasets containing multiple time series — that is, a collection of sequences, often corresponding to temporal data from multiple individuals. For example, consider movement patterns of soldiers during a ruck march, lesion counts of multiple sclerosis patients, or computer activity by employees at a company. In this talk, we describe a framework for flexible joint Bayesian modeling of multiple time series where inference is fast, easy, and scalable. In particular, we construct a scalable Bayesian approach to mixed HMMs, where mixed HMMs are Hidden Markov Models with multi-level generalized linear models (a.k.a. Generalized Linear Mixed Models, or Mixed Effects Models) embedded within the transitions and emissions structure. Mixed HMMs are an excellent framework for personalized time series modeling: models can be personalized, while sharing statistical strength across individuals to “fill in” knowledge as necessary, based on knowledge about other individuals, and particularly similar individuals. Moreover, the impact of dynamic covariates can be learned based on their effects across the entire population of individuals. In this talk, we will introduce mixed HMMs, and then discuss how to make inference fast, easy, and scalable.

Faculty

Michael Wojnowicz, PhD is a Data Scientist II in the Data Intensive Studies Center (DISC) at Tufts University, working with the Machine Learning Research Group. He earned his Ph.D. from Cornell University in 2012, where his work in Cognitive Science led to the Dallenbach Fellowship for Research Excellence, the Cognitive Science Dissertation Proposal Award, and the Cognitive Science Graduate Research Award. Dr. Wojnowicz also has master’s degrees in Mathematics (University of Washington) and Statistics (University of California at Irvine). Before joining Tufts University, Dr. Wojnowicz was the Distinguished Data Scientist at Cylance. At Cylance, he developed statistical machine learning models for detecting malicious computer files and anomalous user activity, leading to 10 patents (5 granted, 5 pending). Dr. Wojnowicz’ current research interests include time series modeling, variational inference, and nonparametric Bayesian modeling.

Details

Wednesday, June 16, 2:00-3:00PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

What are the appropriate analytical techniques for study designs and what measures of association, advantages and disadvantages should be used?

This seminar will provide an introduction to epidemiologic study design, including cross-sectional, cohort, and case-control studies. It will review the appropriate analytical techniques for each design along with their measures of association, advantages and disadvantages, and the types of biases that can be present with each. Examples from the literature will be provided to illustrate concepts and common pitfalls that may occur when analyzing data from observational studies.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Faculty

This workshop will be taught by Janis Breeze, MPH and Angie Rodday, PhD, MS

Janis Breeze is an Epidemiologist, Assistant Professor of Medicine, and Associate Director of the Biostatistics, Epidemiology, and Research Design (BERD) Center at Tufts CTSI. She has many years’ experience helping researchers in the design of observational and experimental studies, particularly in the areas of newborn medicine, obstetrics and gynecology, pulmonary medicine, and surgery.

Angie Rodday is a Biostatistician, Assistant Professor of Medicine, and Associate Director of the Clinical and Translational Science (CTS) Graduate Program at the Tufts University Graduate School of Biomedical Sciences. She has experience as a principal investigator of her own work, as well as experience as a Co-Investigator on others’ projects. Dr. Rodday teaches biostatistics courses as part of the CTS Graduate Program.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, June 23
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 30: Introduction to Sequence-Based Transcriptomic Analysis
• July 14: Bioinformatics Analysis of Bulk RNA Sequencing Data
• July 21: Bioinformatics Analysis of Single-Cell RNA Sequencing Data
• July 28: Functional and Enrichment Analysis Methods for RNAseq Data

Event video (YouTube)

This event seeks to provide former study participants with an opportunity to learn about the results of the study in which they participated.

This hour-long Zoom event will include two 15-minute presentations given by investigators to former research participants from the studies being presented. The audience will also have a chance to ask questions for 15 minutes following each presentation. While former participants are the target audience, the general public, including all Wellforce employees, are invited.

Presenters

Dr. Courtney Schroeder from the Department of Hematology and Oncology at Tufts Medical Center:
A Phase 2 Trial of Infliximab in Coronavirus Disease 2019 (COVID-19)

Dr. CJ Hasson from the Department of Physical Therapy at Northeastern University:
Cyberphysical Therapy for Enhanced Neuromotor Recovery in Stroke Survivors

Learning Objectives

Investigator:

• To communicate study findings in plain language and using visual cues
• To understand the impact that disseminating research results has on former study participants and the community at-large

Attendees will:

• Understand the results of the trials in which they participated
• Have a heightened awareness about research being conducted at Tufts Medical Center and/or Tufts University

Who should attend

Former research participants, Wellforce employees, and the general public.

Details

Thursday, June 24
6:00PM, via Zoom

To attend from a PC, Mac, iPad, iPhone or Android device click this URL to join:
https://wellforce.zoom.us/j/94291265014?pwd=L3BJUC9rZ2dIUDVlWXcwTWNMajIvdz09

Passcode: 628570

Or join by phone:

• US: +1 646 876 9923, or
• +1 301 715 8592, or
• +1 312 626 6799, or
• +1 408 638 0968, or
• +1 669 900 6833, or
• +1 253 215 8782, or
• +1 346 248 7799

Webinar ID: 942 9126 5014

When should you use sample enrichment, sequencing format, and library preparation approaches?

RNA-Seq is a powerful technology that can be used to study transcription profile in sample of interest. Yet, the choice of sample enrichment, sequencing format, and library preparation approach all have profound impact on the usability of sequencing data for downstream intended (and intended) analysis. Thus, the parameters are important consideration when designing your own experiment, as well as utilizing data that is available in the public domain. This session aims to provide some guidance on these topics.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Faculty

This workshop will be taught by Eric Reed, PhD and Albert Tai, PhD.

Eric Reed is a Data Scientist at Tufts University. He earned an MS in Biostatistics from the University of Massachusetts Amherst in 2015 and a PhD in Bioinformatics from Boston University in 2020. Eric’s research is focused on working with biomedical researchers to implement cutting-edge high-throughput profiling techniques and develop analytical approaches to better interrogate the biological questions at hand. His dissertation work encompassed advancement of large-scale transcriptomic profiling for toxicogenomic screening. This included the benchmarking scalable library preparation techniques and development of machine learning methods and software. Through numerous collaborative projects, Eric’s work has led to contributions to various biomedical fields including environmental health, metabolic diseases, oral cancer, breast cancer, Huntington’s disease, and addiction.

Albert Tai is a Research Assistant Professor of Immunology at Tufts University. His research work focuses on providing current research technology to basic research community within and outside of the University, including next generation sequencing (NGS), high throughout screen (HTS), high content screen (HGS), robotics automation and flow cytometry. These technologies, especially NGS and HCS, generates significant amount of data and require specialized analytical approaches. A part of his research centers on creating or optimizing these analytical approaches, via utilizing existing software/pipeline and/or developing new ones. Furthermore, research projects that utilize multiple technologies, or multi-omics, are becoming more popular, a mean to allow association and visualization of multi-omics data is also of interest.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, June 30
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 23: Study Design for Biomedical Data Science
• July 14: Bioinformatics Analysis of Bulk RNA Sequencing Data
• July 21: Bioinformatics Analysis of Single-Cell RNA Sequencing Data
• July 28: Functional and Enrichment Analysis Methods for RNAseq Data