19-21 October 2015 (Stellenbosch) Introduction to the Joint Modelling of Longitudinal and Survival Data, with Applications in R

Posted on Mon, May 11 2015 10:55:00

Dr. Dimitris Rizopoulos (Department of Biostatistics, Erasmus University
Medical Center, the Netherlands) presented this intensive three-day course
at Stellenbosch under the auspices of the South African DST/NRF Centre
for Epidemiological Modelling and Analysis (SACEMA). The course took
place from 19-21 October 2015 at the Stellenbosch Institute for Advanced Study.

Joint Modelling Group Photo

Course summary: 

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). Often these outcomes are separately
analysed, but in many occasions it is of scientific interest to study
their association. This type of research question has given rise in 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 focus is on
a survival outcome and we wish to account for the effect of endogenous
time-dependents covariates measured with error, and second, when focus
is on the longitudinal outcome and we wish to correct for non-random
dropout. This course is aimed at applied researchers and graduate
students, and will provide a comprehensive introduction into this
modelling framework. We will explain 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. Emphasis is
given on applications, and after the end of the course participants will
be able to define appropriate joint models to answer their questions of
interest. In terms of software the R packages JM and JMbayes will be used.

This course assumes knowledge of basic statistical concepts, such as
standard statistical inference using maximum likelihood, and regression
models. In addition, basic knowledge of R would be beneficial but is not
required.

Dimitris Rizopoulos is an Associate Professor in Biostatistics at the
Erasmus University Medical Center. He received a 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, as well as a number of methodological
and applied articles on various aspects on models for survival and
longitudinal data analysis, and he is the author of a recent book on the
topic of joint models for longitudinal and time-to-event data. He has
also written two freely available packages to fit this type of models in
R under maximum likelihood (i.e., package JM) and the Bayesian approach
using JAGS, WinBUGS or OpenBUGS (i.e., package JMbayes). He currently
serves as an Associate Editor for Biometrics and Biostatistics, and he
has been a guest editor of a special issue on joint models in
Statistical Methods in Medical Research.


Webpages:
http://www.erasmusmc.nl/biostatistiek/People/Faculty/drizopoulos/
http://eur.academia.edu/DimitrisRizopoulos

Book websites:
http://jmr.r-forge.r-project.org/
http://www.crcpress.com/product/isbn/9781439872864

Packages:
http://cran.r-project.org/package=JMbayes
http://cran.r-project.org/package=JM