Joint Models for Longitudinal and Survival Data [ESP72]

Course highlights

EC points

0.7

Start date

13-08-2018

End date

17-08-2018

Course days

Monday to Friday (5 mornings)

Faculty

Prof. Dimitris Rizopoulos, PhD

Course fee

€ 470

Location

Erasmus MC, Rotterdam NL

Level

Intermediate

Prerequisites

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.

Disciplines

  • Biostatistics

Application

Go to the ESP website

Detailed information about this course:

Description

Faculty: Dimitris Rizopoulos, PhD


Longitudinal and time-to-event outcomes are the main types of outcomes encountered in medical studies. Primary examples of the former are biomarkers or other patient parameters that are measured during follow-up, whereas for the latter examples include the time to relapse of the disease, time to re-operation or time to death. This course introduces a new type of statistical models that can be used to investigate the association structure between longitudinal and survival outcomes.


In terms of software, we will use R and illustrate how these models can be fitted using package JM and JMbayes.

Participants will be expected to bring their own laptop computers to the session, and to have recent versions of R

(http://www.r-project.org/) and of R packages JM

(http://cran.r-project.org/package=JM) and JMbayes

(http://cran.r-project.org/package=JMbayes) already installed on these computers. All necessary computer code will be provided beforehand.

Objectives

  • Explain when these models should be used in practice and how they can be utilized to extract relevant information from the data.
  • Introduce the concept of dynamic predictions that has direct applications in personalized medicine.

Participant profile

Professional statisticians, epidemiologists and public health experts, working in applied environments where hierarchical modelling and survival analysis are key issues.

Assessment

Attendance


Reduction on fees

PLEASE NOTE THIS DOES NOT APPLY TO THE RESEARCH MASTERS (120 ECTS)

50% reduction on our fees for participating institutes of NIHES

This 50% reduction is offered to participants formally appointed at the departments or sections participating in NIHES.

University Medical Center Rotterdam/Erasmus University Rotterdam:

  • Department of Epidemiology
  • Department of Public Health
  • Department of Psychiatry, Section of Medical Psychology and Psychotherapy
  • Department of Child and Adolescent Psychiatry/Psychology
  • Department of Medical Informatics
  • Department of General Practice
  • Erasmus School of Health Policy & Management
  • Generation R

50% reduction on our fees for NIHES affiliated partners

This 50% reduction is offered to participants formally appointed at the following NIHES affiliated partners:

University Medical Center Rotterdam/Erasmus University Rotterdam, departments of:

  • Dermatology
  • General Paediatrics
  • Plastic and Reconstructive Surgery
  • Rehabilitation Medicine

Institute of Medical Education Research Rotterdam (iMERR)

Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital

  • Department of Cancer Epidemiology

25% reduction on our fees for partner institutes of NIHES

For participants formally appointed at the following departments, institutes:

  • Departments of Erasmus MC (except the departments mentioned above)
  • Addiction Research Institute (IVO)
  • Integraal Kankercentrum Zuid (IKZ)
  • Pallas Health Research and Consultancy
  • Municipal Health Service Amsterdam (GGD Amsterdam)
  • Municipal Health Service Rotterdam (GGD Rotterdam)
  • Havenpolikliniek
  • Admiraal De Ruyter Ziekenhuis

25% reduction for all (international) PhD students
Please state your tutor’s name and email address on the application form (remarks field).

25% reduction on our course fees (maximum 3 EC points per academic year) for all NIHES alumni
Please state that you are a NIHES alumnus/alumna and the academic year you graduated (remarks field).