This 2-day course (10/30 & 10/31) will be offered ONLINE. It will not be recorded as there are in-class activities.

With increased interest in person-centered interventions and treatments has come increased interest in understanding human processes as they unfold within individuals. Additionally, technological advances have made the collection of person-specific data easier and more cost-effective for researchers interested in studying human behavior within everyday contexts. This two-day course focuses on using two popular network models to explore research questions concerning within-person processes.

This course is intended for individuals with research questions that can be answered using multivariate time series data/intensive longitudinal data. Examples of such data include daily diary data; data collected via self-report through ecological momentary sampling (ESM); passive data from cell phones; and other psychophysiological data such as MRI data or heart rate data.

The two network modeling frameworks presented in this course are graphicalVAR (GVAR) and Group Iterative Multiple Model Estimation (GIMME). Both models can be used to explore processes as they unfold within individuals to obtain individual person-specific network models (idiographic analysis) or group/population level network models (nomothetic analysis). Differences between the modeling frameworks will be presented. Challenges and considerations for choosing between methods will be discussed.

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Clinical Protocol Development Series

The goal of this two-day series is to provide researchers with the knowledge, tools, and resources to aid in the development of a scientific protocol for a clinical research study. Both sessions will begin at 9 a.m. and a question-and-answer session will follow each presentation.

The first day of the series will provide an introduction and focus on the following key points:
- Who needs a clinical protocol and why it is important
- UNC Scientific Review Committee processes
- Types of clinical protocols, and content expectations for sections of the protocol
- Resources and tools available at UNC to support clinical protocol development

Target Audience: academic researchers, scientists, study coordinators, and students engaged in clinical research and/or clinical trials.

ACRP Contact Hours Update: The ACRP no longer approves 3rd party requests for CE credit. However, attendees are still welcome to self-report to ACRP for CE credit.

Register

This 2-day course (10/30 & 10/31) will be offered ONLINE. It will not be recorded as there are in-class activities.

With increased interest in person-centered interventions and treatments has come increased interest in understanding human processes as they unfold within individuals. Additionally, technological advances have made the collection of person-specific data easier and more cost-effective for researchers interested in studying human behavior within everyday contexts. This two-day course focuses on using two popular network models to explore research questions concerning within-person processes.

This course is intended for individuals with research questions that can be answered using multivariate time series data/intensive longitudinal data. Examples of such data include daily diary data; data collected via self-report through ecological momentary sampling (ESM); passive data from cell phones; and other psychophysiological data such as MRI data or heart rate data.

The two network modeling frameworks presented in this course are graphicalVAR (GVAR) and Group Iterative Multiple Model Estimation (GIMME). Both models can be used to explore processes as they unfold within individuals to obtain individual person-specific network models (idiographic analysis) or group/population level network models (nomothetic analysis). Differences between the modeling frameworks will be presented. Challenges and considerations for choosing between methods will be discussed.

Register

Clinical Protocol Development Series

The goal of this two-day series is to provide researchers with the knowledge, tools, and resources to aid in the development of a scientific protocol for a clinical research study. Both sessions will begin at 9 a.m. and a question-and-answer session will follow each presentation.

On the second day of the series, we will take a "deeper dive" into clinical study design, statistics, and their impact on clinicaltrials.gov reporting:
- Clinical Protocol Study Design - aims, objectives, endpoints, and outcomes
- Statistical analysis, sample size considerations, data management
- Clinicaltrials.gov - registration and outcome measure reporting
- Protocol problem spots and ways to improve protocols

Target Audience: academic researchers, scientists, study coordinators, and students engaged in clinical research and/or clinical trials.

ACRP Contact Hours: The ACRP no longer approves 3rd party requests for CE credit. However, attendees are still welcome to self-report to ACRP for CE credit.

Register

Statistical modeling for prediction requires a different frame of mind than more typical statistical modeling. In this seminar Jeff Laux, PhD, introduces predictive modeling as distinct from more common analyses and describes cross-validation, the central technique of predictive modeling.

The NC TraCS Biostatistics Seminar Series helps clinical and translational researchers collaborate more effectively with consulting biostatisticians by building deeper understanding of key statistical concepts and methods. Researchers then are better able to (1) evaluate relevance of the concept or method for research aim(s) definition and choice of study design; and (2) properly interpret the results of data analysis.

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Congenital Heart Initiative: Redefining Outcomes and Navigation to Adult Centered Care (CHI-RON) Study

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Thomas W. Carton, PhD, MS
Chief Data and Strategy Officer
Louisiana Public Health Institute

Anitha S. John, MD, PhD
Medical Director
Washington Adult Congenital Heart Program
Professor of Pediatrics
Children’s National Hospital
George Washington University

Learn More