Dynamic prediction of survival using landmarking in large healthcare databases, with an application in cystic fibrosis

In ‘dynamic’ prediction of survival we make updated predictions of individuals’ survival over time as new information becomes available about their health status via longitudinal measurements. Landmarking is an attractive and flexible method for dynamic prediction. I will give an introduction to landmarking and a practical overview of how to use this approach, including some recent developments.

Large observational patient databases, which provide longitudinal data on clinical measurements, present opportunities to develop ‘personalised’ dynamic predictions of survival. I will present an example application of landmarking for dynamic prediction of survival in people with cystic fibrosis, using data from the US Cystic Fibrosis Foundation Patient Registry. This will include discussion of some of the challenges faced in making dynamic predictions using routinely collected data and how they can be addressed in the landmarking framework. I will also show some comparisons between landmarking and the alternative approach of joint modelling, and hopefully convince you that landmarking has a number of advantages.