Join NACC and the Data Core Steering Committee at 4pm ET on Wednesday, March 24th for an exciting webinar featuring presenters from Northwestern University's Feinberg School of Medicine:

Hui Zhang, PhD, Professor of Preventative Medicine

Nathan Gill, Statistical Analyst

In this webinar, our featured presenters will introduce over-dispersion, which is a major challenge to model count using the most popular Poisson model. They will review the statistical methods to detect over-dispersion in cross-sectional data, propose an innovative method to detect over-dispersion in longitudinal data, even with data missing. In addition, they will show how to use an online convenient shiny app (which they've developed) to perform the discussed analysis.

How to Attend:

In order to receive the Zoom link for this free event, you will need to register via Eventbrite. You will receive automatic email reminders about the event and the option to add the event to your calendar.

Please feel free to share this Eventbrite link widely with your Center and your network.

About NACC

The National Alzheimer's Coordinating Center was established in 1999 by the National Institute on Aging/NIH to facilitate collaborative, longitudinal research. Using data collected from NIA-funded Alzheimer's Disease Research Centers (ADRCs) across the United States, NACC has developed and maintains a large relational database of standardized clinical and neuropathological research data.

There are approximately 30 NIA-funded ADRCs across the United States. The Centers are organized in "cores" (Administrative; Clinical; Data Management & Statistics; Neuropathology; Outreach, Recruitment and Education; and Biomarker) and "components" (Research Education Component). These "cores" regularly host webinars, the likes of what you'll see on our channel!

For more information on upcoming NACC webinars and past webinars, please visit https://naccdata.org/adrc-resources/adrc-webinars.

To learn more about NACC, please visit https://naccdata.org/.

Detection of over-dispersion in count data with an online shiny app