Apologies for cross-listing.
Short course: Joint Modeling of Longitudinal and Survival Data
Instructors: Dimitris Rizopoulos, Professor of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands
Sponsor: ASA Lifetime Data Science Section
Date and Time (This course will be taught via Zoom):
Thursday, March 24, 11:00 a.m. – 2:00 p.m. ET
Registration Deadline: Wednesday, March 23, at 12:00 p.m. ET
Course Description: In follow-up studies, different types of outcomes are typically collected for each subject. These include longitudinally measured responses (e.g., biomarkers) and the time until an event of interest occurs (e.g., death, dropout). These outcomes are often separately analyzed, but on many occasions, it is of scientific interest to study their association. This research question has given rise to the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First when the focus is on a survival outcome, and we wish to account for the effect of endogenous time-dependent covariates measured with error, and second when the focus is on the longitudinal outcome, and we wish to correct for non-random dropout. This short course will introduce when these models should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data.
Time permitting, the following topics will be covered:
- Review of Relative Risk and Mixed Models: Definitions, Cox and linear mixed models, how to fit them in R
- The Basic Joint Model: Definition of joint models, assumptions, estimation, comparison with time-dependent Cox model, connection with missing data
- Extensions of the Basic Joint Model: Functional form, Multiple longitudinal outcomes, multiple failure times
- Special topics: Dynamic predictions for the survival and longitudinal outcomes
This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood and regression models. Also, a basic knowledge of R would be beneficial but is not required.
Registration:
www.amstat.org/education/web-based-lectures#JMLSD
Fees:
LiDS Section Members: $20
Regular ASA Members: $30
Students ASA Member: $25
Nonmembers $45
Each registration is allowed one web connection. Sound is received via audio streaming from your computer's speakers.
Access Information
After registering, an automated email confirmation will be sent to you with instructions on how to access the webinar.
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Esra Kurum
Assistant Professor
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