Postdoctoral fellows and junior faculty interested in applying for the NC TraCS K12 mentored career development program are invited to learn more about the program from the K12 leaders, Michelle Hernandez, MD, and Jon Juliano, MD. Those interested in being mentors are also invited to learn more about the program.

The same information will be presented at each meeting followed by time for questions and discussion. All sessions are in-person ONLY & held in Brinkhous-Bullitt room 219. Please register for the information session that works best for your schedule.

More information about the NC TraCS K12 mentored career development program is available at https://tracs.unc.edu/index.php/services/education/ctsa-k12.

register

Methods for Handling Missing Data in Cluster Randomized Trials

This NIH Collaboratory Rethinking Clinical Trials Biostatistics Series Grand Rounds features:

Speaker: Rui Wang, PhD
Associate Professor of Population Medicine and Associate Professor in the Department of Biostatistics
Harvard Pilgrim Health Care Institute and Harvard Medical School

Moderator: Fan Li, PhD
Assistant Professor of Biostatistics
Yale School of Public Health

Learn More

Postdoctoral fellows and junior faculty interested in applying for the NC TraCS K12 mentored career development program are invited to learn more about the program from the K12 leaders, Michelle Hernandez, MD, and Jon Juliano, MD. Those interested in being mentors are also invited to learn more about the program.

The same information will be presented at each meeting followed by time for questions and discussion. All sessions are in-person ONLY & held in Brinkhous-Bullitt room 219. Please register for the information session that works best for your schedule.

More information about the NC TraCS K12 mentored career development program is available at https://tracs.unc.edu/index.php/services/education/ctsa-k12.

register

Epigenetic Modulation of the Cancer Antigen GD2 to Improve Immunotherapy in Pediatric Solid Tumors

Join the Children's Research Institute for a hybrid seminar with Justin Sperlazza, MD, PhD, a pediatric oncologist at UNC Health. Participate in the seminar either in person at 3116 Mary Ellen Jones Building (with lunch provided) or via Zoom.

Zoom information - Meeting ID: 985 6644 6544 | Password: 097476

Join Zoom

Design And Implementation Of A Weighted Lottery To Equitably Allocate Scarce COVID-19 Resources

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Erin K. McCreary, PharmD, BCIDP
Director of Infectious Diseases Improvement and Clinical Research Innovation, UPMC
Clinical Assistant Professor of Medicine, University of Pittsburgh
President-Elect, Society of Infectious Diseases Pharmacists (SIDP)

Learn More

The following PCORI Funding Announcements (PFAs)—to be released Jan 9, 2024—invite applications for high-quality comparative clinical effectiveness research (CER) projects.

Both funding opportunities enable the support of PCORnet® studies using PCORnet, the National Patient-Centered Clinical Research Network, to advance PCORI's National Priorities for Health. PCORnet is accessible to all investigators interested in using the PCORnet infrastructure, which was designed to improve the nation's capacity to conduct large-scale patient-centered health research.

UNC is a participating PCORnet site under the STAR CRN. Mike Kappelman, MD is the UNC site principal investigator leading and promoting PCORnet as a national data infrastructure resource for all researchers within UNC interested in using the PCORnet Common Data Model (CDM) to their research questions, from observational studies to large-scale multi-network comparative effectiveness trials.

We invite any interested UNC researchers to submit for the grant opportunities above for Cycle 1 2024.

Office Hours

TraCS is offering office hours on Jan 12, 2024 from 3 - 4 p.m. to answer questions about these opportunities before submission. Presenters:
Michael Kappelman, MD
Nisha Datta, MS
Kellie Walters, MPH
Penny Wang, MS

Join Zoom

NC TraCS Data Science Seminar Series: Introduction to the TraCS Data Science Lab

Join NC TraCS for a monthly seminar series focused on healthcare data science. The January 2024 seminar will kick-off the series with an introduction to the TraCS Data Science Lab (TDSL) from Peter Leese, the director and lead scientist of the lab. In this seminar, Leese will introduce the TDSL, its mission, and how researchers at UNC can collaborate with the TDSL.

The NC TraCS Data Science Seminar Series will be held on the third Tuesday of each month. These 1-hour sessions will cover a range of topics broadly applicable to healthcare data science. While some sessions will focus on organizational aspects of starting or getting involved in data science and AI healthcare research at UNC, other sessions will focus on technical aspects of data architecture and modeling, programming, or application of machine learning methods.

register

The Cecil G. Sheps Center for Health Services Research is hosting Data Science Week, a focused series of discussions tailored for anyone studying access, disparities, quality, or the cost of health care, and is interested in emerging trends in data science.

The week’s agenda includes in-depth exploration of current themes in healthcare analytics, such as the application of causal machine learning techniques, the utilization of compliant synthetic data in research settings, the intricacies of Medicare claims data analysis, and the development of algorithmic approaches in clinical decision-making.

Sheps welcomes contributions from colleagues in the field, offering an environment conducive to scholarly exchange and professional development. This event is an opportunity for researchers, students, and practicing data scientists to engage with complex topics, refine analytical methodologies, and discuss the implications of advanced data science in the realm of health services research.

Presentations will begin at 12:00pm each day, followed up by a discussion period. Attendees may join in-person at the Sheps Center in room 2002 or via Zoom.

Wednesday, January 17, 12:00 – 1:30pm
Research Journey: Health Services Research Studies in Women’s Health, Disabilities, Surgical Outcomes, and More – Leveraging Large Observational Data
Speaker: Neil Kamdar, MA

Large administrative claims, electronic medical records, and other large clinical and surgical registries possess a breadth of information about our patient population. While these data sources may not offer the depth that may be afforded through studies involving primary data collection, they represent an important and fundamental strength of large sample sizes and the ability to study vulnerable populations and/or rare diseases. I will go through studies involving several data sources and populations broken into thematic units: vulnerable populations (e.g. disabled and those with diseases of aging), women’s health, and surgical outcomes and quality. This talk is especially geared for those who wish to better understand the breadth of published studies that can be tackled using these data, the key findings, and illustrate some other new areas I have explored with these registries over the last several years. I will finally touch upon some of the new avenues for exploration using these data and the potential synergies between Sheps analytic data assets and resources with our community of researchers.

Thursday, January 18, 12:00 – 1:30pm
Long COVID Phenotypes Leveraging a Large Primary Care Electronic Medical Record Registry, the American Family Cohort
Speaker: Neil Kamdar, MA

Long COVID or Post-Acute Sequelae following initial SARS-COV-2 (COVID-19) has continued to be a challenge for diagnosing in the primary care setting. Various published studies have incorporated different approaches and methods which have defined some potential opportunities for classification of COVID-19 afflicted patients into relevant sub-groups based on their symptom presentation during the follow-up period. Using a large primary care registry, the American Family Cohort, I will walk through the key existing challenges for classification in the literature, and the incorporation of a latent class analysis approach to examine extant differences in racial composition, sex, and social deprivation index in group membership. Diagnostic phenotypes of potential Long COVID patients and their comparisons to likely influenza-like (ILI) controls will be explored and examined. This talk was presented as a Distinguished Paper at the National American Primary Care Research Group (NAPCRG) in November 2023.

Friday, January 19, 12:00 – 2:00pm
Methods Session: Detecting and Mitigating Bias in Machine Learning
Speaker: Ashley Avis, MS

Statistical models utilize historic and current data to identify patterns and predict them forward. Historic and present-day biases can be perpetuated without careful evaluation and bias mitigation. We will discuss how to prepare and plan a project utilizing responsible data science methods and other techniques from peer reviewed journals and gray literature to support bias evaluation and mitigation. In this presentation, we will explore how to identify patterns of bias, how predictive models incorporate patterns of bias, and methods for resolving bias. We will explore definitions of algorithmic fairness, the conflicts between different fairness definitions, and the importance of diverse perspectives on final fairness evaluation. We will discuss methods for identifying unequal or biased model decisions. Lastly, we will discuss methods for correcting biased models and how to practice data science responsibly.

An Application and Example of Glassbox Explainable Boosting Machines (EBMs) in a Surgical Cohort Registry
Speaker: Neil Kamdar, MA

Predictive models have become important for postoperative occurrences after surgery, such as readmissions, 30-day reoperation, and other complications. Frequently, the application of predictive models has an opaqueness which remains extraordinarily difficult to achieve stakeholder buy-in, which is usually hospital administrators, clinicians, and other policymakers. In this tutorial-based, mostly graph-based presentation with code snippets, a statement of the key issues surrounding prediction, a description of the sample data source, the research question in-progress, and model outputs including graphs and interaction term interpretations will be discussed using Explainable Boosting Machines (EBMs). Advantages of this modeling approach for these contexts will be discussed, with special attention to some use cases within the Sheps context focusing on surgical and other clinical outcomes.

Register

UNC NRP January 2024 Education Session: BERRY: A 6-month randomized controlled trial of the effect of blueberries on mild cognitive decline

Please join the UNC Network for Research Professionals as Carol Cheatham, PhD, returns to share her research on nutrition and the aging brain.

Register

The Cecil G. Sheps Center for Health Services Research is hosting Data Science Week, a focused series of discussions tailored for anyone studying access, disparities, quality, or the cost of health care, and is interested in emerging trends in data science.

The week’s agenda includes in-depth exploration of current themes in healthcare analytics, such as the application of causal machine learning techniques, the utilization of compliant synthetic data in research settings, the intricacies of Medicare claims data analysis, and the development of algorithmic approaches in clinical decision-making.

Sheps welcomes contributions from colleagues in the field, offering an environment conducive to scholarly exchange and professional development. This event is an opportunity for researchers, students, and practicing data scientists to engage with complex topics, refine analytical methodologies, and discuss the implications of advanced data science in the realm of health services research.

Presentations will begin at 12:00pm each day, followed up by a discussion period. Attendees may join in-person at the Sheps Center in room 2002 or via Zoom.

Wednesday, January 17, 12:00 – 1:30pm
Research Journey: Health Services Research Studies in Women’s Health, Disabilities, Surgical Outcomes, and More – Leveraging Large Observational Data
Speaker: Neil Kamdar, MA

Large administrative claims, electronic medical records, and other large clinical and surgical registries possess a breadth of information about our patient population. While these data sources may not offer the depth that may be afforded through studies involving primary data collection, they represent an important and fundamental strength of large sample sizes and the ability to study vulnerable populations and/or rare diseases. I will go through studies involving several data sources and populations broken into thematic units: vulnerable populations (e.g. disabled and those with diseases of aging), women’s health, and surgical outcomes and quality. This talk is especially geared for those who wish to better understand the breadth of published studies that can be tackled using these data, the key findings, and illustrate some other new areas I have explored with these registries over the last several years. I will finally touch upon some of the new avenues for exploration using these data and the potential synergies between Sheps analytic data assets and resources with our community of researchers.

Thursday, January 18, 12:00 – 1:30pm
Long COVID Phenotypes Leveraging a Large Primary Care Electronic Medical Record Registry, the American Family Cohort
Speaker: Neil Kamdar, MA

Long COVID or Post-Acute Sequelae following initial SARS-COV-2 (COVID-19) has continued to be a challenge for diagnosing in the primary care setting. Various published studies have incorporated different approaches and methods which have defined some potential opportunities for classification of COVID-19 afflicted patients into relevant sub-groups based on their symptom presentation during the follow-up period. Using a large primary care registry, the American Family Cohort, I will walk through the key existing challenges for classification in the literature, and the incorporation of a latent class analysis approach to examine extant differences in racial composition, sex, and social deprivation index in group membership. Diagnostic phenotypes of potential Long COVID patients and their comparisons to likely influenza-like (ILI) controls will be explored and examined. This talk was presented as a Distinguished Paper at the National American Primary Care Research Group (NAPCRG) in November 2023.

Friday, January 19, 12:00 – 2:00pm
Methods Session: Detecting and Mitigating Bias in Machine Learning
Speaker: Ashley Avis, MS

Statistical models utilize historic and current data to identify patterns and predict them forward. Historic and present-day biases can be perpetuated without careful evaluation and bias mitigation. We will discuss how to prepare and plan a project utilizing responsible data science methods and other techniques from peer reviewed journals and gray literature to support bias evaluation and mitigation. In this presentation, we will explore how to identify patterns of bias, how predictive models incorporate patterns of bias, and methods for resolving bias. We will explore definitions of algorithmic fairness, the conflicts between different fairness definitions, and the importance of diverse perspectives on final fairness evaluation. We will discuss methods for identifying unequal or biased model decisions. Lastly, we will discuss methods for correcting biased models and how to practice data science responsibly.

An Application and Example of Glassbox Explainable Boosting Machines (EBMs) in a Surgical Cohort Registry
Speaker: Neil Kamdar, MA

Predictive models have become important for postoperative occurrences after surgery, such as readmissions, 30-day reoperation, and other complications. Frequently, the application of predictive models has an opaqueness which remains extraordinarily difficult to achieve stakeholder buy-in, which is usually hospital administrators, clinicians, and other policymakers. In this tutorial-based, mostly graph-based presentation with code snippets, a statement of the key issues surrounding prediction, a description of the sample data source, the research question in-progress, and model outputs including graphs and interaction term interpretations will be discussed using Explainable Boosting Machines (EBMs). Advantages of this modeling approach for these contexts will be discussed, with special attention to some use cases within the Sheps context focusing on surgical and other clinical outcomes.

Register

The Cecil G. Sheps Center for Health Services Research is hosting Data Science Week, a focused series of discussions tailored for anyone studying access, disparities, quality, or the cost of health care, and is interested in emerging trends in data science.

The week’s agenda includes in-depth exploration of current themes in healthcare analytics, such as the application of causal machine learning techniques, the utilization of compliant synthetic data in research settings, the intricacies of Medicare claims data analysis, and the development of algorithmic approaches in clinical decision-making.

Sheps welcomes contributions from colleagues in the field, offering an environment conducive to scholarly exchange and professional development. This event is an opportunity for researchers, students, and practicing data scientists to engage with complex topics, refine analytical methodologies, and discuss the implications of advanced data science in the realm of health services research.

Presentations will begin at 12:00pm each day, followed up by a discussion period. Attendees may join in-person at the Sheps Center in room 2002 or via Zoom.

Wednesday, January 17, 12:00 – 1:30pm
Research Journey: Health Services Research Studies in Women’s Health, Disabilities, Surgical Outcomes, and More – Leveraging Large Observational Data
Speaker: Neil Kamdar, MA

Large administrative claims, electronic medical records, and other large clinical and surgical registries possess a breadth of information about our patient population. While these data sources may not offer the depth that may be afforded through studies involving primary data collection, they represent an important and fundamental strength of large sample sizes and the ability to study vulnerable populations and/or rare diseases. I will go through studies involving several data sources and populations broken into thematic units: vulnerable populations (e.g. disabled and those with diseases of aging), women’s health, and surgical outcomes and quality. This talk is especially geared for those who wish to better understand the breadth of published studies that can be tackled using these data, the key findings, and illustrate some other new areas I have explored with these registries over the last several years. I will finally touch upon some of the new avenues for exploration using these data and the potential synergies between Sheps analytic data assets and resources with our community of researchers.

Thursday, January 18, 12:00 – 1:30pm
Long COVID Phenotypes Leveraging a Large Primary Care Electronic Medical Record Registry, the American Family Cohort
Speaker: Neil Kamdar, MA

Long COVID or Post-Acute Sequelae following initial SARS-COV-2 (COVID-19) has continued to be a challenge for diagnosing in the primary care setting. Various published studies have incorporated different approaches and methods which have defined some potential opportunities for classification of COVID-19 afflicted patients into relevant sub-groups based on their symptom presentation during the follow-up period. Using a large primary care registry, the American Family Cohort, I will walk through the key existing challenges for classification in the literature, and the incorporation of a latent class analysis approach to examine extant differences in racial composition, sex, and social deprivation index in group membership. Diagnostic phenotypes of potential Long COVID patients and their comparisons to likely influenza-like (ILI) controls will be explored and examined. This talk was presented as a Distinguished Paper at the National American Primary Care Research Group (NAPCRG) in November 2023.

Friday, January 19, 12:00 – 2:00pm
Methods Session: Detecting and Mitigating Bias in Machine Learning
Speaker: Ashley Avis, MS

Statistical models utilize historic and current data to identify patterns and predict them forward. Historic and present-day biases can be perpetuated without careful evaluation and bias mitigation. We will discuss how to prepare and plan a project utilizing responsible data science methods and other techniques from peer reviewed journals and gray literature to support bias evaluation and mitigation. In this presentation, we will explore how to identify patterns of bias, how predictive models incorporate patterns of bias, and methods for resolving bias. We will explore definitions of algorithmic fairness, the conflicts between different fairness definitions, and the importance of diverse perspectives on final fairness evaluation. We will discuss methods for identifying unequal or biased model decisions. Lastly, we will discuss methods for correcting biased models and how to practice data science responsibly.

An Application and Example of Glassbox Explainable Boosting Machines (EBMs) in a Surgical Cohort Registry
Speaker: Neil Kamdar, MA

Predictive models have become important for postoperative occurrences after surgery, such as readmissions, 30-day reoperation, and other complications. Frequently, the application of predictive models has an opaqueness which remains extraordinarily difficult to achieve stakeholder buy-in, which is usually hospital administrators, clinicians, and other policymakers. In this tutorial-based, mostly graph-based presentation with code snippets, a statement of the key issues surrounding prediction, a description of the sample data source, the research question in-progress, and model outputs including graphs and interaction term interpretations will be discussed using Explainable Boosting Machines (EBMs). Advantages of this modeling approach for these contexts will be discussed, with special attention to some use cases within the Sheps context focusing on surgical and other clinical outcomes.

Register

Why Are Cardiovascular Imaging Trials So Hard?

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Pamela S. Douglas, MD, MACC, FASE, FAHA
Ursula Geller Professor of Research in Cardiovascular Diseases
Duke Clinical Research Institute – Duke University
Past President, American College of Cardiology
Past President, American Society of Echocardiography

Learn More

Introduction to ATLAS.ti Part 1

This 2-hour course will be offered online only. Attendance is required as the course will not be recorded.

This short course will demonstrate the capabilities of the PC version of ATLAS.ti (version 23), a qualitative analysis software program for coding and interpreting qualitative data. ATLAS.ti also provides numerous options for attaching memos and comments to text segments, documents, and codes. We will demonstrate how to import textual data, create and apply codes, write memos, create diagrams, and examine the hierarchical and relational connections among codes. We will also discuss special analysis features, such as the code co-occurrence matrix, the codes-variables table, and Sankey diagrams.

Register

This two-day (1/23/24 and 1/25/24) course will be offered via Zoom only. Attendance is required as the course will not be recorded.

Usability testing in survey research allows in-depth evaluation of how respondents and interviewers interact with questionnaires, particularly web and mobile surveys. A respondent may understand the survey question and response options, but may be unable to select their answer accurately on the small screen of a smartphone. Although there is a growing body of literature on best practices for web surveys and mobile devices, not all design guidelines work equally well for all surveys and all survey populations. In addition, it is clear that the capabilities of computerized surveys are constantly emerging. Examples are the use of images, videos, maps and GPS, interactive features, and mobile devices. As a result, it is critical for researchers to have the necessary tools to evaluate, test, and modify surveys to incorporate user-centered design in an iterative method as part of the survey pretesting process.

Register

This 5-hour (1/23/24 and 1/25/24; 1pm – 3:30pm each day) course will be offered online only. Attendance is required – this course will not be recorded.

The course introduces students to Stata and data management. It is tailored for beginners and researchers who want to learn how to manage data more effectively. Each day, the class will demonstrate how to use the commands, followed by hands-on exercises using sample datasets.

Register

This one-day course will be offered via Zoom only. There will be a 1-hour lunch and (2) 10-minute breaks (one in the morning and one in afternoon). Attendance is required as it will not be recorded.

This 6-hour course provides a general introduction to qualitative data collection, with an emphasis on applications within public health. We will examine the principles behind crafting cogent and engaging research questions, as well as selecting appropriate sampling strategies and sample sizes for qualitative inquiry. The workshop then delves into the two most commonly employed qualitative data collection methods – in-depth interviews and focus groups. Participants will gain first-hand experience designing instruments and conducting qualitative interviews. Best practices associated with qualitative data management, including transcription and translation, will comprise the final portion of the workshop. Course structure includes lecture, demonstrations, and hands-on exercises. Examples will include both domestic and international research contexts.

Register

Join the School of Data Science and Society for a seminar featuring Michael R. Kosorok, PhD, W.R. Kenan, Jr. Distinguished Professor of Biostatistics and professor of statistics and operations research. This seminar will be held in-person at ITS Manning in room 2400.

In this talk, Kosorok will discuss at a high level important recent developments in artificial intelligence, including large language models and other artificial intelligence tools such as off-policy reinforcement learning, and their strengths and limitations, with an emphasis on applications in causally valid precision health research. This will include some perspectives on the dangers of overhype and unrealistic expectations, a discussion of present and future dangers, and suggestions on how to move forward.

Learn More

This two-day (1/23/24 and 1/25/24) course will be offered via Zoom only. Attendance is required as the course will not be recorded.

Usability testing in survey research allows in-depth evaluation of how respondents and interviewers interact with questionnaires, particularly web and mobile surveys. A respondent may understand the survey question and response options, but may be unable to select their answer accurately on the small screen of a smartphone. Although there is a growing body of literature on best practices for web surveys and mobile devices, not all design guidelines work equally well for all surveys and all survey populations. In addition, it is clear that the capabilities of computerized surveys are constantly emerging. Examples are the use of images, videos, maps and GPS, interactive features, and mobile devices. As a result, it is critical for researchers to have the necessary tools to evaluate, test, and modify surveys to incorporate user-centered design in an iterative method as part of the survey pretesting process.

Register

This 5-hour (1/23/24 and 1/25/24; 1pm – 3:30pm each day) course will be offered online only. Attendance is required – this course will not be recorded.

The course introduces students to Stata and data management. It is tailored for beginners and researchers who want to learn how to manage data more effectively. Each day, the class will demonstrate how to use the commands, followed by hands-on exercises using sample datasets.

Register

Advancing the Safe, Effective and Equitable Use of AI in Healthcare

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Suresh Balu, MD, MBA
Director, Duke Institute for Health Innovation (DIHI)
Associate Dean, Innovation and Partnership
Duke School of Medicine

Mark Sendak, MD, MPP
Population Health & Data Science lead
Duke Institute for Health Innovation (DIHI)

Learn More

Integrated Mixed Methods: Bridging Qualitative and Quantitative Methods and Results

This is a one-day course (9 a.m. - 3 p.m. ET). A break for lunch will happen from 12 p.m. - 1 p.m. Attendance is required as this class will not be recorded.

Mixed methods research (MMR) refers to research design and implementation that combines qualitative and quantitative data collection and/or analysis strategies. This seminar discusses best practices, through the review of exemplars and discussion of theoretical approaches, in “mixing” methods and integrating qualitative and quantitative research. This short couse will focus on strategies, tips, and best practices to accomplish effective integration. A particular focus will be on decision-making related to study design and execution, including writing up methods and results of MMR.

Register

Optimizing Data Collection and Creating Maps with Drones

This course is being offered in collaboration between the Odum Institute and the Center for Urban & Regional Studies. Attendance is required - this course will not be recorded.

This one-day short course, led by the Carolina Drone Lab, will cover drone data collection, planning, and analysis. Small unoccupied aircraft systems (sUAS or drones) are a common mapping and 3D-modeling tool in many organizations. To fully leverage the technology and its benefits, there needs to be a foundation in understanding how to collect quality images and process those images into usable information. The course discusses the best flight parameters for different environments along with advanced data analysis in a GIS environment. Attendees will be introduced to: mission planning, creating automated flights for data capture, processing software for drone imagery (Pix4dMapper), and working with drone imagery in GIS. Commonly used tools and resources will be shared.

Students will learn the technical capabilities and limitations of the drones available for use, and why selection of the right drone and sensor combination is important to obtaining the right data for a project. This course offers participants the chance to learn about a broad spectrum of techniques to take the next steps on their own.

Register

Introduction to Implementation Science

This one-day course will be offered online only. Attendance is required – this course will not be recorded.

There is a substantial gap between the development of innovations in medicine, public health, education and other fields and their delivery in hospitals, communities and schools. Implementation science is an emerging field that is dedicated to the study of closing this gap by scientifically identifying the factors that facilitate and impede the systematic uptake of knowledge and evidence. It includes the study of how individual, organizational and environmental behavior impact implementation effectiveness, and how to develop and test strategies to change these behaviors. This course will provide an overview of the core theories and methods in implementation research and practice. Students will have opportunities to apply these principles through a case study.

Register

The Indiana CCCR—An Untapped Resource for Musculoskeletal Researchers

Join the UNC School of Medicine Thurston Arthritis Research Center for a UNC Core Center for Clinical Research (CCCR) Speaker Series seminar featuring Sharon M. Moe, MD. Moe is the Associate Dean for Clinical and Translational Research; Co-Director, Indiana Clinical Translational Sciences Institute; and the Stuart A. Kleit Professor of Medicine at Indiana University School of Medicine.

In this talk, Moe will review the ongoing research in the Indiana University CCCR and opportunities for collaboration and using data to improve your MSK assessments, link phenotype with clinical measures and outcomes, and conduct hypothesis generating epidemiology or genetic studies.

Register