27th International Workshop on Statistical Modelling
IWSM'27 • July 16–20, 2012 • Prague, Czech Republic
|Welcome||Venue||Committees||Programme||Short  course||Important  dates||Information  for  authors|
|Final paper submission||Registration||Student  awards||Travel  grants||Social  events||Accommodation||Transport|| |
SHORT COURSE, SUNDAY JULY 15
An Introduction to Joint Models for Longitudinal and Survival Data with Applications in R
|Department of Biostatistics|
|Erasmus University Medical Center Rotterdam, the Netherlands|
In follow-up studies often different types of outcomes are collected for each subject. These may include several longitudinally measured responses (e.g., biomarkers or other clinical parameters), and the time at which an event of particular interest occurs (e.g., death, disease progression or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modelling approach is either required or may produce better insight into the mechanisms that underlie the phenomenon under study. To this end a new class of models has been developed known as joint models for longitudinal and time-to-event data.
Aims of the course
The aim of this course is to introduce this joint modeling framework, and in particular focus on when these models should be used, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data. The course will be explanatory rather than mathematically rigorous, but sufficient technical background will be provided to understand the properties of these models. All concepts will be illustrated in real data sets. The final part of the course will include a short software practical illustrating how these models can be fitted in R using package JM. By the end of the course, participants will be able to define a joint model suitable for their own data and scientific questions, to fit it in R, and correctly interpret the results.
The course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood, and regression models. In addition, basic knowledge of mixed effects models and survival analysis would be beneficial (but not required).