ASA Connect

 

Abstract: This workshop focuses on data collected in follow-up studies. Outcomes from these studies typically include longitudinally measured responses (e.g., biomarkers) and the time until an event of interest occurs (e.g., death, dropout). The aim often is to utilize the longitudinal information to predict the risk of the event. An important attribute of these predictions is that they have a time-dynamic nature, i.e., they are updated each time new longitudinal measurements are recorded. In this workshop, we will introduce the framework of joint models for longitudinal and time-to-event data and explain how it can be used to estimate and evaluate such dynamic risk predictions. We will use the R package JMbayes2 to showcase the capabilities of these models.

Prerequisites: This workshop assumes knowledge of basic statistical concepts, such as regression models and standard statistical inference using maximum likelihood and Bayesian methods. Also, a basic knowledge of R would be beneficial but is not required. Participants need a laptop for this workshop. Before the course, instructions will be sent for installing the required software.

Presenter Background: Dimitris Rizopoulos is a Professor of Biostatistics at the Erasmus University Medical Center. He received an M.Sc. in statistics (2003) from the Athens University of Economics and Business and a Ph.D. in Biostatistics (2008) from the Katholieke Universiteit Leuven. Dr. Rizopoulos wrote his dissertation and several methodological and applied articles on various aspects of models for survival and longitudinal data analysis. He is the author of a book on joint models for longitudinal and time-to-event data. He has also written three freely available packages to fit such models in R under maximum likelihood (i.e., package JM) and the Bayesian approach (i.e., packages JMbayes and JMbayes2). He currently serves as Co-Editor for the journal Biostatistics.

Schedule (tentative):

9:00am – 9:55am          Session I: Introduction and presentation of the joint modeling framework

10:00am – 10:55am       Session II: Dynamic risk predictions from joint models

11:00am – 12:00pm      Session III: Practice session using the R package JMbayes2

Registration:

• Fee: $25
• Deadline: March 7, 2023
• Please send registration fee via PayPal to asariskanalysis@gmail.com. Important: please provide an email address in the comment for the purpose to receive a Zoom link to the webinar.
• Contact Dr. Yue Jiang at yue.jiang@duke.edu for questions related to the registration process or to receive confirmation.
• Zoom link for registered participants will be sent by March 8^th, 2023 to the email noted in the registration.
• Registered participants unable to join the alive webinar will receive a link to the recorded workshop.

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Yichuan Zhao
Georgia State University
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