DOCENTE: Michael Crowther, University of Leicester CODICE CORSO: D-EB14 LINGUA:

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 one-day course will provide an introduction to joint modelling through real applications to both clinical trial data and electronic health records, using examples in cancer and liver cirrhosis. 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 and computing exercises making use of the stjm and megenreg packages in Stata, written by the course lecturer.

This one day workshop is of particular interest to biostatisticians, epidemiologists, applied statisticians and researchers or professionals working in  economics, the social sciences or public health wishing to carry out survival analysis on longitudinal/panel data in their applied research.

Participants should be familiar with Stata. Working knowledge of survival analysis and an introductory knowledge of panel data is required.

Introductions

 

Lecture 1: Survival analysis, longitudinal analysis and their combination

Practical 1

 

Lecture 2: Joint modelling of longitudinal and survival data

Practical 2

 

Lecture 3: Extended association structures and predictions

Practical 3

 

Lecture 4: Further topics in joint modelling

 

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 one-day course will provide an introduction to joint modelling through real applications to both clinical trial data and electronic health records, using examples in cancer and liver cirrhosis. During the course of the day, participants will study the methodological framework, underlying assumptions, estimation, model building and predictions. Consideration will also be given to 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 and computing exercises making use of the stjm and merlin packages in Stata, written by the course lecturer.