TEACHER: Michael Crowther, University of Leicester COURSE ID: D-EB14 LANGUAGE:

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 course will be held in Bologna on the 16th of November 2018.

 

Training Course follows the XV Italian Stata Users Group Meeting  and it is possible choice between the following options:

Meeting only:
Students / PhD Students*: € 62.00
Others: € 95.00

Training Course only:
Students / PhD Students* € 227.00
Others: € 350.00

Meeting and Training Course:
Students / PhD Students* € 244.00
Others: € 375.00

**To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year.

Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number.

Conference fees include coffee breaks, lunch, course materials and for participants attending a training course, a temporary licence of Stata.

The number of participants is limited to 15. Places, will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled.

To maximize the usefulness of this course, we strongly recommend that participants bring their own laptops with them, to enable them to actively participate in the empirical sessions.

 

REGISTRATION DEADLINE

Individuals interested in attending the training course, must return their completed registration forms to TStat by the 5th of November 2018.


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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 megenreg packages in Stata, written by the course lecturer.