11-12 April 2013 in Sydney
About the Presenter
Dimitris Rizopoulos is Assistant Professor at the Department of Biostatistics of the Erasmus University Medical Center in the Netherlands. 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, as well as a number of methodological articles, on various aspects of models for longitudinal data analysis and survival analysis. He is the author of a recent book on the topic of joint models for longitudinal a
nd time-to-event data. 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.
Abstract
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 modeling approach is either required or may produce a 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.
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 on real data sets and the course will also feature short software practical sessions illustrating how these models can be fitted in R using package JM.
Course Outline
Day I
· Introduction & motivation: Which type of research questions require joint modeling
· A brief review of relative risk models
· A brief review of mixed effects models
· The basic joint model and its properties
· Software practical: Fitting simple joint models with package JM
Day II
· Extensions of the basic joint model
· Using joint models to derive dynamic individualized predictions
· Measuring accuracy of longitudinal outcomes with joint models
· Software practical: Extended joint models & dynamic predictions with package JM
Course Timetable (including morning tea, lunchtime and afternoon tea breaks):
Both days
09h00 – 10h30: Theory session
10h30: Coffee break
10h45 – 12h00: Theory session
12h00 – 13h30: Lunch
13h30 – 15h00: Theory session
15h00: Coffee break
15h15 – 17h00: Software practical
Target Audience
This course is aimed at applied researchers, such as statisticians, biostatisticians and epidemiologists, with interest in the analysis of longitudinal and survival data. 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 and Cox proportional hazards models would be beneficial but is not required.
Learning Objectives
After this course participants will be able to identify settings in which a joint modeling approach is required. In addition, from the course it will become clear which joint models can be used depending on the actual research questions to be answered, and which model-building strategies are currently available. Further, participants should be able to construct and fit an appropriate joint model, correctly interpret the obtained results, and extract additional useful information (e.g. plots) that can help communicate the results in an efficient manner.
Would you please note that workshop participants are required to bring their own laptop. The software needed for the workshop is the latest version of R and the latest version of package JM. R can be downloaded from http://cran.r-project.org/ -- after installing R, the following command should be executed prior coming to the course: install.packages (“JM”).
Venue
Macquarie Park Executive Conference Centre
Macquarie Graduate School of Management
Talavera Road
North Ryde
Sydney
Cost
Payment before 28 March 2013, 2pm AEST (Early Bird):
SSAI Members $550.00
Non-members of SSAI $600.00
Full-time Students $300.00
Payment from 28 March 2013, 2:01pm AEST until 4 April 2013:
SSAI Members $700.00
Non-members of SSAI $750.00
Full-time Students $300.00
Registrations close on 4 April 2013.
Online registration is available here for SSAI member, non-members and student member registrations, and here for non-member student registrations. Students are required to post, email or fax proof of their full-time student status before their registration becomes valid. The address for SSAI is PO Box 213, Belconnen, ACT 2616, email: eo@statsoc.org.au and fax no 02 6251 0204. Members of SSAI need to be logged in with their username to be able to take advantage of the member discount.
A flyer for this event can be downloaded here.
Cancellation Policy
Cancellations received prior to Thursday, 4 April 2013 will be refunded in full. Cancellations need to be accompanied by a valid credit card number and expiry date which will be used to put the refund through. After this time no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to eo@statsoc.org.au