Joint Modelling of Longitudinal and Survival Data

The joint modelling of longitudinal and survival data has been an area of growing interest in recent years, with the benefits of the approach becoming recognised in ever widening fields of study. The models can provide both an effective way of conducting an analysis of a survival endpoint (e.g. time to death), influenced by a time-varying covariate measured with error, or alternatively correct for non-random dropout in the analysis of a longitudinal outcome (e.g. a biomarker such as blood pressure). This week-long course will provide an introduction to joint modelling through real applications to both clinical trial data and electronic health records, using examples in cancer, liver cirrhosis and cardiovascular disease. We will study the methodological framework, underlying assumptions, estimation, model building and predictions. We will also consider current developments in the field, looking at some of the many extensions of the standard framework, such as the ability to model multiple biomarkers and competing risks. The course will consist of lectures, classroom exercises, and computing exercises making use of the stjm and megenreg packages in Stata, written by the course lecturer.

Course leader

Michael Crowther (University of Leicester and Karolinska Institutet)

Target group

Physicians, clinicians and public health professionals from public and private institutions who are looking for systematic training in the principles of epidemiology and biostatistics, or epidemiology applied to health care planning and evaluation. They will acquire familiarity with epidemiological and biostatistical principles and techniques and with the computational tools needed to solve practical problems.


Students in biostatistics and epidemiology, and researchers both from public and private institutions who wish to increase their familiarity with quantitative methods or to deepen their knowledge of a specific area of interest, so they can more effectively address problems in health research. They will gain knowledge in modern, advanced methods useful for health professionals engaged in clinical practice, research and teaching.

Course aim

This week-long course will provide an introduction to joint modelling through real applications to both clinical trial data and electronic health records, using examples in cancer, liver cirrhosis and cardiovascular disease. We will study the methodological framework, underlying assumptions, estimation, model building and predictions.

Credits info

It is possible to gain credits for the Summer School’s courses, but the decision is always made by the student’s own institution.

Fee info

EUR 1450: For University students: Registration before March 24th, 2019 €1,250. After March 24th, 2019 €1,450.
EUR 1650: General: Registration before March 24th, 2019 €1,450. After March 24th, 2019 €1,650.