Biostatistics short course: Joint Modelling of Longitudinal and Survival Data

Led by Michael J. Crowther, PhD, Karolinska Institute, Stockholm, this short course will provide an introduction to joint modelling through real applications. Participants will learn about the methodological framework, underlying assumptions, estimation, model building and predictions, as well as current developments in the field. Registration required.

Michael J. Crowther, PhD
Biostatistician, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm
Consultant Stata Software Developer, StataCorp LLC

Abstract
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 recognized 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 account for non-random dropout in the analysis of a longitudinal outcome (e.g., a biomarker such as blood pressure). This short course will provide an introduction to joint modelling through real applications. 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 incorporating illustrative Stata code making use of the merlin package in Stata, written by the course lecturer.

Target Audience
This course is aimed as an introduction to the field of joint modelling; however, some experience with separate survival and longitudinal analysis would be beneficial. Although the course will focus on applying the methods in Stata, most example scripts will also be provided in R for attendees to take away.

Recommended Pre-Course Readings
Crowther MJ. merlin – a unified framework for data analysis and methods development in Stata. Stata Journal 2020;20(4):763-784.

Gould AL, Boye ME, Crowther MJ, Ibrahim JG, Quartey G, Micallef S, Bois FY. Joint modelling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modelling working group. Statistics in Medicine 2015;34(14):2181-2195.

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